library(readxl)## Warning: package 'readxl' was built under R version 4.4.3library(plm)## Warning: package 'plm' was built under R version 4.4.3library(lmtest)## Warning: package 'lmtest' was built under R version 4.4.3## Loading required package: zoo## Warning: package 'zoo' was built under R version 4.4.3##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericlibrary(sandwich)## Warning: package 'sandwich' was built under R version 4.4.3library(stargazer)##
## Please cite as:## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.3. https://CRAN.R-project.org/package=stargazerdf <- read_excel("Panel data 1.xlsx", skip = 1)names(df) <- c(
"country", "id", "year", "emerging",
"innovation", "people", "planet", "prosperity",
"partnership", "peace",
"inflation", "pop_growth", "urban_growth"
)df$country <- as.factor(df$country)
df$year <- as.numeric(df$year)
df$emerging <- as.numeric(df$emerging)
df$innovation <- as.numeric(df$innovation)## Warning: NAs introduced by coerciondf$people <- as.numeric(df$people)## Warning: NAs introduced by coerciondf$planet <- as.numeric(df$planet)
df$prosperity <- as.numeric(df$prosperity)
df$partnership <- as.numeric(df$partnership)
df$peace <- as.numeric(df$peace)
df$inflation <- as.numeric(df$inflation)
df$pop_growth <- as.numeric(df$pop_growth)
df$urban_growth <- as.numeric(df$urban_growth)
df$id <- NULL#Addressing missing data
install.packages("plm") ## Warning: package 'plm' is in use and will not be installedlibrary(plm)
library(dplyr)## Warning: package 'dplyr' was built under R version 4.4.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plm':
##
## between, lag, lead## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(zoo)
library(plm)
library(lmtest)
library(sandwich)
df <- df %>%
group_by(country) %>%
arrange(year) %>%
mutate(across(where(is.numeric),
~ na.approx(., na.rm = FALSE))) %>%
mutate(across(where(is.numeric),
~ na.locf(., na.rm = FALSE))) %>%
mutate(across(where(is.numeric),
~ na.locf(., fromLast = TRUE))) %>%
ungroup()
colSums(is.na(df))## country year emerging innovation people planet
## 0 0 0 0 0 0
## prosperity partnership peace inflation pop_growth urban_growth
## 0 0 0 0 0 0library(plm)
pdata <- pdata.frame(df, index = c("country", "year"))
pdim(pdata)## Balanced Panel: n = 20, T = 11, N = 220is.pbalanced(pdata)## [1] TRUElibrary(lmtest)
pool_prosperity <- plm(
prosperity ~ innovation + emerging + inflation + pop_growth + urban_growth,
data = pdata,
model = "pooling"
)
fe_prosperity <- plm(
prosperity ~ innovation + inflation + pop_growth + urban_growth,
data = pdata,
model = "within"
)
re_prosperity <- plm(
prosperity ~ innovation + inflation + pop_growth + urban_growth,
data = pdata,
model = "random"
)
summary(pool_prosperity)## Pooling Model
##
## Call:
## plm(formula = prosperity ~ innovation + emerging + inflation +
## pop_growth + urban_growth, data = pdata, model = "pooling")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -11.00476 -1.09187 0.18052 1.36776 9.01890
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## (Intercept) 2.865378 1.184145 2.4198 0.0163649 *
## innovation -0.018003 0.012864 -1.3995 0.1631138
## emerging 0.338770 0.549209 0.6168 0.5380016
## inflation 0.058192 0.022035 2.6409 0.0088790 **
## pop_growth -1.483123 0.429944 -3.4496 0.0006762 ***
## urban_growth 0.758059 0.369263 2.0529 0.0412990 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 2192.4
## Residual Sum of Squares: 1910.5
## R-Squared: 0.12862
## Adj. R-Squared: 0.10826
## F-statistic: 6.3174 on 5 and 214 DF, p-value: 1.7157e-05summary(fe_prosperity)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = prosperity ~ innovation + inflation + pop_growth +
## urban_growth, data = pdata, model = "within")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -11.84744 -0.61685 0.24422 0.93532 9.19719
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## innovation -0.0030262 0.0199332 -0.1518 0.87949
## inflation 0.0391551 0.0298872 1.3101 0.19170
## pop_growth -1.3271898 0.5148673 -2.5777 0.01068 *
## urban_growth 0.2690993 0.4515799 0.5959 0.55193
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 1799.1
## Residual Sum of Squares: 1633.1
## R-Squared: 0.092268
## Adj. R-Squared: -0.014251
## F-statistic: 4.9807 on 4 and 196 DF, p-value: 0.00076151summary(re_prosperity)## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = prosperity ~ innovation + inflation + pop_growth +
## urban_growth, data = pdata, model = "random")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Effects:
## var std.dev share
## idiosyncratic 8.3319 2.8865 0.954
## individual 0.4018 0.6339 0.046
## theta: 0.1917
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -10.62105 -0.89597 0.11962 1.30525 9.23615
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) 3.243741 1.115885 2.9069 0.003651 **
## innovation -0.020404 0.012533 -1.6279 0.103539
## inflation 0.062781 0.021965 2.8582 0.004261 **
## pop_growth -1.438461 0.444733 -3.2344 0.001219 **
## urban_growth 0.688174 0.380116 1.8104 0.070229 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 2056.1
## Residual Sum of Squares: 1827.5
## R-Squared: 0.11118
## Adj. R-Squared: 0.094641
## Chisq: 26.8929 on 4 DF, p-value: 2.0895e-05phtest(fe_prosperity, re_prosperity)##
## Hausman Test
##
## data: prosperity ~ innovation + inflation + pop_growth + urban_growth
## chisq = 8.9501, df = 4, p-value = 0.06236
## alternative hypothesis: one model is inconsistentcoeftest(fe_prosperity, vcov = vcovHC(fe_prosperity, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation -0.0030262 0.0112007 -0.2702 0.7873
## inflation 0.0391551 0.0317812 1.2320 0.2194
## pop_growth -1.3271898 0.1803145 -7.3604 4.931e-12 ***
## urban_growth 0.2690993 0.2397956 1.1222 0.2631
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1developing <- subset(pdata, emerging == 1)
developed <- subset(pdata, emerging == 0)
fe_dev <- plm(
prosperity ~ innovation + inflation + pop_growth + urban_growth,
data = developing,
model = "within"
)
coeftest(fe_dev, vcov = vcovHC(fe_dev, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation -0.004334 0.011129 -0.3894 0.6978
## inflation 0.032225 0.030222 1.0663 0.2890
## pop_growth -1.350754 0.159333 -8.4776 2.745e-13 ***
## urban_growth 0.215746 0.237646 0.9078 0.3662
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1fe_adv <- plm(
prosperity ~ innovation + inflation + pop_growth + urban_growth,
data = developed,
model = "within"
)
coeftest(fe_adv, vcov = vcovHC(fe_adv, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation -0.017616 0.048416 -0.3639 0.71676
## inflation 0.183542 0.144525 1.2700 0.20717
## pop_growth -1.055745 0.518871 -2.0347 0.04464 *
## urban_growth 0.369052 0.428977 0.8603 0.39176
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# 1) Fixed Effect - Full Sample
fe_people <- plm(
people ~ innovation + inflation + pop_growth + urban_growth,
data = pdata,
model = "within"
)
summary(fe_people)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = people ~ innovation + inflation + pop_growth +
## urban_growth, data = pdata, model = "within")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -12.549251 -1.357753 0.016685 1.281127 16.504849
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## innovation 0.104403 0.030130 3.4651 0.000651 ***
## inflation 0.060890 0.045176 1.3478 0.179264
## pop_growth 1.799063 0.778242 2.3117 0.021834 *
## urban_growth -1.038783 0.682581 -1.5218 0.129659
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 4197.9
## Residual Sum of Squares: 3731.1
## R-Squared: 0.11119
## Adj. R-Squared: 0.0068909
## F-statistic: 6.12989 on 4 and 196 DF, p-value: 0.00011447coeftest(
fe_people,
vcov = vcovHC(fe_people, type = "HC1", cluster = "group")
)##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation 0.104403 0.047138 2.2148 0.02792 *
## inflation 0.060890 0.038917 1.5646 0.11929
## pop_growth 1.799063 0.836237 2.1514 0.03267 *
## urban_growth -1.038783 0.699747 -1.4845 0.13928
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# 2) Random Effect - Full Sample
re_people <- plm(
people ~ innovation + inflation + pop_growth + urban_growth,
data = pdata,
model = "random"
)
summary(re_people)## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = people ~ innovation + inflation + pop_growth +
## urban_growth, data = pdata, model = "random")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Effects:
## var std.dev share
## idiosyncratic 19.036 4.363 0.078
## individual 224.999 15.000 0.922
## theta: 0.9126
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -9.8254 -1.7693 -0.2196 1.2168 19.8364
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) 94.494951 4.184372 22.5828 < 2.2e-16 ***
## innovation 0.114224 0.029977 3.8104 0.0001387 ***
## inflation 0.057395 0.045103 1.2725 0.2031908
## pop_growth 1.794495 0.780490 2.2992 0.0214942 *
## urban_growth -1.024291 0.683904 -1.4977 0.1342082
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 4654.7
## Residual Sum of Squares: 4136.7
## R-Squared: 0.11129
## Adj. R-Squared: 0.094758
## Chisq: 26.9243 on 4 DF, p-value: 2.0592e-05# 3) Hausman Test
phtest(fe_people, re_people)##
## Hausman Test
##
## data: people ~ innovation + inflation + pop_growth + urban_growth
## chisq = 43.525, df = 4, p-value = 8.052e-09
## alternative hypothesis: one model is inconsistent# Developing Countries
fe_dev_people <- plm(
people ~ innovation + inflation + pop_growth + urban_growth,
data = developing,
model = "within"
)
coeftest(
fe_dev_people,
vcov = vcovHC(fe_dev_people, type = "HC1", cluster = "group")
)##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation 0.151260 0.038452 3.9338 0.0001581 ***
## inflation 0.080353 0.047655 1.6861 0.0950146 .
## pop_growth 1.188882 0.267422 4.4457 2.353e-05 ***
## urban_growth -0.154674 0.411145 -0.3762 0.7075965
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Developed Countries
fe_adv_people <- plm(
people ~ innovation + inflation + pop_growth + urban_growth,
data = developed,
model = "within"
)
coeftest(
fe_adv_people,
vcov = vcovHC(fe_adv_people, type = "HC1", cluster = "group")
)##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation -0.24028 0.19910 -1.2068 0.23046
## inflation 0.21589 0.67644 0.3192 0.75030
## pop_growth 9.21804 5.20935 1.7695 0.07998 .
## urban_growth -9.02480 4.10484 -2.1986 0.03031 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# 1) Fixed Effect - Full Sample
fe_planet <- plm(
planet ~ innovation + inflation + pop_growth + urban_growth,
data = pdata,
model = "within"
)
summary(fe_planet)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = planet ~ innovation + inflation + pop_growth +
## urban_growth, data = pdata, model = "within")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -2.049145 -0.370654 0.016869 0.343846 1.389278
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## innovation -0.0144209 0.0044286 -3.2563 0.00133 **
## inflation -0.0055770 0.0066401 -0.8399 0.40199
## pop_growth 0.0964916 0.1143885 0.8435 0.39995
## urban_growth -0.1569759 0.1003278 -1.5646 0.11928
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 85.944
## Residual Sum of Squares: 80.607
## R-Squared: 0.062098
## Adj. R-Squared: -0.047962
## F-statistic: 3.24428 on 4 and 196 DF, p-value: 0.013237coeftest(
fe_planet,
vcov = vcovHC(fe_planet, type = "HC1", cluster = "group")
)##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation -0.0144209 0.0085399 -1.6887 0.09287 .
