The first problem
brumn_dat <- read.dta13('datasets/brumm.dta')
ivmodel <- ivreg(inflat ~ output+money|initial +school+inv+poprate+money, data = brumn_dat)
stargazer(ivmodel, type = 'text')
##
## ===============================================
## Dependent variable:
## ---------------------------
## inflat
## -----------------------------------------------
## output -1.394**
## (0.552)
##
## money 1.035***
## (0.010)
##
## Constant -1.094
## (1.858)
##
## -----------------------------------------------
## Observations 76
## R2 0.995
## Adjusted R2 0.995
## Residual Std. Error 4.344 (df = 73)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01
summary(ivmodel)
##
## Call:
## ivreg(formula = inflat ~ output + money | initial + school +
## inv + poprate + money, data = brumn_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.633 -1.487 0.204 1.937 9.498
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.093985 1.858208 -0.589 0.5579
## output -1.394200 0.551503 -2.528 0.0136 *
## money 1.035059 0.009773 105.914 <2e-16 ***
##
## Diagnostic tests:
## df1 df2 statistic p-value
## Weak instruments 4 70 4.642 0.00221 **
## Wu-Hausman 1 72 0.300 0.58551
## Sargan 3 NA 2.455 0.48344
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.344 on 73 degrees of freedom
## Multiple R-Squared: 0.9947, Adjusted R-squared: 0.9946
## Wald test: 6852 on 2 and 73 DF, p-value: < 2.2e-16
The second problem
income_democracy_dat <- read.dta13('datasets/income_democracy.dta')
income_democracy_dat.p <- pdata.frame(income_democracy_dat, index = c("code","year"))
# 检查面板数据是否平衡
pdim(income_democracy_dat.p)
## Unbalanced Panel: n = 195, T = 1-9, N = 1369
# 查找数据
t.a <- income_democracy_dat %>%
filter(country=="United States" & year == 2000) %>%
select('dem_ind')
kable(t.a, caption = "US and year = 2000")
t.a2 <- income_democracy_dat %>%
filter(country == "United States") %>%
summarise(mean_dem_ind = mean(dem_ind))
kable(t.a2, caption = "US average of dem_ind")
US average of dem_ind
| 0.9855556 |
# (b)
summary_income <- income_democracy_dat %>%
group_by(country) %>%
drop_na(dem_ind) %>%
summarise(mean_dem_ind = mean(dem_ind))
list_5.1 <- summary_income %>%
filter(mean_dem_ind>0.95) %>% arrange(mean_dem_ind) %>% head(n=5)
# <0.10
list_5.2 <- summary_income %>%
filter(mean_dem_ind<0.10) %>% arrange(mean_dem_ind) %>% head(n=5)
# [0.3, 0.7]
list_5.3 <- summary_income %>%
filter(dplyr::between(mean_dem_ind, 0.3, 0.7)) %>% arrange(mean_dem_ind) %>% head(n=5)
# five_country_res <- bind_cols(">0.95" = list_5.1, "<0.10" = list_5.2, "0.3<x<0.7"= list_5.3)
kable(list_5.1, caption = ">0.95 Five countries")
>0.95 Five countries
| Malta |
0.9523809 |
| Luxembourg |
0.9607407 |
| Germany, West |
0.9616667 |
| Japan |
0.9618518 |
| Costa Rica |
0.9788889 |
kable(list_5.2, caption = "<0.10 Five countries")
<0.10 Five countries
| Eritrea |
0.0000000 |
| Turkmenistan |
0.0000000 |
| Uzbekistan |
0.0000000 |
| Vietnam |
0.0000000 |
| Equatorial Guinea |
0.0277778 |
kable(list_5.3, caption = "[0.3,0.7] Five countries")
[0.3,0.7] Five countries
| Zimbabwe |
0.3095238 |
| Jordan |
0.3107407 |
| Nigeria |
0.3112500 |
| Bulgaria |
0.3259259 |
| Bosnia and Herzegovina |
0.3333333 |
3.回归分析
md3 <- lm(dem_ind ~ log_gdppc, data=income_democracy_dat)
md3.reg <- coeftest(md3, vcov. = vcovHC, type="HC0")
stargazer(md3.reg, type = 'text')
##
## =====================================
## Dependent variable:
## ---------------------------
##
## -------------------------------------
## log_gdppc 0.236***
## (0.007)
##
## Constant -1.355***
## (0.061)
##
## =====================================
## =====================================
## Note: *p<0.1; **p<0.05; ***p<0.01
# 3(b)
est <- 0.2 * md3$coefficients
kable(est, caption = "dem_ind result")
dem_ind result
| (Intercept) |
-0.2709656 |
| log_gdppc |
0.0471346 |
are <- 0.2*confint(md3, 'log_gdppc', level = 0.95)
kable(are, caption = '95% interval')
95% interval
| log_gdppc |
0.0437491 |
0.0505202 |
4.回归分析
固定效应模型(a)
regfeind <- plm(dem_ind ~ log_gdppc, data = income_democracy_dat, effect = "individual")
stargazer(regfeind, type = 'text')
##
## ========================================
## Dependent variable:
## ---------------------------
## dem_ind
## ----------------------------------------
## log_gdppc 0.084***
## (0.021)
##
## ----------------------------------------
## Observations 958
## R2 0.020
## Adjusted R2 -0.163
## F Statistic 16.202*** (df = 1; 807)
## ========================================
## Note: *p<0.1; **p<0.05; ***p<0.01
est4.a <- 0.2 * regfeind$coefficients
kable(est4.a, caption = "dem_ind result in 4(a)")
dem_ind result in 4(a)
| log_gdppc |
0.0167482 |
are4.a <- 0.2*confint(regfeind, 'log_gdppc', level = 0.95)
kable(are4.a, caption = '95% interval in 4(a)')
95% interval in 4(a)
| log_gdppc |
0.0085929 |
0.0249035 |
固定效应模型(b)
no_azerbaijan_dat <- income_democracy_dat %>%
filter(country != 'Azerbaijan')
dat <- dplyr::filter(income_democracy_dat, country != "Azerbaijan")
no_a_md <- plm(dem_ind ~ log_gdppc, data = dat, effect = "individual")
stargazer(no_a_md,type = 'text')
##
## ========================================
## Dependent variable:
## ---------------------------
## dem_ind
## ----------------------------------------
## log_gdppc 0.084***
## (0.021)
##
## ----------------------------------------
## Observations 957
## R2 0.020
## Adjusted R2 -0.161
## F Statistic 16.202*** (df = 1; 807)
## ========================================
## Note: *p<0.1; **p<0.05; ***p<0.01
est4.b <- 0.2 * no_a_md$coefficients
kable(est4.b, caption = "dem_ind result in 4(b)")
dem_ind result in 4(b)
| log_gdppc |
0.0167482 |
are4.b <- 0.2*confint(no_a_md, 'log_gdppc', level = 0.95)
kable(are4.b, caption = '95% interval in 4(b)')
95% interval in 4(b)
| log_gdppc |
0.0085929 |
0.0249035 |
固定效应模型(c)
two_fixed_md <- plm