## inflation -0.0055770 0.0080708 -0.6910 0.49038
## pop_growth 0.0964916 0.0884479 1.0909 0.27664
## urban_growth -0.1569759 0.0642528 -2.4431 0.01545 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# 2) Random Effect - Full Sample
re_planet <- plm(
planet ~ innovation + inflation + pop_growth + urban_growth,
data = pdata,
model = "random"
)
summary(re_planet)## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = planet ~ innovation + inflation + pop_growth +
## urban_growth, data = pdata, model = "random")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Effects:
## var std.dev share
## idiosyncratic 0.4113 0.6413 0.029
## individual 13.8895 3.7269 0.971
## theta: 0.9482
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -1.99817 -0.41227 -0.12958 0.34439 1.77749
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) 8.9176432 0.9502875 9.3842 < 2.2e-16 ***
## innovation -0.0127731 0.0046103 -2.7706 0.005596 **
## inflation -0.0067265 0.0069210 -0.9719 0.331102
## pop_growth 0.1115638 0.1194187 0.9342 0.350188
## urban_growth -0.1661947 0.1047044 -1.5873 0.112450
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 101.15
## Residual Sum of Squares: 96.536
## R-Squared: 0.04561
## Adj. R-Squared: 0.027854
## Chisq: 10.2748 on 4 DF, p-value: 0.036045# 3) Hausman Test
phtest(fe_planet, re_planet)##
## Hausman Test
##
## data: planet ~ innovation + inflation + pop_growth + urban_growth
## chisq = 2.0548, df = 4, p-value = 0.7257
## alternative hypothesis: one model is inconsistent# Developing Countries
fe_dev_planet <- plm(
planet ~ innovation + inflation + pop_growth + urban_growth,
data = developing,
model = "within"
)
coeftest(
fe_dev_planet,
vcov = vcovHC(fe_dev_planet, type = "HC1", cluster = "group")
)##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation -0.00649978 0.00759801 -0.8555 0.3944
## inflation 0.00081167 0.00232508 0.3491 0.7278
## pop_growth 0.12922965 0.02231541 5.7910 8.820e-08 ***
## urban_growth -0.14002755 0.03004837 -4.6601 1.018e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Developed Countries
fe_adv_planet <- plm(
planet ~ innovation + inflation + pop_growth + urban_growth,
data = developed,
model = "within"
)
coeftest(
fe_adv_planet,
vcov = vcovHC(fe_adv_planet, type = "HC1", cluster = "group")
)##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation -0.043566 0.032931 -1.3229 0.1889983
## inflation -0.197682 0.052792 -3.7445 0.0003081 ***
## pop_growth -0.519287 0.463423 -1.1205 0.2652767
## urban_growth 0.363490 0.333668 1.0894 0.2787153
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# 1) Fixed Effect - Full Sample
fe_partnership <- plm(
partnership ~ innovation + inflation + pop_growth + urban_growth,
data = pdata,
model = "within"
)
summary(fe_partnership)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = partnership ~ innovation + inflation + pop_growth +
## urban_growth, data = pdata, model = "within")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -14.58662 -3.24186 -0.26078 2.29493 22.67526
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## innovation 0.043004 0.040450 1.0632 0.2890209
## inflation 0.240690 0.060649 3.9686 0.0001014 ***
## pop_growth 0.619910 1.044795 0.5933 0.5536429
## urban_growth -0.173052 0.916368 -0.1888 0.8504096
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 7517.4
## Residual Sum of Squares: 6724.7
## R-Squared: 0.10545
## Adj. R-Squared: 0.00047321
## F-statistic: 5.77592 on 4 and 196 DF, p-value: 0.00020502coeftest(
fe_partnership,
vcov = vcovHC(fe_partnership, type = "HC1", cluster = "group")
)##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation 0.043004 0.061586 0.6983 0.4858334
## inflation 0.240690 0.061285 3.9274 0.0001189 ***
## pop_growth 0.619910 0.744969 0.8321 0.4063489
## urban_growth -0.173052 0.647657 -0.2672 0.7895987
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# 2) Random Effect - Full Sample
re_partnership <- plm(
partnership ~ innovation + inflation + pop_growth + urban_growth,
data = pdata,
model = "random"
)
summary(re_partnership)## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = partnership ~ innovation + inflation + pop_growth +
## urban_growth, data = pdata, model = "random")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Effects:
## var std.dev share
## idiosyncratic 34.310 5.857 0.04
## individual 819.810 28.632 0.96
## theta: 0.9384
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -14.02895 -3.59439 -0.89547 2.30030 28.55701
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) 59.734781 7.313974 8.1672 3.156e-16 ***
## innovation 0.051604 0.040801 1.2648 0.2059540
## inflation 0.235800 0.061283 3.8477 0.0001192 ***
## pop_growth 0.579548 1.058095 0.5477 0.5838788
## urban_growth -0.127987 0.927593 -0.1380 0.8902584
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 8377.2
## Residual Sum of Squares: 7584
## R-Squared: 0.094686
## Adj. R-Squared: 0.077843
## Chisq: 22.4867 on 4 DF, p-value: 0.00016031# 3) Hausman Test
phtest(fe_partnership, re_partnership)##
## Hausman Test
##
## data: partnership ~ innovation + inflation + pop_growth + urban_growth
## chisq = 2.951, df = 4, p-value = 0.5661
## alternative hypothesis: one model is inconsistent# Developing Countries
fe_dev_partnership <- plm(
partnership ~ innovation + inflation + pop_growth + urban_growth,
data = developing,
model = "within"
)
coeftest(
fe_dev_partnership,
vcov = vcovHC(fe_dev_partnership, type = "HC1", cluster = "group")
)##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation 0.024269 0.064874 0.3741 0.7092
## inflation 0.206543 0.042567 4.8522 4.717e-06 ***
## pop_growth 0.793073 0.732546 1.0826 0.2817
## urban_growth -0.375639 0.648665 -0.5791 0.5639
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Developed Countries
fe_adv_partnership <- plm(
partnership ~ innovation + inflation + pop_growth + urban_growth,
data = developed,
model = "within"
)
coeftest(
fe_adv_partnership,
vcov = vcovHC(fe_adv_partnership, type = "HC1", cluster = "group")
)##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation 0.030311 0.168087 0.1803 0.85727
## inflation 1.352131 0.529864 2.5518 0.01229 *
## pop_growth 0.423550 2.259457 0.1875 0.85170
## urban_growth -0.745084 2.017032 -0.3694 0.71265
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# 1) Fixed Effect - Full Sample
fe_peace <- plm(
peace ~ innovation + inflation + pop_growth + urban_growth,
data = pdata,
model = "within"
)
summary(fe_peace)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = peace ~ innovation + inflation + pop_growth + urban_growth,
## data = pdata, model = "within")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -6.70257 -1.60393 0.19976 1.62031 7.75459
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## innovation 0.027534 0.018043 1.5260 0.1286119
## inflation -0.023745 0.027053 -0.8778 0.3811517
## pop_growth -1.084759 0.466033 -2.3276 0.0209518 *
## urban_growth 1.370221 0.408749 3.3522 0.0009619 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 1459.1
## Residual Sum of Squares: 1338
## R-Squared: 0.083012
## Adj. R-Squared: -0.024594
## F-statistic: 4.43582 on 4 and 196 DF, p-value: 0.0018722coeftest(
fe_peace,
vcov = vcovHC(fe_peace, type = "HC1", cluster = "group")
)##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation 0.027534 0.039845 0.6910 0.4903730
## inflation -0.023745 0.036522 -0.6502 0.5163398
## pop_growth -1.084759 0.345812 -3.1368 0.0019709 **
## urban_growth 1.370221 0.400403 3.4221 0.0007563 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# 2) Random Effect - Full Sample
re_peace <- plm(
peace ~ innovation + inflation + pop_growth + urban_growth,
data = pdata,
model = "random"
)
summary(re_peace)## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = peace ~ innovation + inflation + pop_growth + urban_growth,
## data = pdata, model = "random")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Effects:
## var std.dev share
## idiosyncratic 6.826 2.613 0.075
## individual 84.194 9.176 0.925
## theta: 0.9145
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -8.19667 -1.80662 0.17292 2.00968 6.68124
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) 65.526408 2.708807 24.1901 < 2.2e-16 ***
## innovation 0.040917 0.019143 2.1374 0.032564 *
## inflation -0.037723 0.028799 -1.3099 0.190245
## pop_growth -1.067278 0.498260 -2.1420 0.032193 *
## urban_growth 1.300261 0.436617 2.9780 0.002901 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 1821
## Residual Sum of Squares: 1685.5
## R-Squared: 0.074393
## Adj. R-Squared: 0.057172
## Chisq: 17.28 on 4 DF, p-value: 0.0017052# 3) Hausman Test
phtest(fe_peace, re_peace)##
## Hausman Test
##
## data: peace ~ innovation + inflation + pop_growth + urban_growth
## chisq = 6.4253, df = 4, p-value = 0.1696
## alternative hypothesis: one model is inconsistent# Developing Countries
fe_dev_peace <- plm(
peace ~ innovation + inflation + pop_growth + urban_growth,
data = developing,
model = "within"
)
coeftest(
fe_dev_peace,
vcov = vcovHC(fe_dev_peace, type = "HC1", cluster = "group")
)##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation 0.018495 0.041579 0.4448 0.657445
## inflation -0.013893 0.042839 -0.3243 0.746415
## pop_growth -0.981240 0.381010 -2.5754 0.011539 *
## urban_growth 1.372282 0.457074 3.0023 0.003415 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Developed Countries
fe_adv_peace <- plm(
peace ~ innovation + inflation + pop_growth + urban_growth,
data = developed,
model = "within"
)
coeftest(
fe_adv_peace,
vcov = vcovHC(fe_adv_peace, type = "HC1", cluster = "group")
)##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation 0.13309 0.12106 1.0994 0.2744
## inflation -0.27056 0.16821 -1.6085 0.1110
## pop_growth -2.20104 1.43696 -1.5317 0.1289
## urban_growth 1.79072 1.11614 1.6044 0.1119install.packages("stargazer")## Warning: package 'stargazer' is in use and will not be installedlibrary(stargazer)
stargazer(
fe_prosperity, fe_people, fe_planet, fe_partnership, fe_peace,
type = "text",
title = "Fixed Effects Results",
column.labels = c("Prosperity", "People", "Planet", "Partnership", "Peace"),
dep.var.labels.include = FALSE,
digits = 3
)##
## Fixed Effects Results
## ============================================================================
## Dependent variable:
## --------------------------------------------------
## Prosperity People Planet Partnership Peace
## (1) (2) (3) (4) (5)
## ----------------------------------------------------------------------------
## innovation -0.003 0.104*** -0.014*** 0.043 0.028
## (0.020) (0.030) (0.004) (0.040) (0.018)
##
## inflation 0.039 0.061 -0.006 0.241*** -0.024
## (0.030) (0.045) (0.007) (0.061) (0.027)
##
## pop_growth -1.327** 1.799** 0.096 0.620 -1.085**
## (0.515) (0.778) (0.114) (1.045) (0.466)
##
## urban_growth 0.269 -1.039 -0.157 -0.173 1.370***
## (0.452) (0.683) (0.100) (0.916) (0.409)
##
## ----------------------------------------------------------------------------
## Observations 220 220 220 220 220
## R2 0.092 0.111 0.062 0.105 0.083
## Adjusted R2 -0.014 0.007 -0.048 0.0005 -0.025
## F Statistic (df = 4; 196) 4.981*** 6.130*** 3.244** 5.776*** 4.436***
## ============================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01stargazer(
re_prosperity, re_people, re_planet, re_partnership, re_peace,
type = "text",
title = "Random Effects Results",
column.labels = c("Prosperity", "People", "Planet", "Partnership", "Peace"),
dep.var.labels.include = FALSE,
digits = 3,
se = list(
sqrt(diag(vcovHC(re_prosperity, type = "HC1", cluster = "group"))),
sqrt(diag(vcovHC(re_people, type = "HC1", cluster = "group"))),
sqrt(diag(vcovHC(re_planet, type = "HC1", cluster = "group"))),
sqrt(diag(vcovHC(re_partnership, type = "HC1", cluster = "group"))),
sqrt(diag(vcovHC(re_peace, type = "HC1", cluster = "group")))
)
)##
## Random Effects Results
## =================================================================
## Dependent variable:
## ----------------------------------------------------
## Prosperity People Planet Partnership Peace
## (1) (2) (3) (4) (5)
## -----------------------------------------------------------------
## innovation -0.020 0.114** -0.013 0.052 0.041
## (0.015) (0.046) (0.008) (0.062) (0.039)
##
## inflation 0.063*** 0.057 -0.007 0.236*** -0.038
## (0.018) (0.038) (0.008) (0.060) (0.034)
##
## pop_growth -1.438*** 1.794** 0.112 0.580 -1.067***
## (0.413) (0.838) (0.087) (0.755) (0.342)
##
## urban_growth 0.688* -1.024 -0.166*** -0.128 1.300***
## (0.397) (0.708) (0.064) (0.680) (0.383)
##
## Constant 3.244** 94.495*** 8.918*** 59.735*** 65.526***
## (1.384) (5.955) (1.714) (7.969) (4.816)
##
## -----------------------------------------------------------------
## Observations 220 220 220 220 220
## R2 0.111 0.111 0.046 0.095 0.074
## Adjusted R2 0.095 0.095 0.028 0.078 0.057
## F Statistic 26.893*** 26.924*** 10.275** 22.487*** 17.280***
## =================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01coeftest(fe_prosperity, vcov = vcovHC(fe_prosperity, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation -0.0030262 0.0112007 -0.2702 0.7873
## inflation 0.0391551 0.0317812 1.2320 0.2194
## pop_growth -1.3271898 0.1803145 -7.3604 4.931e-12 ***
## urban_growth 0.2690993 0.2397956 1.1222 0.2631
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coeftest(fe_people, vcov = vcovHC(fe_people, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation 0.104403 0.047138 2.2148 0.02792 *
## inflation 0.060890 0.038917 1.5646 0.11929
## pop_growth 1.799063 0.836237 2.1514 0.03267 *
## urban_growth -1.038783 0.699747 -1.4845 0.13928
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coeftest(fe_planet, vcov = vcovHC(fe_planet, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation -0.0144209 0.0085399 -1.6887 0.09287 .
## inflation -0.0055770 0.0080708 -0.6910 0.49038
## pop_growth 0.0964916 0.0884479 1.0909 0.27664
## urban_growth -0.1569759 0.0642528 -2.4431 0.01545 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coeftest(fe_partnership, vcov = vcovHC(fe_partnership, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation 0.043004 0.061586 0.6983 0.4858334
## inflation 0.240690 0.061285 3.9274 0.0001189 ***
## pop_growth 0.619910 0.744969 0.8321 0.4063489
## urban_growth -0.173052 0.647657 -0.2672 0.7895987
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coeftest(fe_peace, vcov = vcovHC(fe_peace, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation 0.027534 0.039845 0.6910 0.4903730
## inflation -0.023745 0.036522 -0.6502 0.5163398
## pop_growth -1.084759 0.345812 -3.1368 0.0019709 **
## urban_growth 1.370221 0.400403 3.4221 0.0007563 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Prosperity
twfe_prosperity <- plm(
prosperity ~ innovation + inflation + pop_growth + urban_growth,
data = pdata,
model = "within",
effect = "twoways"
)
summary(twfe_prosperity)## Twoways effects Within Model
##
## Call:
## plm(formula = prosperity ~ innovation + inflation + pop_growth +
## urban_growth, data = pdata, effect = "twoways", model = "within")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -5.745747 -0.672581 0.089172 0.673767 6.241315
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## innovation 0.016241 0.018734 0.8669 0.38710
## inflation -0.014460 0.019184 -0.7538 0.45194
## pop_growth -0.682002 0.317253 -2.1497 0.03287 *
## urban_growth -0.125201 0.280512 -0.4463 0.65588
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 638.56
## Residual Sum of Squares: 569.55
## R-Squared: 0.10807
## Adj. R-Squared: -0.05017
## F-statistic: 5.63439 on 4 and 186 DF, p-value: 0.00026544coeftest(
twfe_prosperity,
vcov = vcovHC(twfe_prosperity, type = "HC1", cluster = "group")
)##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation 0.016241 0.013738 1.1822 0.2386
## inflation -0.014460 0.013439 -1.0760 0.2833
## pop_growth -0.682002 0.086015 -7.9289 1.989e-13 ***
## urban_growth -0.125201 0.088076 -1.4215 0.1568
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# People
twfe_people <- plm(
people ~ innovation + inflation + pop_growth + urban_growth,
data = pdata,
model = "within",
effect = "twoways"
)
summary(twfe_people)## Twoways effects Within Model
##
## Call:
## plm(formula = people ~ innovation + inflation + pop_growth +
## urban_growth, data = pdata, effect = "twoways", model = "within")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -11.82897 -1.23066 -0.04688 1.21813 16.34940
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## innovation 0.126812 0.047647 2.6615 0.008461 **
## inflation 0.059812 0.048794 1.2258 0.221818
## pop_growth 1.837253 0.806907 2.2769 0.023930 *
## urban_growth -1.003027 0.713460 -1.4059 0.161433
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 3978.3
## Residual Sum of Squares: 3684.4
## R-Squared: 0.073864
## Adj. R-Squared: -0.090451
## F-statistic: 3.7086 on 4 and 186 DF, p-value: 0.0062618coeftest(
twfe_people,
vcov = vcovHC(twfe_people, type = "HC1", cluster = "group")
)##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation 0.126812 0.107455 1.1801 0.23945
## inflation 0.059812 0.031766 1.8829 0.06127 .
## pop_growth 1.837253 0.799279 2.2986 0.02264 *
## urban_growth -1.003027 0.742537 -1.3508 0.17840
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Planet
twfe_planet <- plm(
planet ~ innovation + inflation + pop_growth + urban_growth,
data = pdata,
model = "within",
effect = "twoways"
)
summary(twfe_planet)## Twoways effects Within Model
##
## Call:
## plm(formula = planet ~ innovation + inflation + pop_growth +
## urban_growth, data = pdata, effect = "twoways", model = "within")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -1.251333 -0.238935 -0.012009 0.234201 1.268538
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## innovation 0.0406261 0.0044725 9.0835 < 2e-16 ***
## inflation 0.0030973 0.0045801 0.6763 0.49972
## pop_growth 0.1525493 0.0757415 2.0141 0.04544 *
## urban_growth -0.1289809 0.0669699 -1.9260 0.05563 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 49.111
## Residual Sum of Squares: 32.463
## R-Squared: 0.33898
## Adj. R-Squared: 0.22171
## F-statistic: 23.8463 on 4 and 186 DF, p-value: 6.1928e-16coeftest(
twfe_planet,
vcov = vcovHC(twfe_planet, type = "HC1", cluster = "group")
)##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation 0.0406261 0.0080134 5.0698 9.577e-07 ***
## inflation 0.0030973 0.0044454 0.6967 0.48683
## pop_growth 0.1525493 0.0889109 1.7158 0.08787 .
## urban_growth -0.1289809 0.0504951 -2.5543 0.01144 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Partnership
twfe_partnership <- plm(
partnership ~ innovation + inflation + pop_growth + urban_growth,
data = pdata,
model = "within",
effect = "twoways"
)
summary(twfe_partnership)## Twoways effects Within Model
##
## Call:
## plm(formula = partnership ~ innovation + inflation + pop_growth +
## urban_growth, data = pdata, effect = "twoways", model = "within")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -14.476587 -2.430891 0.037557 2.502254 17.336019
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## innovation -0.166537 0.049366 -3.3735 0.000903 ***
## inflation 0.066078 0.050553 1.3071 0.192795
## pop_growth 1.131432 0.836007 1.3534 0.177578
## urban_growth -1.426034 0.739190 -1.9292 0.055230 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 4343.9
## Residual Sum of Squares: 3955
## R-Squared: 0.089529
## Adj. R-Squared: -0.072006
## F-statistic: 4.57249 on 4 and 186 DF, p-value: 0.0015186coeftest(
twfe_partnership,
vcov = vcovHC(twfe_partnership, type = "HC1", cluster = "group")
)##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation -0.166537 0.070551 -2.3605 0.01928 *
## inflation 0.066078 0.053779 1.2287 0.22074
## pop_growth 1.131432 0.663641 1.7049 0.08989 .
## urban_growth -1.426034 0.655775 -2.1746 0.03092 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Peace
twfe_peace <- plm(
peace ~ innovation + inflation + pop_growth + urban_growth,
data = pdata,
model = "within",
effect = "twoways"
)
summary(twfe_peace)## Twoways effects Within Model
##
## Call:
## plm(formula = peace ~ innovation + inflation + pop_growth + urban_growth,
## data = pdata, effect = "twoways", model = "within")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -6.70093 -1.55753 -0.11623 1.59964 8.10860
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## innovation 0.043292 0.028175 1.5365 0.1261057
## inflation -0.015869 0.028853 -0.5500 0.5829758
## pop_growth -1.050688 0.477147 -2.2020 0.0288935 *
## urban_growth 1.434703 0.421889 3.4007 0.0008227 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 1416.6
## Residual Sum of Squares: 1288.3
## R-Squared: 0.090549
## Adj. R-Squared: -0.070805
## F-statistic: 4.62976 on 4 and 186 DF, p-value: 0.0013822coeftest(
twfe_peace,
vcov = vcovHC(twfe_peace, type = "HC1", cluster = "group")
)##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## innovation 0.043292 0.049721 0.8707 0.3850336
## inflation -0.015869 0.040835 -0.3886 0.6980022
## pop_growth -1.050688 0.341785 -3.0741 0.0024290 **
## urban_growth 1.434703 0.388349 3.6944 0.0002897 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(modelsummary)## Warning: package 'modelsummary' was built under R version 4.4.3modelsummary(
list(
"Prosperity" = twfe_prosperity,
"People" = twfe_people,
"Planet" = twfe_planet,
"Partnership" = twfe_partnership,
"Peace" = twfe_peace
),
stars = TRUE,
output = "markdown"
)| Prosperity | People | Planet | Partnership | Peace | |
|---|---|---|---|---|---|
| innovation | 0.016 | 0.127** | 0.041*** | -0.167*** | 0.043 |
| (0.019) | (0.048) | (0.004) | (0.049) | (0.028) | |
| inflation | -0.014 | 0.060 | 0.003 | 0.066 | -0.016 |
| (0.019) | (0.049) | (0.005) | (0.051) | (0.029) | |
| pop_growth | -0.682* | 1.837* | 0.153* | 1.131 | -1.051* |
| (0.317) | (0.807) | (0.076) | (0.836) | (0.477) | |
| urban_growth | -0.125 | -1.003 | -0.129+ | -1.426+ | 1.435*** |
| (0.281) | (0.713) | (0.067) | (0.739) | (0.422) | |
| Num.Obs. | 220 | 220 | 220 | 220 | 220 |
| R2 | 0.108 | 0.074 | 0.339 | 0.090 | 0.091 |
| R2 Adj. | -0.050 | -0.090 | 0.222 | -0.072 | -0.071 |
| AIC | 843.6 | 1254.3 | 213.4 | 1269.9 | 1023.2 |
| BIC | 860.6 | 1271.3 | 230.3 | 1286.9 | 1040.1 |
| RMSE | 1.61 | 4.09 | 0.38 | 4.24 | 2.42 |
|
|||||
# Create country-level means
pdata$mean_innovation <- ave(pdata$innovation, pdata$country)
pdata$mean_inflation <- ave(pdata$inflation, pdata$country)
pdata$mean_pop_growth <- ave(pdata$pop_growth, pdata$country)
pdata$mean_urban_growth <- ave(pdata$urban_growth, pdata$country)
# Check emerging dummy
table(pdata$emerging)##
## 0 1
## 110 110cre_prosperity <- plm(
prosperity ~ innovation + inflation + pop_growth + urban_growth +
emerging + mean_innovation + mean_inflation +
mean_pop_growth + mean_urban_growth,
data = pdata,
model = "random"
)
cre_people <- plm(
people ~ innovation + inflation + pop_growth + urban_growth +
emerging + mean_innovation + mean_inflation +
mean_pop_growth + mean_urban_growth,
data = pdata,
model = "random"
)
cre_planet <- plm(
planet ~ innovation + inflation + pop_growth + urban_growth +
emerging + mean_innovation + mean_inflation +
mean_pop_growth + mean_urban_growth,
data = pdata,
model = "random"
)
cre_partnership <- plm(
partnership ~ innovation + inflation + pop_growth + urban_growth +
emerging + mean_innovation + mean_inflation +
mean_pop_growth + mean_urban_growth,
data = pdata,
model = "random"
)
cre_peace <- plm(
peace ~ innovation + inflation + pop_growth + urban_growth +
emerging + mean_innovation + mean_inflation +
mean_pop_growth + mean_urban_growth,
data = pdata,
model = "random"
)summary(cre_prosperity)## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = prosperity ~ innovation + inflation + pop_growth +
## urban_growth + emerging + mean_innovation + mean_inflation +
## mean_pop_growth + mean_urban_growth, data = pdata, model = "random")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Effects:
## var std.dev share
## idiosyncratic 8.3319 2.8865 0.954
## individual 0.3995 0.6321 0.046
## theta: 0.1909
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -10.74948 -0.88966 0.16356 1.34939 9.49080
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) 3.6809403 1.8912341 1.9463 0.051617 .
## innovation -0.0030262 0.0199332 -0.1518 0.879331
## inflation 0.0391551 0.0298872 1.3101 0.190164
## pop_growth -1.3271898 0.5148673 -2.5777 0.009945 **
## urban_growth 0.2690993 0.4515799 0.5959 0.551238
## emerging -0.8416570 0.8293534 -1.0148 0.310184
## mean_innovation -0.0310662 0.0291756 -1.0648 0.286968
## mean_inflation -0.0108225 0.0524076 -0.2065 0.836395
## mean_pop_growth -0.4497601 1.0730503 -0.4191 0.675113
## mean_urban_growth 1.6688875 0.9485019 1.7595 0.078493 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 2056.6
## Residual Sum of Squares: 1749.7
## R-Squared: 0.14922
## Adj. R-Squared: 0.11276
## Chisq: 36.8333 on 9 DF, p-value: 2.8183e-05summary(cre_people)## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = people ~ innovation + inflation + pop_growth +
## urban_growth + emerging + mean_innovation + mean_inflation +
## mean_pop_growth + mean_urban_growth, data = pdata, model = "random")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Effects:
## var std.dev share
## idiosyncratic 19.036 4.363 0.085
## individual 205.433 14.333 0.915
## theta: 0.9086
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -11.18215 -1.57780 -0.22613 1.30395 18.73039
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) 65.412752 25.307136 2.5848 0.009745 **
## innovation 0.104403 0.030130 3.4651 0.000530 ***
## inflation 0.060890 0.045176 1.3478 0.177709
## pop_growth 1.799063 0.778242 2.3117 0.020794 *
## urban_growth -1.038783 0.682581 -1.5218 0.128047
## emerging -17.252362 11.097812 -1.5546 0.120048
## mean_innovation 0.348995 0.286670 1.2174 0.223449
## mean_inflation 0.271003 0.577834 0.4690 0.639071
## mean_pop_growth -7.069205 12.621968 -0.5601 0.575431
## mean_urban_growth 13.235732 11.182249 1.1836 0.236557
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 4697.8
## Residual Sum of Squares: 3997.6
## R-Squared: 0.14905
## Adj. R-Squared: 0.11258
## Chisq: 36.7818 on 9 DF, p-value: 2.8785e-05summary(cre_planet)## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = planet ~ innovation + inflation + pop_growth +
## urban_growth + emerging + mean_innovation + mean_inflation +
## mean_pop_growth + mean_urban_growth, data = pdata, model = "random")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Effects:
## var std.dev share
## idiosyncratic 0.4113 0.6413 0.03
## individual 13.2344 3.6379 0.97
## theta: 0.9469
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -2.13800 -0.39195 -0.01792 0.39925 1.63398
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) -3.9661071 6.4054743 -0.6192 0.535801
## innovation -0.0144209 0.0044286 -3.2563 0.001129 **
## inflation -0.0055770 0.0066401 -0.8399 0.400968
## pop_growth 0.0964916 0.1143885 0.8435 0.398925
## urban_growth -0.1569759 0.1003278 -1.5646 0.117670
## emerging -3.7056110 2.8089607 -1.3192 0.187099
## mean_innovation 0.1460366 0.0722928 2.0201 0.043376 *
## mean_inflation -0.1036246 0.1459586 -0.7100 0.477730
## mean_pop_growth 5.1384807 3.1907115 1.6104 0.107300
## mean_urban_growth -0.5144821 2.8268355 -0.1820 0.855583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 101.9
## Residual Sum of Squares: 86.365
## R-Squared: 0.15245
## Adj. R-Squared: 0.11612
## Chisq: 37.7719 on 9 DF, p-value: 1.9152e-05summary(cre_partnership)## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = partnership ~ innovation + inflation + pop_growth +
## urban_growth + emerging + mean_innovation + mean_inflation +
## mean_pop_growth + mean_urban_growth, data = pdata, model = "random")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Effects:
## var std.dev share
## idiosyncratic 34.310 5.857 0.038
## individual 872.509 29.538 0.962
## theta: 0.9403
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -14.16434 -3.50657 -0.73696 2.41664 26.17133
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) -57.039877 52.029034 -1.0963 0.27294
## innovation 0.043004 0.040450 1.0632 0.28771
## inflation 0.240690 0.060649 3.9686 7.23e-05 ***
## pop_growth 0.619910 1.044795 0.5933 0.55296
## urban_growth -0.173052 0.916368 -0.1888 0.85021
## emerging 7.114247 22.816032 0.3118 0.75519
## mean_innovation 1.336464 0.587496 2.2748 0.02292 *
## mean_inflation -1.566780 1.185886 -1.3212 0.18644
## mean_pop_growth -53.641162 25.921242 -2.0694 0.03851 *
## mean_urban_growth 53.908156 22.965047 2.3474 0.01890 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 8325.5
## Residual Sum of Squares: 7205
## R-Squared: 0.13458
## Adj. R-Squared: 0.097492
## Chisq: 32.6571 on 9 DF, p-value: 0.00015321summary(cre_peace)## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = peace ~ innovation + inflation + pop_growth + urban_growth +
## emerging + mean_innovation + mean_inflation + mean_pop_growth +
## mean_urban_growth, data = pdata, model = "random")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Effects:
## var std.dev share
## idiosyncratic 6.826 2.613 0.163
## individual 35.084 5.923 0.837
## theta: 0.8682
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -6.67643 -1.50628 0.20879 1.72335 6.70523
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) 47.282338 10.506184 4.5004 6.782e-06 ***
## innovation 0.027534 0.018043 1.5260 0.1269999
## inflation -0.023745 0.027053 -0.8778 0.3800770
## pop_growth -1.084759 0.466033 -2.3276 0.0199311 *
## urban_growth 1.370221 0.408749 3.3522 0.0008016 ***
## emerging -21.428510 4.607224 -4.6511 3.302e-06 ***
## mean_innovation 0.341966 0.119718 2.8564 0.0042845 **
## mean_inflation -0.450004 0.240677 -1.8697 0.0615195 .
## mean_pop_growth -9.636802 5.250726 -1.8353 0.0664572 .
## mean_urban_growth 11.579174 4.651615 2.4893 0.0128002 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 2318.8
## Residual Sum of Squares: 1433.5
## R-Squared: 0.38179
## Adj. R-Squared: 0.35529
## Chisq: 129.689 on 9 DF, p-value: < 2.22e-16coeftest(cre_prosperity, vcov = vcovHC(cre_prosperity, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.6809403 1.6701496 2.2040 0.02861 *
## innovation -0.0030262 0.0113596 -0.2664 0.79019
## inflation 0.0391551 0.0322321 1.2148 0.22581
## pop_growth -1.3271898 0.1828723 -7.2575 7.52e-12 ***
## urban_growth 0.2690993 0.2431971 1.1065 0.26977
## emerging -0.8416570 0.6547398 -1.2855 0.20004
## mean_innovation -0.0310662 0.0243809 -1.2742 0.20400
## mean_inflation -0.0108225 0.0472939 -0.2288 0.81922
## mean_pop_growth -0.4497601 0.7376511 -0.6097 0.54271
## mean_urban_growth 1.6688875 0.7122907 2.3430 0.02007 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coeftest(cre_people, vcov = vcovHC(cre_people, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 65.412752 21.331878 3.0664 0.002451 **
## innovation 0.104403 0.047807 2.1838 0.030081 *
## inflation 0.060890 0.039469 1.5427 0.124401
## pop_growth 1.799063 0.848099 2.1213 0.035070 *
## urban_growth -1.038783 0.709673 -1.4637 0.144758
## emerging -17.252362 11.818185 -1.4598 0.145835
## mean_innovation 0.348995 0.228569 1.5269 0.128299
## mean_inflation 0.271003 0.391496 0.6922 0.489561
## mean_pop_growth -7.069205 10.725002 -0.6591 0.510532
## mean_urban_growth 13.235732 8.701514 1.5211 0.129743
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coeftest(cre_planet, vcov = vcovHC(cre_planet, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.9661071 5.1289726 -0.7733 0.44023
## innovation -0.0144209 0.0086610 -1.6650 0.09740 .
## inflation -0.0055770 0.0081853 -0.6813 0.49641
## pop_growth 0.0964916 0.0897025 1.0757 0.28330
## urban_growth -0.1569759 0.0651643 -2.4089 0.01686 *
## emerging -3.7056110 2.1153907 -1.7517 0.08128 .
## mean_innovation 0.1460366 0.0591991 2.4669 0.01443 *
## mean_inflation -0.1036246 0.1164966 -0.8895 0.37475
## mean_pop_growth 5.1384807 2.7001126 1.9031 0.05840 .
## mean_urban_growth -0.5144821 2.2472443 -0.2289 0.81914
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coeftest(cre_partnership, vcov = vcovHC(cre_partnership, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -57.039877 42.875636 -1.3304 0.1848438
## innovation 0.043004 0.062460 0.6885 0.4918930
## inflation 0.240690 0.062154 3.8725 0.0001439 ***
## pop_growth 0.619910 0.755536 0.8205 0.4128671
## urban_growth -0.173052 0.656844 -0.2635 0.7924548
## emerging 7.114247 18.888073 0.3767 0.7068119
## mean_innovation 1.336464 0.492623 2.7130 0.0072219 **
## mean_inflation -1.566780 0.887015 -1.7664 0.0787897 .
## mean_pop_growth -53.641162 28.285147 -1.8964 0.0592742 .
## mean_urban_growth 53.908156 25.832454 2.0868 0.0381097 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coeftest(cre_peace, vcov = vcovHC(cre_peace, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 47.282338 11.652016 4.0579 6.984e-05 ***
## innovation 0.027534 0.040411 0.6814 0.4964000
## inflation -0.023745 0.037040 -0.6411 0.5221682
## pop_growth -1.084759 0.350717 -3.0930 0.0022509 **
## urban_growth 1.370221 0.406083 3.3742 0.0008815 ***
## emerging -21.428510 4.766258 -4.4959 1.145e-05 ***
## mean_innovation 0.341966 0.134285 2.5466 0.0115953 *
## mean_inflation -0.450004 0.097200 -4.6297 6.412e-06 ***
## mean_pop_growth -9.636802 2.385809 -4.0392 7.520e-05 ***
## mean_urban_growth 11.579174 2.870189 4.0343 7.668e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(modelsummary)
modelsummary(
list(
"Prosperity" = cre_prosperity,
"People" = cre_people,
"Planet" = cre_planet,
"Partnership" = cre_partnership,
"Peace" = cre_peace
),
stars = TRUE,
output = "markdown"
)| Prosperity | People | Planet | Partnership | Peace | |
|---|---|---|---|---|---|
| (Intercept) | 3.681+ | 65.413* | -3.966 | -57.040 | 47.282*** |
| (1.891) | (25.307) | (6.405) | (52.029) | (10.506) | |
| innovation | -0.003 | 0.104*** | -0.014** | 0.043 | 0.028 |
| (0.020) | (0.030) | (0.004) | (0.040) | (0.018) | |
| inflation | 0.039 | 0.061 | -0.006 | 0.241*** | -0.024 |
| (0.030) | (0.045) | (0.007) | (0.061) | (0.027) | |
| pop_growth | -1.327* | 1.799* | 0.096 | 0.620 | -1.085* |
| (0.515) | (0.778) | (0.114) | (1.045) | (0.466) | |
| urban_growth | 0.269 | -1.039 | -0.157 | -0.173 | 1.370*** |
| (0.452) | (0.683) | (0.100) | (0.916) | (0.409) | |
| emerging | -0.842 | -17.252 | -3.706 | 7.114 | -21.429*** |
| (0.829) | (11.098) | (2.809) | (22.816) | (4.607) | |
| mean_innovation | -0.031 | 0.349 | 0.146* | 1.336* | 0.342** |
| (0.029) | (0.287) | (0.072) | (0.587) | (0.120) | |
| mean_inflation | -0.011 | 0.271 | -0.104 | -1.567 | -0.450+ |
| (0.052) | (0.578) | (0.146) | (1.186) | (0.241) | |
| mean_pop_growth | -0.450 | -7.069 | 5.138 | -53.641* | -9.637+ |
| (1.073) | (12.622) | (3.191) | (25.921) | (5.251) | |
| mean_urban_growth | 1.669+ | 13.236 | -0.514 | 53.908* | 11.579* |
| (0.949) | (11.182) | (2.827) | (22.965) | (4.652) | |
| Num.Obs. | 220 | 220 | 220 | 220 | 220 |
| R2 | 0.149 | 0.149 | 0.152 | 0.135 | 0.382 |
| R2 Adj. | 0.113 | 0.113 | 0.116 | 0.097 | 0.355 |
| AIC | 1102.5 | 1284.3 | 440.6 | 1413.9 | 1058.7 |
| BIC | 1139.8 | 1321.6 | 478.0 | 1451.2 | 1096.0 |
| RMSE | 2.82 | 4.26 | 0.63 | 5.72 | 2.55 |
|
|||||
library(plm)
library(lmtest)
library(sandwich)
library(modelsummary)gmm_prosperity <- pgmm(
prosperity ~ lag(prosperity, 1) + innovation + inflation + pop_growth + urban_growth |
lag(prosperity, 2:3) + lag(innovation, 2:3),
data = pdata,
effect = "individual",
model = "twosteps",
transformation = "ld"
)## Warning in pgmm(prosperity ~ lag(prosperity, 1) + innovation + inflation + :
## the second-step matrix is singular, a general inverse is usedsummary(gmm_prosperity, robust = TRUE)## Warning in vcovHC.pgmm(object): a general inverse is used## Oneway (individual) effect Two-steps model System GMM
##
## Call:
## pgmm(formula = prosperity ~ lag(prosperity, 1) + innovation +
## inflation + pop_growth + urban_growth | lag(prosperity, 2:3) +
## lag(innovation, 2:3), data = pdata, effect = "individual",
## model = "twosteps", transformation = "ld")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Number of Observations Used: 380
## Residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -11.15288 -1.59401 -0.13528 -0.09092 1.12632 17.26963
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## lag(prosperity, 1) -0.0829879 0.0801417 -1.0355 0.30043
## innovation 0.0147178 0.0076776 1.9170 0.05524 .
## inflation 0.0770571 0.0196368 3.9241 8.704e-05 ***
## pop_growth -3.6072096 3.0113426 -1.1979 0.23097
## urban_growth 2.8367006 2.4939336 1.1374 0.25535
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sargan test: chisq(53) = 19.51368 (p-value = 0.99999)
## Autocorrelation test (1): normal = -4.172157 (p-value = 3.0173e-05)
## Autocorrelation test (2): normal = -1.635858 (p-value = 0.10187)
## Wald test for coefficients: chisq(5) = 60.38876 (p-value = 1.0102e-11)gmm_people <- pgmm(
people ~ lag(people, 1) + innovation + inflation + pop_growth + urban_growth |
lag(people, 2:3) + lag(innovation, 2:3),
data = pdata,
effect = "individual",
model = "twosteps",
transformation = "ld"
)## Warning in pgmm(people ~ lag(people, 1) + innovation + inflation + pop_growth +
## : the second-step matrix is singular, a general inverse is usedsummary(gmm_people, robust = TRUE)## Warning in vcovHC.pgmm(object): a general inverse is used## Oneway (individual) effect Two-steps model System GMM
##
## Call:
## pgmm(formula = people ~ lag(people, 1) + innovation + inflation +
## pop_growth + urban_growth | lag(people, 2:3) + lag(innovation,
## 2:3), data = pdata, effect = "individual", model = "twosteps",
## transformation = "ld")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Number of Observations Used: 380
## Residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -23.8215 -1.1966 -0.1629 -0.1806 0.6846 25.7498
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## lag(people, 1) 1.0145243 0.0156575 64.7947 <2e-16 ***
## innovation -0.0156447 0.0203505 -0.7688 0.4420
## inflation 0.0034494 0.0284166 0.1214 0.9034
## pop_growth -0.9494732 1.8210850 -0.5214 0.6021
## urban_growth 0.9081725 1.5484466 0.5865 0.5575
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sargan test: chisq(53) = 14.85826 (p-value = 1)
## Autocorrelation test (1): normal = -1.975582 (p-value = 0.048202)
## Autocorrelation test (2): normal = 1.318212 (p-value = 0.18743)
## Wald test for coefficients: chisq(5) = 204485.6 (p-value = < 2.22e-16)gmm_planet <- pgmm(
planet ~ lag(planet, 1) + innovation + inflation + pop_growth + urban_growth |
lag(planet, 2:3) + lag(innovation, 2:3),
data = pdata,
effect = "individual",
model = "twosteps",
transformation = "ld"
)## Warning in pgmm(planet ~ lag(planet, 1) + innovation + inflation + pop_growth +
## : the second-step matrix is singular, a general inverse is usedsummary(gmm_planet, robust = TRUE)## Warning in vcovHC.pgmm(object): a general inverse is used## Oneway (individual) effect Two-steps model System GMM
##
## Call:
## pgmm(formula = planet ~ lag(planet, 1) + innovation + inflation +
## pop_growth + urban_growth | lag(planet, 2:3) + lag(innovation,
## 2:3), data = pdata, effect = "individual", model = "twosteps",
## transformation = "ld")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Number of Observations Used: 380
## Residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.480720 -0.188976 -0.011857 -0.004745 0.160136 2.350122
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## lag(planet, 1) 0.9791390 0.0205394 47.6712 <2e-16 ***
## innovation 0.0006583 0.0031539 0.2087 0.8347
## inflation 0.0020438 0.0050565 0.4042 0.6861
## pop_growth -0.1301399 0.2262853 -0.5751 0.5652
## urban_growth 0.0948248 0.2350431 0.4034 0.6866
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sargan test: chisq(53) = 18.66998 (p-value = 1)
## Autocorrelation test (1): normal = -2.9581 (p-value = 0.0030954)
## Autocorrelation test (2): normal = -1.612135 (p-value = 0.10693)
## Wald test for coefficients: chisq(5) = 59770.6 (p-value = < 2.22e-16)gmm_partnership <- pgmm(
partnership ~ lag(partnership, 1) + innovation + inflation + pop_growth + urban_growth |
lag(partnership, 2:3) + lag(innovation, 2:3),
data = pdata,
effect = "individual",
model = "twosteps",
transformation = "ld"
)## Warning in pgmm(partnership ~ lag(partnership, 1) + innovation + inflation + :
## the second-step matrix is singular, a general inverse is usedsummary(gmm_partnership, robust = TRUE)## Warning in vcovHC.pgmm(object): a general inverse is used## Oneway (individual) effect Two-steps model System GMM
##
## Call:
## pgmm(formula = partnership ~ lag(partnership, 1) + innovation +
## inflation + pop_growth + urban_growth | lag(partnership,
## 2:3) + lag(innovation, 2:3), data = pdata, effect = "individual",
## model = "twosteps", transformation = "ld")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Number of Observations Used: 380
## Residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -39.45528 -3.55603 -0.05144 0.07740 3.88720 23.67903
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## lag(partnership, 1) 0.967443 0.090516 10.6881 <2e-16 ***
## innovation 0.031618 0.069987 0.4518 0.6514
## inflation 0.056249 0.080540 0.6984 0.4849
## pop_growth -1.325611 1.780903 -0.7443 0.4567
## urban_growth 0.434590 1.640797 0.2649 0.7911
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sargan test: chisq(53) = 18.96842 (p-value = 1)
## Autocorrelation test (1): normal = -2.657468 (p-value = 0.007873)
## Autocorrelation test (2): normal = -3.19606 (p-value = 0.0013932)
## Wald test for coefficients: chisq(5) = 4999.666 (p-value = < 2.22e-16)gmm_peace <- pgmm(
peace ~ lag(peace, 1) + innovation + inflation + pop_growth + urban_growth |
lag(peace, 2:3) + lag(innovation, 2:3),
data = pdata,
effect = "individual",
model = "twosteps",
transformation = "ld"
)## Warning in pgmm(peace ~ lag(peace, 1) + innovation + inflation + pop_growth + :
## the second-step matrix is singular, a general inverse is usedsummary(gmm_peace, robust = TRUE)## Warning in vcovHC.pgmm(object): a general inverse is used## Oneway (individual) effect Two-steps model System GMM
##
## Call:
## pgmm(formula = peace ~ lag(peace, 1) + innovation + inflation +
## pop_growth + urban_growth | lag(peace, 2:3) + lag(innovation,
## 2:3), data = pdata, effect = "individual", model = "twosteps",
## transformation = "ld")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Number of Observations Used: 380
## Residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -8.38213 -1.32595 0.03738 0.07950 1.45928 10.93956
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## lag(peace, 1) 0.9991631 0.0354451 28.1890 <2e-16 ***
## innovation 0.0027963 0.0263189 0.1062 0.9154
## inflation -0.0052132 0.0197662 -0.2637 0.7920
## pop_growth 0.5017130 1.3702688 0.3661 0.7143
## urban_growth -0.6384434 1.2448329 -0.5129 0.6080
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sargan test: chisq(53) = 17.77763 (p-value = 1)
## Autocorrelation test (1): normal = -3.042204 (p-value = 0.0023485)
## Autocorrelation test (2): normal = 1.104659 (p-value = 0.26931)
## Wald test for coefficients: chisq(5) = 39995.75 (p-value = < 2.22e-16)modelsummary(
list(
"Prosperity" = gmm_prosperity,
"People" = gmm_people,
"Planet" = gmm_planet,
"Partnership" = gmm_partnership,
"Peace" = gmm_peace
),
stars = TRUE,
output = "markdown"
)## Warning: `modelsummary could not extract goodness-of-fit statistics from a model
## of class "pgmm". The package tried a sequence of 2 helper functions:
##
## performance::model_performance(model)
## broom::glance(model)
##
## One of these functions must return a one-row `data.frame`. The `modelsummary` website explains how to summarize unsupported models or add support for new models yourself:
##
## https://modelsummary.com/vignettes/modelsummary.html
## Warning: `modelsummary could not extract goodness-of-fit statistics from a model
## of class "pgmm". The package tried a sequence of 2 helper functions:
##
## performance::model_performance(model)
## broom::glance(model)
##
## One of these functions must return a one-row `data.frame`. The `modelsummary` website explains how to summarize unsupported models or add support for new models yourself:
##
## https://modelsummary.com/vignettes/modelsummary.html
## Warning: `modelsummary could not extract goodness-of-fit statistics from a model
## of class "pgmm". The package tried a sequence of 2 helper functions:
##
## performance::model_performance(model)
## broom::glance(model)
##
## One of these functions must return a one-row `data.frame`. The `modelsummary` website explains how to summarize unsupported models or add support for new models yourself:
##
## https://modelsummary.com/vignettes/modelsummary.html
## Warning: `modelsummary could not extract goodness-of-fit statistics from a model
## of class "pgmm". The package tried a sequence of 2 helper functions:
##
## performance::model_performance(model)
## broom::glance(model)
##
## One of these functions must return a one-row `data.frame`. The `modelsummary` website explains how to summarize unsupported models or add support for new models yourself:
##
## https://modelsummary.com/vignettes/modelsummary.html
## Warning: `modelsummary could not extract goodness-of-fit statistics from a model
## of class "pgmm". The package tried a sequence of 2 helper functions:
##
## performance::model_performance(model)
## broom::glance(model)
##
## One of these functions must return a one-row `data.frame`. The `modelsummary` website explains how to summarize unsupported models or add support for new models yourself:
##
## https://modelsummary.com/vignettes/modelsummary.html| Prosperity | People | Planet | Partnership | Peace | |
|---|---|---|---|---|---|
| lag(prosperity, 1) | -0.083** | ||||
| (0.029) | |||||
| innovation | 0.015*** | -0.016 | 0.001 | 0.032 | 0.003 |
| (0.004) | (0.013) | (0.002) | (0.039) | (0.015) | |
| inflation | 0.077*** | 0.003 | 0.002 | 0.056 | -0.005 |
| (0.008) | (0.017) | (0.002) | (0.039) | (0.012) | |
| pop_growth | -3.607* | -0.949 | -0.130 | -1.326 | 0.502 |
| (1.798) | (1.169) | (0.118) | (0.948) | (1.111) | |
| urban_growth | 2.837+ | 0.908 | 0.095 | 0.435 | -0.638 |
| (1.479) | (1.010) | (0.127) | (0.870) | (1.026) | |
| lag(people, 1) | 1.015*** | ||||
| (0.008) | |||||
| lag(planet, 1) | 0.979*** | ||||
| (0.011) | |||||
| lag(partnership, 1) | 0.967*** | ||||
| (0.052) | |||||
| lag(peace, 1) | 0.999*** | ||||
| (0.022) | |||||
|
|||||
summary(gmm_prosperity, robust = TRUE)## Warning in vcovHC.pgmm(object): a general inverse is used## Oneway (individual) effect Two-steps model System GMM
##
## Call:
## pgmm(formula = prosperity ~ lag(prosperity, 1) + innovation +
## inflation + pop_growth + urban_growth | lag(prosperity, 2:3) +
## lag(innovation, 2:3), data = pdata, effect = "individual",
## model = "twosteps", transformation = "ld")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Number of Observations Used: 380
## Residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -11.15288 -1.59401 -0.13528 -0.09092 1.12632 17.26963
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## lag(prosperity, 1) -0.0829879 0.0801417 -1.0355 0.30043
## innovation 0.0147178 0.0076776 1.9170 0.05524 .
## inflation 0.0770571 0.0196368 3.9241 8.704e-05 ***
## pop_growth -3.6072096 3.0113426 -1.1979 0.23097
## urban_growth 2.8367006 2.4939336 1.1374 0.25535
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sargan test: chisq(53) = 19.51368 (p-value = 0.99999)
## Autocorrelation test (1): normal = -4.172157 (p-value = 3.0173e-05)
## Autocorrelation test (2): normal = -1.635858 (p-value = 0.10187)
## Wald test for coefficients: chisq(5) = 60.38876 (p-value = 1.0102e-11)summary(pdata$innovation)## total sum of squares: 82821.48
## id time
## 0.7186724 0.1717624
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 13.50 69.68 84.80 77.98 92.34 100.00summary(pdata$prosperity)## total sum of squares: 2192.438
## id time
## 0.1794283 0.5293148
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -9.1029 0.5272 1.6561 1.5219 2.9813 10.8246summary(pdata$people)## total sum of squares: 64044.34
## id time
## 0.934453321 0.003429251
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 73.25 98.35 102.63 104.06 106.82 159.11summary(pdata$planet)## total sum of squares: 5749.602
## id time
## 0.985052098 0.006406287
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.680 3.875 6.238 7.784 10.362 20.697summary(pdata$partnership)## total sum of squares: 234381.3
## id time
## 0.96792673 0.01353995
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 23.40 42.23 59.31 65.34 75.97 184.11summary(pdata$peace)## total sum of squares: 50923.83
## id time
## 0.9713476533 0.0008343312
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 38.82 54.98 68.34 69.16 84.31 91.94summary(pdata$inflation)## total sum of squares: 21351.6
## id time
## 0.43849531 0.07306699
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -15.827 1.499 3.071 5.147 6.010 95.526# Count zero or negative values
sapply(
pdata[, c("innovation", "prosperity", "people", "planet", "partnership", "peace")],
function(x) sum(x <= 0, na.rm = TRUE)
)## innovation prosperity people planet partnership peace
## 0 48 0 0 0 0# Inflation check for log(1 + inflation)
sum(1 + pdata$inflation <= 0, na.rm = TRUE)## [1] 7# Variables with positive values only
pdata$ln_innovation <- log(pdata$innovation)
pdata$ln_people <- log(pdata$people)
pdata$ln_planet <- log(pdata$planet)
pdata$ln_partnership <- log(pdata$partnership)
pdata$ln_peace <- log(pdata$peace)
# Prosperity cannot be fully logged
# because it contains zero/negative values
# Inflation cannot be fully logged
# because some observations <= -1library(plm)
library(sandwich)
library(lmtest)
fe_log_prosperity <- plm(prosperity ~ ln_innovation + inflation + pop_growth + urban_growth,
data = pdata, model = "within")
re_log_prosperity <- plm(prosperity ~ ln_innovation + inflation + pop_growth + urban_growth,
data = pdata, model = "random")
fe_log_people <- plm(ln_people ~ ln_innovation + inflation + pop_growth + urban_growth,
data = pdata, model = "within")
re_log_people <- plm(ln_people ~ ln_innovation + inflation + pop_growth + urban_growth,
data = pdata, model = "random")
fe_log_planet <- plm(ln_planet ~ ln_innovation + inflation + pop_growth + urban_growth,
data = pdata, model = "within")
re_log_planet <- plm(ln_planet ~ ln_innovation + inflation + pop_growth + urban_growth,
data = pdata, model = "random")
fe_log_partnership <- plm(ln_partnership ~ ln_innovation + inflation + pop_growth + urban_growth,
data = pdata, model = "within")
re_log_partnership <- plm(ln_partnership ~ ln_innovation + inflation + pop_growth + urban_growth,
data = pdata, model = "random")
fe_log_peace <- plm(ln_peace ~ ln_innovation + inflation + pop_growth + urban_growth,
data = pdata, model = "within")
re_log_peace <- plm(ln_peace ~ ln_innovation + inflation + pop_growth + urban_growth,
data = pdata, model = "random")
# Hausman
phtest(fe_log_prosperity, re_log_prosperity)##
## Hausman Test
##
## data: prosperity ~ ln_innovation + inflation + pop_growth + urban_growth
## chisq = 8.491, df = 4, p-value = 0.07516
## alternative hypothesis: one model is inconsistentphtest(fe_log_people, re_log_people)##
## Hausman Test
##
## data: ln_people ~ ln_innovation + inflation + pop_growth + urban_growth
## chisq = 8.1942, df = 4, p-value = 0.08472
## alternative hypothesis: one model is inconsistentphtest(fe_log_planet, re_log_planet)##
## Hausman Test
##
## data: ln_planet ~ ln_innovation + inflation + pop_growth + urban_growth
## chisq = 1.6426, df = 4, p-value = 0.8011
## alternative hypothesis: one model is inconsistentphtest(fe_log_partnership, re_log_partnership)##
## Hausman Test
##
## data: ln_partnership ~ ln_innovation + inflation + pop_growth + urban_growth
## chisq = 3.0438, df = 4, p-value = 0.5505
## alternative hypothesis: one model is inconsistentphtest(fe_log_peace, re_log_peace)##
## Hausman Test
##
## data: ln_peace ~ ln_innovation + inflation + pop_growth + urban_growth
## chisq = 4.9474, df = 4, p-value = 0.2927
## alternative hypothesis: one model is inconsistent# Robust FE results
coeftest(fe_log_prosperity, vcov = vcovHC(fe_log_prosperity, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## ln_innovation -0.527050 0.396250 -1.3301 0.1850
## inflation 0.039953 0.031243 1.2788 0.2025
## pop_growth -1.320302 0.176886 -7.4641 2.677e-12 ***
## urban_growth 0.249123 0.241614 1.0311 0.3038
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coeftest(fe_log_people, vcov = vcovHC(fe_log_people, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## ln_innovation 0.06813126 0.02011352 3.3873 0.0008529 ***
## inflation 0.00063750 0.00039718 1.6051 0.1100910
## pop_growth 0.01392411 0.00667057 2.0874 0.0381452 *
## urban_growth -0.00721260 0.00546656 -1.3194 0.1885739
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coeftest(fe_log_planet, vcov = vcovHC(fe_log_planet, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## ln_innovation 0.03975478 0.06891342 0.5769 0.56468
## inflation -0.00060774 0.00079345 -0.7659 0.44463
## pop_growth 0.01888996 0.01186146 1.5925 0.11287
## urban_growth -0.01626549 0.00839023 -1.9386 0.05398 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coeftest(fe_log_partnership, vcov = vcovHC(fe_log_partnership, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## ln_innovation 0.0267037 0.0410286 0.6509 0.515901
## inflation 0.0041367 0.0014168 2.9198 0.003912 **
## pop_growth 0.0106053 0.0115010 0.9221 0.357597
## urban_growth -0.0035579 0.0102969 -0.3455 0.730065
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coeftest(fe_log_peace, vcov = vcovHC(fe_log_peace, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## ln_innovation 0.03207279 0.02958084 1.0842 0.2795895
## inflation -0.00037929 0.00074595 -0.5085 0.6116993
## pop_growth -0.02020440 0.00647754 -3.1191 0.0020872 **
## urban_growth 0.02480187 0.00721182 3.4391 0.0007131 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1twfe_log_prosperity <- plm(prosperity ~ ln_innovation + inflation + pop_growth + urban_growth,
data = pdata, model = "within", effect = "twoways")
twfe_log_people <- plm(ln_people ~ ln_innovation + inflation + pop_growth + urban_growth,
data = pdata, model = "within", effect = "twoways")
twfe_log_planet <- plm(ln_planet ~ ln_innovation + inflation + pop_growth + urban_growth,
data = pdata, model = "within", effect = "twoways")
twfe_log_partnership <- plm(ln_partnership ~ ln_innovation + inflation + pop_growth + urban_growth,
data = pdata, model = "within", effect = "twoways")
twfe_log_peace <- plm(ln_peace ~ ln_innovation + inflation + pop_growth + urban_growth,
data = pdata, model = "within", effect = "twoways")
coeftest(twfe_log_prosperity, vcov = vcovHC(twfe_log_prosperity, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## ln_innovation -0.140083 0.519761 -0.2695 0.78783
## inflation -0.014613 0.014002 -1.0436 0.29800
## pop_growth -0.683021 0.090155 -7.5761 1.624e-12 ***
## urban_growth -0.152901 0.092257 -1.6573 0.09914 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coeftest(twfe_log_people, vcov = vcovHC(twfe_log_people, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## ln_innovation 0.07387084 0.03345150 2.2083 0.02845 *
## inflation 0.00061866 0.00032645 1.8951 0.05963 .
## pop_growth 0.01432944 0.00629870 2.2750 0.02405 *
## urban_growth -0.00723348 0.00538493 -1.3433 0.18082
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coeftest(twfe_log_planet, vcov = vcovHC(twfe_log_planet, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## ln_innovation 0.28542505 0.05262109 5.4242 1.796e-07 ***
## inflation 0.00092403 0.00052707 1.7532 0.0812224 .
## pop_growth 0.02814155 0.00769788 3.6558 0.0003336 ***
## urban_growth -0.01955922 0.00744323 -2.6278 0.0093118 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coeftest(twfe_log_partnership, vcov = vcovHC(twfe_log_partnership, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## ln_innovation -0.0690411 0.0377570 -1.8286 0.06907 .
## inflation 0.0013337 0.0012800 1.0420 0.29875
## pop_growth 0.0190530 0.0109598 1.7385 0.08379 .
## urban_growth -0.0211835 0.0107607 -1.9686 0.05049 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coeftest(twfe_log_peace, vcov = vcovHC(twfe_log_peace, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## ln_innovation 0.03772158 0.02616842 1.4415 0.1511272
## inflation -0.00023241 0.00080253 -0.2896 0.7724469
## pop_growth -0.01991018 0.00671105 -2.9668 0.0034046 **
## urban_growth 0.02588856 0.00731344 3.5399 0.0005058 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pdata$mean_ln_innovation <- ave(pdata$ln_innovation, pdata$country)
pdata$mean_inflation <- ave(pdata$inflation, pdata$country)
pdata$mean_pop_growth <- ave(pdata$pop_growth, pdata$country)
pdata$mean_urban_growth <- ave(pdata$urban_growth, pdata$country)
cre_log_prosperity <- plm(
prosperity ~ ln_innovation + inflation + pop_growth + urban_growth +
emerging + mean_ln_innovation + mean_inflation + mean_pop_growth + mean_urban_growth,
data = pdata, model = "random"
)
cre_log_people <- plm(
ln_people ~ ln_innovation + inflation + pop_growth + urban_growth +
emerging + mean_ln_innovation + mean_inflation + mean_pop_growth + mean_urban_growth,
data = pdata, model = "random"
)
cre_log_planet <- plm(
ln_planet ~ ln_innovation + inflation + pop_growth + urban_growth +
emerging + mean_ln_innovation + mean_inflation + mean_pop_growth + mean_urban_growth,
data = pdata, model = "random"
)
cre_log_partnership <- plm(
ln_partnership ~ ln_innovation + inflation + pop_growth + urban_growth +
emerging + mean_ln_innovation + mean_inflation + mean_pop_growth + mean_urban_growth,
data = pdata, model = "random"
)
cre_log_peace <- plm(
ln_peace ~ ln_innovation + inflation + pop_growth + urban_growth +
emerging + mean_ln_innovation + mean_inflation + mean_pop_growth + mean_urban_growth,
data = pdata, model = "random"
)
coeftest(cre_log_prosperity, vcov = vcovHC(cre_log_prosperity, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.31278352 4.25487482 2.6588 0.008447 **
## ln_innovation -0.52704978 0.40187132 -1.3115 0.191125
## inflation 0.03995297 0.03168645 1.2609 0.208749
## pop_growth -1.32030239 0.17939525 -7.3597 4.1e-12 ***
## urban_growth 0.24912333 0.24504168 1.0167 0.310487
## emerging -0.88004020 0.62613225 -1.4055 0.161345
## mean_ln_innovation -1.84320311 1.03970320 -1.7728 0.077709 .
## mean_inflation 0.00063311 0.04923179 0.0129 0.989752
## mean_pop_growth -0.14573248 0.69025177 -0.2111 0.832991
## mean_urban_growth 1.35825358 0.68863364 1.9724 0.049878 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coeftest(cre_log_people, vcov = vcovHC(cre_log_people, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.37375047 0.62286052 5.4165 1.656e-07 ***
## ln_innovation 0.06813126 0.02039883 3.3400 0.0009915 ***
## inflation 0.00063750 0.00040282 1.5826 0.1150156
## pop_growth 0.01392411 0.00676519 2.0582 0.0408056 *
## urban_growth -0.00721260 0.00554411 -1.3009 0.1947019
## emerging -0.14956171 0.10478317 -1.4273 0.1549658
## mean_ln_innovation 0.21767648 0.13467825 1.6163 0.1075371
## mean_inflation 0.00142285 0.00477218 0.2982 0.7658799
## mean_pop_growth -0.09858988 0.10975311 -0.8983 0.3700609
## mean_urban_growth 0.14396785 0.09686317 1.4863 0.1386992
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coeftest(cre_log_planet, vcov = vcovHC(cre_log_planet, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.07340198 1.63829838 -2.4864 0.0136853 *
## ln_innovation 0.03975478 0.06989096 0.5688 0.5700919
## inflation -0.00060774 0.00080471 -0.7552 0.4509576
## pop_growth 0.01888996 0.01202972 1.5703 0.1178568
## urban_growth -0.01626549 0.00850925 -1.9115 0.0573009 .
## emerging -0.49842122 0.27031617 -1.8438 0.0666146 .
## mean_ln_innovation 1.30589298 0.38476231 3.3940 0.0008233 ***
## mean_inflation -0.01283246 0.01634364 -0.7852 0.4332413
## mean_pop_growth 0.41075778 0.38419929 1.0691 0.2862399
## mean_urban_growth 0.04118362 0.33246188 0.1239 0.9015330
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coeftest(cre_log_partnership, vcov = vcovHC(cre_log_partnership, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.6451553 1.3547695 -0.4762 0.634420
## ln_innovation 0.0267037 0.0416105 0.6418 0.521733
## inflation 0.0041367 0.0014369 2.8790 0.004402 **
## pop_growth 0.0106053 0.0116641 0.9092 0.364273
## urban_growth -0.0035579 0.0104430 -0.3407 0.733671
## emerging 0.0355092 0.2661597 0.1334 0.893994
## mean_ln_innovation 1.0194184 0.3131005 3.2559 0.001318 **
## mean_inflation -0.0247400 0.0099785 -2.4793 0.013950 *
## mean_pop_growth -0.7365029 0.3297110 -2.2338 0.026552 *
## mean_urban_growth 0.7826150 0.3087091 2.5351 0.011969 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1coeftest(cre_log_peace, vcov = vcovHC(cre_log_peace, type = "HC1", cluster = "group"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.13564815 0.49768423 6.3005 1.715e-09 ***
## ln_innovation 0.03207279 0.03000045 1.0691 0.2862624
## inflation -0.00037929 0.00075653 -0.5014 0.6166479
## pop_growth -0.02020440 0.00656943 -3.0755 0.0023807 **
## urban_growth 0.02480187 0.00731412 3.3910 0.0008321 ***
## emerging -0.34166405 0.07723026 -4.4240 1.555e-05 ***
## mean_ln_innovation 0.24280465 0.11111806 2.1851 0.0299875 *
## mean_inflation -0.00899794 0.00165890 -5.4240 1.597e-07 ***
## mean_pop_growth -0.16143499 0.03458189 -4.6682 5.414e-06 ***
## mean_urban_growth 0.19877049 0.04528895 4.3889 1.803e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1gmm_log_prosperity <- pgmm(
prosperity ~ lag(prosperity, 1) + ln_innovation + inflation + pop_growth + urban_growth |
lag(prosperity, 2:3) + lag(ln_innovation, 2:3),
data = pdata, effect = "individual", model = "twosteps", transformation = "ld"
)## Warning in pgmm(prosperity ~ lag(prosperity, 1) + ln_innovation + inflation + :
## the second-step matrix is singular, a general inverse is usedgmm_log_people <- pgmm(
ln_people ~ lag(ln_people, 1) + ln_innovation + inflation + pop_growth + urban_growth |
lag(ln_people, 2:3) + lag(ln_innovation, 2:3),
data = pdata, effect = "individual", model = "twosteps", transformation = "ld"
)## Warning in pgmm(ln_people ~ lag(ln_people, 1) + ln_innovation + inflation + :
## the second-step matrix is singular, a general inverse is usedgmm_log_planet <- pgmm(
ln_planet ~ lag(ln_planet, 1) + ln_innovation + inflation + pop_growth + urban_growth |
lag(ln_planet, 2:3) + lag(ln_innovation, 2:3),
data = pdata, effect = "individual", model = "twosteps", transformation = "ld"
)## Warning in pgmm(ln_planet ~ lag(ln_planet, 1) + ln_innovation + inflation + :
## the second-step matrix is singular, a general inverse is usedgmm_log_partnership <- pgmm(
ln_partnership ~ lag(ln_partnership, 1) + ln_innovation + inflation + pop_growth + urban_growth |
lag(ln_partnership, 2:3) + lag(ln_innovation, 2:3),
data = pdata, effect = "individual", model = "twosteps", transformation = "ld"
)## Warning in pgmm(ln_partnership ~ lag(ln_partnership, 1) + ln_innovation + : the
## second-step matrix is singular, a general inverse is usedgmm_log_peace <- pgmm(
ln_peace ~ lag(ln_peace, 1) + ln_innovation + inflation + pop_growth + urban_growth |
lag(ln_peace, 2:3) + lag(ln_innovation, 2:3),
data = pdata, effect = "individual", model = "twosteps", transformation = "ld"
)## Warning in pgmm(ln_peace ~ lag(ln_peace, 1) + ln_innovation + inflation + : the
## second-step matrix is singular, a general inverse is usedsummary(gmm_log_prosperity, robust = TRUE)## Warning in vcovHC.pgmm(object): a general inverse is used## Oneway (individual) effect Two-steps model System GMM
##
## Call:
## pgmm(formula = prosperity ~ lag(prosperity, 1) + ln_innovation +
## inflation + pop_growth + urban_growth | lag(prosperity, 2:3) +
## lag(ln_innovation, 2:3), data = pdata, effect = "individual",
## model = "twosteps", transformation = "ld")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Number of Observations Used: 380
## Residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -11.09523 -1.50334 -0.14556 -0.06449 1.27169 17.30328
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## lag(prosperity, 1) -0.077686 0.079788 -0.9737 0.33023
## ln_innovation 0.311773 0.142174 2.1929 0.02831 *
## inflation 0.071123 0.015342 4.6358 3.556e-06 ***
## pop_growth -2.703746 1.145241 -2.3609 0.01823 *
## urban_growth 1.988795 1.052160 1.8902 0.05873 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sargan test: chisq(53) = 19.46524 (p-value = 0.99999)
## Autocorrelation test (1): normal = -3.634756 (p-value = 0.00027824)
## Autocorrelation test (2): normal = -1.586719 (p-value = 0.11258)
## Wald test for coefficients: chisq(5) = 78.5302 (p-value = 1.7031e-15)summary(gmm_log_people, robust = TRUE)## Warning in vcovHC.pgmm(object): a general inverse is used## Oneway (individual) effect Two-steps model System GMM
##
## Call:
## pgmm(formula = ln_people ~ lag(ln_people, 1) + ln_innovation +
## inflation + pop_growth + urban_growth | lag(ln_people, 2:3) +
## lag(ln_innovation, 2:3), data = pdata, effect = "individual",
## model = "twosteps", transformation = "ld")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Number of Observations Used: 380
## Residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.2040621 -0.0096939 0.0016394 0.0007794 0.0108544 0.1798164
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## lag(ln_people, 1) 1.01645231 0.01066042 95.3482 <2e-16 ***
## ln_innovation -0.01889862 0.01183297 -1.5971 0.1102
## inflation 0.00038869 0.00033917 1.1460 0.2518
## pop_growth -0.00059319 0.00558971 -0.1061 0.9155
## urban_growth 0.00421032 0.00572256 0.7357 0.4619
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sargan test: chisq(53) = 17.66239 (p-value = 1)
## Autocorrelation test (1): normal = -2.192009 (p-value = 0.028379)
## Autocorrelation test (2): normal = 1.188304 (p-value = 0.23471)
## Wald test for coefficients: chisq(5) = 4823052 (p-value = < 2.22e-16)summary(gmm_log_planet, robust = TRUE)## Warning in vcovHC.pgmm(object): a general inverse is used## Oneway (individual) effect Two-steps model System GMM
##
## Call:
## pgmm(formula = ln_planet ~ lag(ln_planet, 1) + ln_innovation +
## inflation + pop_growth + urban_growth | lag(ln_planet, 2:3) +
## lag(ln_innovation, 2:3), data = pdata, effect = "individual",
## model = "twosteps", transformation = "ld")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Number of Observations Used: 380
## Residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.1755267 -0.0312581 -0.0019850 -0.0008448 0.0291071 0.2317285
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## lag(ln_planet, 1) 0.9903802 0.0218261 45.3759 <2e-16 ***
## ln_innovation 0.0014704 0.0104878 0.1402 0.8885
## inflation 0.0001472 0.0004273 0.3445 0.7305
## pop_growth -0.0162505 0.0140591 -1.1559 0.2477
## urban_growth 0.0144163 0.0137817 1.0460 0.2955
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sargan test: chisq(53) = 19.64818 (p-value = 0.99999)
## Autocorrelation test (1): normal = -3.299177 (p-value = 0.00096969)
## Autocorrelation test (2): normal = -1.312738 (p-value = 0.18927)
## Wald test for coefficients: chisq(5) = 295465.2 (p-value = < 2.22e-16)summary(gmm_log_partnership, robust = TRUE)## Warning in vcovHC.pgmm(object): a general inverse is used## Oneway (individual) effect Two-steps model System GMM
##
## Call:
## pgmm(formula = ln_partnership ~ lag(ln_partnership, 1) + ln_innovation +
## inflation + pop_growth + urban_growth | lag(ln_partnership,
## 2:3) + lag(ln_innovation, 2:3), data = pdata, effect = "individual",
## model = "twosteps", transformation = "ld")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Number of Observations Used: 380
## Residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.3474205 -0.0636935 0.0005158 0.0000326 0.0700580 0.4395825
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## lag(ln_partnership, 1) 0.9088137 0.0516818 17.5848 < 2e-16 ***
## ln_innovation 0.0839340 0.0483603 1.7356 0.08263 .
## inflation 0.0012224 0.0012823 0.9533 0.34044
## pop_growth -0.0442075 0.0293730 -1.5050 0.13231
## urban_growth 0.0363622 0.0287888 1.2631 0.20656
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sargan test: chisq(53) = 19.23477 (p-value = 0.99999)
## Autocorrelation test (1): normal = -3.211723 (p-value = 0.0013194)
## Autocorrelation test (2): normal = -3.611547 (p-value = 0.00030438)
## Wald test for coefficients: chisq(5) = 235131.5 (p-value = < 2.22e-16)summary(gmm_log_peace, robust = TRUE)## Warning in vcovHC.pgmm(object): a general inverse is used## Oneway (individual) effect Two-steps model System GMM
##
## Call:
## pgmm(formula = ln_peace ~ lag(ln_peace, 1) + ln_innovation +
## inflation + pop_growth + urban_growth | lag(ln_peace, 2:3) +
## lag(ln_innovation, 2:3), data = pdata, effect = "individual",
## model = "twosteps", transformation = "ld")
##
## Balanced Panel: n = 20, T = 11, N = 220
##
## Number of Observations Used: 380
## Residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.1687445 -0.0212628 -0.0018075 -0.0002363 0.0195163 0.2080830
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## lag(ln_peace, 1) 1.00701453 0.02066479 48.7309 <2e-16 ***
## ln_innovation -0.00560856 0.01956729 -0.2866 0.7744
## inflation -0.00019038 0.00019330 -0.9849 0.3247
## pop_growth 0.00824978 0.00654220 1.2610 0.2073
## urban_growth -0.00940146 0.00704925 -1.3337 0.1823
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sargan test: chisq(53) = 18.31517 (p-value = 1)
## Autocorrelation test (1): normal = -2.818569 (p-value = 0.0048238)
## Autocorrelation test (2): normal = 1.142425 (p-value = 0.25328)
## Wald test for coefficients: chisq(5) = 3100304 (p-value = < 2.22e-16)# ==============================
# GMM Diagnostic Tests
# ==============================
library(plm)
# --------------------------------
# Prosperity
# --------------------------------
cat("\n========================")##
## ========================cat("\nProsperity - GMM Results")##
## Prosperity - GMM Resultscat("\n========================\n")##
## ========================summary(gmm_log_prosperity, robust = TRUE)$coefficients## Warning in vcovHC.pgmm(object): a general inverse is used## Estimate Std. Error z-value Pr(>|z|)
## lag(prosperity, 1) -0.0776859 0.07978751 -0.973660 3.302254e-01
## ln_innovation 0.3117729 0.14217382 2.192899 2.831463e-02
## inflation 0.0711228 0.01534220 4.635763 3.556233e-06
## pop_growth -2.7037457 1.14524108 -2.360853 1.823296e-02
## urban_growth 1.9887947 1.05215957 1.890203 5.873088e-02cat("\n--- Sargan Test ---\n")##
## --- Sargan Test ---sargan(gmm_log_prosperity)##
## Sargan test
##
## data: prosperity ~ lag(prosperity, 1) + ln_innovation + inflation + ...
## chisq = 19.465, df = 53, p-value = 1
## alternative hypothesis: overidentifying restrictions not validcat("\n--- AR(1) Test ---\n")##
## --- AR(1) Test ---mtest(gmm_log_prosperity, order = 1)##
## Arellano-Bond autocorrelation test of degree 1
##
## data: prosperity ~ lag(prosperity, 1) + ln_innovation + inflation + ...
## normal = -4.587, p-value = 4.497e-06
## alternative hypothesis: autocorrelation presentcat("\n--- AR(2) Test ---\n")##
## --- AR(2) Test ---mtest(gmm_log_prosperity, order = 2)##
## Arellano-Bond autocorrelation test of degree 2
##
## data: prosperity ~ lag(prosperity, 1) + ln_innovation + inflation + ...
## normal = -1.7365, p-value = 0.08247
## alternative hypothesis: autocorrelation present# --------------------------------
# People
# --------------------------------
cat("\n====================")##
## ====================cat("\nPeople - GMM Results")##
## People - GMM Resultscat("\n====================\n")##
## ====================summary(gmm_log_people, robust = TRUE)$coefficients## Warning in vcovHC.pgmm(object): a general inverse is used## Estimate Std. Error z-value Pr(>|z|)
## lag(ln_people, 1) 1.0164523102 0.0106604213 95.3482308 0.0000000
## ln_innovation -0.0188986154 0.0118329681 -1.5971154 0.1102400
## inflation 0.0003886905 0.0003391678 1.1460125 0.2517900
## pop_growth -0.0005931891 0.0055897095 -0.1061216 0.9154859
## urban_growth 0.0042103242 0.0057225597 0.7357414 0.4618881cat("\n--- Sargan Test ---\n")##
## --- Sargan Test ---sargan(gmm_log_people)##
## Sargan test
##
## data: ln_people ~ lag(ln_people, 1) + ln_innovation + inflation + pop_growth + ...
## chisq = 17.662, df = 53, p-value = 1
## alternative hypothesis: overidentifying restrictions not validcat("\n--- AR(1) Test ---\n")##
## --- AR(1) Test ---mtest(gmm_log_people, order = 1)##
## Arellano-Bond autocorrelation test of degree 1
##
## data: ln_people ~ lag(ln_people, 1) + ln_innovation + inflation + pop_growth + ...
## normal = -2.1927, p-value = 0.02833
## alternative hypothesis: autocorrelation presentcat("\n--- AR(2) Test ---\n")##
## --- AR(2) Test ---mtest(gmm_log_people, order = 2)##
## Arellano-Bond autocorrelation test of degree 2
##
## data: ln_people ~ lag(ln_people, 1) + ln_innovation + inflation + pop_growth + ...
## normal = 1.1888, p-value = 0.2345
## alternative hypothesis: autocorrelation present# --------------------------------
# Planet
# --------------------------------
cat("\n====================")##
## ====================cat("\nPlanet - GMM Results")##
## Planet - GMM Resultscat("\n====================\n")##
## ====================summary(gmm_log_planet, robust = TRUE)$coefficients## Warning in vcovHC.pgmm(object): a general inverse is used## Estimate Std. Error z-value Pr(>|z|)
## lag(ln_planet, 1) 0.9903801546 0.0218261407 45.3758715 0.0000000
## ln_innovation 0.0014704119 0.0104878527 0.1402014 0.8885008
## inflation 0.0001472034 0.0004272979 0.3444983 0.7304715
## pop_growth -0.0162504742 0.0140590477 -1.1558730 0.2477331
## urban_growth 0.0144162741 0.0137817112 1.0460438 0.2955408cat("\n--- Sargan Test ---\n")##
## --- Sargan Test ---sargan(gmm_log_planet)##
## Sargan test
##
## data: ln_planet ~ lag(ln_planet, 1) + ln_innovation + inflation + pop_growth + ...
## chisq = 19.648, df = 53, p-value = 1
## alternative hypothesis: overidentifying restrictions not validcat("\n--- AR(1) Test ---\n")##
## --- AR(1) Test ---mtest(gmm_log_planet, order = 1)##
## Arellano-Bond autocorrelation test of degree 1
##
## data: ln_planet ~ lag(ln_planet, 1) + ln_innovation + inflation + pop_growth + ...
## normal = -3.313, p-value = 0.000923
## alternative hypothesis: autocorrelation presentcat("\n--- AR(2) Test ---\n")##
## --- AR(2) Test ---mtest(gmm_log_planet, order = 2)##
## Arellano-Bond autocorrelation test of degree 2
##
## data: ln_planet ~ lag(ln_planet, 1) + ln_innovation + inflation + pop_growth + ...
## normal = -1.3129, p-value = 0.1892
## alternative hypothesis: autocorrelation present# --------------------------------
# Partnership
# --------------------------------
cat("\n===========================")##
## ===========================cat("\nPartnership - GMM Results")##
## Partnership - GMM Resultscat("\n===========================\n")##
## ===========================summary(gmm_log_partnership, robust = TRUE)$coefficients## Warning in vcovHC.pgmm(object): a general inverse is used## Estimate Std. Error z-value Pr(>|z|)
## lag(ln_partnership, 1) 0.908813671 0.051681829 17.5847814 3.222217e-69
## ln_innovation 0.083934008 0.048360284 1.7355979 8.263495e-02
## inflation 0.001222416 0.001282297 0.9533016 3.404373e-01
## pop_growth -0.044207533 0.029373048 -1.5050373 1.323145e-01
## urban_growth 0.036362244 0.028788833 1.2630677 2.065649e-01cat("\n--- Sargan Test ---\n")##
## --- Sargan Test ---sargan(gmm_log_partnership)##
## Sargan test
##
## data: ln_partnership ~ lag(ln_partnership, 1) + ln_innovation + inflation + ...
## chisq = 19.235, df = 53, p-value = 1
## alternative hypothesis: overidentifying restrictions not validcat("\n--- AR(1) Test ---\n")##
## --- AR(1) Test ---mtest(gmm_log_partnership, order = 1)##
## Arellano-Bond autocorrelation test of degree 1
##
## data: ln_partnership ~ lag(ln_partnership, 1) + ln_innovation + inflation + ...
## normal = -3.4432, p-value = 0.0005748
## alternative hypothesis: autocorrelation presentcat("\n--- AR(2) Test ---\n")##
## --- AR(2) Test ---mtest(gmm_log_partnership, order = 2)##
## Arellano-Bond autocorrelation test of degree 2
##
## data: ln_partnership ~ lag(ln_partnership, 1) + ln_innovation + inflation + ...
## normal = -3.6274, p-value = 0.0002863
## alternative hypothesis: autocorrelation present# --------------------------------
# Peace
# --------------------------------
cat("\n===================")##
## ===================cat("\nPeace - GMM Results")##
## Peace - GMM Resultscat("\n===================\n")##
## ===================summary(gmm_log_peace, robust = TRUE)$coefficients## Warning in vcovHC.pgmm(object): a general inverse is used## Estimate Std. Error z-value Pr(>|z|)
## lag(ln_peace, 1) 1.0070145335 0.0206647910 48.7309324 0.0000000
## ln_innovation -0.0056085591 0.0195672859 -0.2866294 0.7743961
## inflation -0.0001903811 0.0001932999 -0.9849001 0.3246732
## pop_growth 0.0082497799 0.0065422040 1.2610093 0.2073055
## urban_growth -0.0094014639 0.0070492505 -1.3336828 0.1823078cat("\n--- Sargan Test ---\n")##
## --- Sargan Test ---sargan(gmm_log_peace)##
## Sargan test
##
## data: ln_peace ~ lag(ln_peace, 1) + ln_innovation + inflation + pop_growth + ...
## chisq = 18.315, df = 53, p-value = 1
## alternative hypothesis: overidentifying restrictions not validcat("\n--- AR(1) Test ---\n")##
## --- AR(1) Test ---mtest(gmm_log_peace, order = 1)##
## Arellano-Bond autocorrelation test of degree 1
##
## data: ln_peace ~ lag(ln_peace, 1) + ln_innovation + inflation + pop_growth + ...
## normal = -2.8213, p-value = 0.004783
## alternative hypothesis: autocorrelation presentcat("\n--- AR(2) Test ---\n")##
## --- AR(2) Test ---mtest(gmm_log_peace, order = 2)##
## Arellano-Bond autocorrelation test of degree 2
##
## data: ln_peace ~ lag(ln_peace, 1) + ln_innovation + inflation + pop_growth + ...
## normal = 1.1435, p-value = 0.2528
## alternative hypothesis: autocorrelation presentgmm_summary_prosperity <- summary(gmm_log_prosperity, robust = TRUE)
cat(“====================”) cat(“Diagnostics”) cat(“====================”)
gmm_summary_prosperity\(sargan gmm_summary_prosperity\)m1 gmm_summary_prosperity$m2
gmm_summary_people <- summary(gmm_log_people, robust = TRUE)
cat(“====================”) cat(“Diagnostics”) cat(“====================”)
gmm_summary_people\(sargan gmm_summary_people\)m1 gmm_summary_people$m2
gmm_summary_planet <- summary(gmm_log_planet, robust = TRUE)
cat(“====================”) cat(“Diagnostics”) cat(“====================”)
gmm_summary_planet\(sargan gmm_summary_planet\)m1 gmm_summary_planet$m2
gmm_summary_partnership <- summary(gmm_log_partnership, robust = TRUE)
cat(“====================”) cat(“Diagnostics”) cat(“====================”)
gmm_summary_partnership\(sargan gmm_summary_partnership\)m1 gmm_summary_partnership$m2
gmm_summary_peace <- summary(gmm_log_peace, robust = TRUE)
cat(“====================”) cat(“Diagnostics”) cat(“====================”)
gmm_summary_peace\(sargan gmm_summary_peace\)m1 gmm_summary_peace$m2
# ----------------------------
# 5) Tables
# ----------------------------
``` r
library(modelsummary)
modelsummary(
list(
"Prosperity" = twfe_log_prosperity,
"People" = twfe_log_people,
"Planet" = twfe_log_planet,
"Partnership" = twfe_log_partnership,
"Peace" = twfe_log_peace
),
stars = TRUE,
output = "markdown"
)| Prosperity | People | Planet | Partnership | Peace | |
|---|---|---|---|---|---|
| ln_innovation | -0.140 | 0.074*** | 0.285*** | -0.069* | 0.038* |
| (0.766) | (0.016) | (0.023) | (0.035) | (0.019) | |
| inflation | -0.015 | 0.001 | 0.001 | 0.001 | -0.000 |
| (0.019) | (0.000) | (0.001) | (0.001) | (0.000) | |
| pop_growth | -0.683* | 0.014* | 0.028** | 0.019 | -0.020* |
| (0.318) | (0.007) | (0.010) | (0.014) | (0.008) | |
| urban_growth | -0.153 | -0.007 | -0.020* | -0.021+ | 0.026*** |
| (0.280) | (0.006) | (0.008) | (0.013) | (0.007) | |
| Num.Obs. | 220 | 220 | 220 | 220 | 220 |
| R2 | 0.105 | 0.134 | 0.473 | 0.062 | 0.110 |
| R2 Adj. | -0.054 | -0.019 | 0.380 | -0.105 | -0.048 |
| AIC | 844.4 | -859.0 | -695.1 | -519.7 | -783.2 |
| BIC | 861.4 | -842.0 | -678.1 | -502.8 | -766.3 |
| RMSE | 1.61 | 0.03 | 0.05 | 0.07 | 0.04 |
|
|||||
modelsummary(
list(
"Prosperity" = cre_log_prosperity,
"People" = cre_log_people,
"Planet" = cre_log_planet,
"Partnership" = cre_log_partnership,
"Peace" = cre_log_peace
),
stars = TRUE,
output = "markdown"
)| Prosperity | People | Planet | Partnership | Peace | |
|---|---|---|---|---|---|
| (Intercept) | 11.313* | 3.374*** | -4.073+ | -0.645 | 3.136*** |
| (4.869) | (0.641) | (2.272) | (2.002) | (0.473) | |
| ln_innovation | -0.527 | 0.068*** | 0.040 | 0.027 | 0.032* |
| (0.960) | (0.012) | (0.027) | (0.032) | (0.014) | |
| inflation | 0.040 | 0.001+ | -0.001 | 0.004*** | -0.000 |
| (0.030) | (0.000) | (0.001) | (0.001) | (0.000) | |
| pop_growth | -1.320* | 0.014* | 0.019 | 0.011 | -0.020** |
| (0.514) | (0.006) | (0.015) | (0.017) | (0.008) | |
| urban_growth | 0.249 | -0.007 | -0.016 | -0.004 | 0.025*** |
| (0.450) | (0.006) | (0.013) | (0.015) | (0.007) | |
| emerging | -0.880 | -0.150 | -0.498 | 0.036 | -0.342*** |
| (0.735) | (0.097) | (0.343) | (0.302) | (0.071) | |
| mean_ln_innovation | -1.843 | 0.218 | 1.306* | 1.019* | 0.243* |
| (1.450) | (0.144) | (0.508) | (0.448) | (0.107) | |
| mean_inflation | 0.001 | 0.001 | -0.013 | -0.025 | -0.009* |
| (0.051) | (0.005) | (0.019) | (0.017) | (0.004) | |
| mean_pop_growth | -0.146 | -0.099 | 0.411 | -0.737+ | -0.161+ |
| (1.047) | (0.120) | (0.426) | (0.375) | (0.089) | |
| mean_urban_growth | 1.358 | 0.144 | 0.041 | 0.783* | 0.199* |
| (0.935) | (0.108) | (0.383) | (0.337) | (0.080) | |
| Num.Obs. | 220 | 220 | 220 | 220 | 220 |
| R2 | 0.164 | 0.224 | 0.138 | 0.134 | 0.350 |
| R2 Adj. | 0.128 | 0.191 | 0.101 | 0.097 | 0.323 |
| AIC | 1102.2 | -828.9 | -462.2 | -397.2 | -748.7 |
| BIC | 1139.5 | -791.6 | -424.8 | -359.8 | -711.4 |
| RMSE | 2.82 | 0.03 | 0.08 | 0.09 | 0.04 |
|
|||||
modelsummary(
list(
"Prosperity" = gmm_log_prosperity,
"People" = gmm_log_people,
"Planet" = gmm_log_planet,
"Partnership" = gmm_log_partnership,
"Peace" = gmm_log_peace
),
stars = TRUE,
output = "markdown"
)## Warning: `modelsummary could not extract goodness-of-fit statistics from a model
## of class "pgmm". The package tried a sequence of 2 helper functions:
##
## performance::model_performance(model)
## broom::glance(model)
##
## One of these functions must return a one-row `data.frame`. The `modelsummary` website explains how to summarize unsupported models or add support for new models yourself:
##
## https://modelsummary.com/vignettes/modelsummary.html
## Warning: `modelsummary could not extract goodness-of-fit statistics from a model
## of class "pgmm". The package tried a sequence of 2 helper functions:
##
## performance::model_performance(model)
## broom::glance(model)
##
## One of these functions must return a one-row `data.frame`. The `modelsummary` website explains how to summarize unsupported models or add support for new models yourself:
##
## https://modelsummary.com/vignettes/modelsummary.html
## Warning: `modelsummary could not extract goodness-of-fit statistics from a model
## of class "pgmm". The package tried a sequence of 2 helper functions:
##
## performance::model_performance(model)
## broom::glance(model)
##
## One of these functions must return a one-row `data.frame`. The `modelsummary` website explains how to summarize unsupported models or add support for new models yourself:
##
## https://modelsummary.com/vignettes/modelsummary.html
## Warning: `modelsummary could not extract goodness-of-fit statistics from a model
## of class "pgmm". The package tried a sequence of 2 helper functions:
##
## performance::model_performance(model)
## broom::glance(model)
##
## One of these functions must return a one-row `data.frame`. The `modelsummary` website explains how to summarize unsupported models or add support for new models yourself:
##
## https://modelsummary.com/vignettes/modelsummary.html
## Warning: `modelsummary could not extract goodness-of-fit statistics from a model
## of class "pgmm". The package tried a sequence of 2 helper functions:
##
## performance::model_performance(model)
## broom::glance(model)
##
## One of these functions must return a one-row `data.frame`. The `modelsummary` website explains how to summarize unsupported models or add support for new models yourself:
##
## https://modelsummary.com/vignettes/modelsummary.html| Prosperity | People | Planet | Partnership | Peace | |
|---|---|---|---|---|---|
| lag(prosperity, 1) | -0.078** | ||||
| (0.025) | |||||
| ln_innovation | 0.312*** | -0.019*** | 0.001 | 0.084** | -0.006 |
| (0.060) | (0.004) | (0.005) | (0.027) | (0.009) | |
| inflation | 0.071*** | 0.000*** | 0.000 | 0.001** | -0.000* |
| (0.005) | (0.000) | (0.000) | (0.000) | (0.000) | |
| pop_growth | -2.704*** | -0.001 | -0.016** | -0.044*** | 0.008*** |
| (0.535) | (0.002) | (0.006) | (0.009) | (0.002) | |
| urban_growth | 1.989*** | 0.004+ | 0.014* | 0.036*** | -0.009** |
| (0.490) | (0.002) | (0.006) | (0.009) | (0.003) | |
| lag(ln_people, 1) | 1.016*** | ||||
| (0.004) | |||||
| lag(ln_planet, 1) | 0.990*** | ||||
| (0.010) | |||||
| lag(ln_partnership, 1) | 0.909*** | ||||
| (0.028) | |||||
| lag(ln_peace, 1) | 1.007*** | ||||
| (0.009) | |||||
|
|||||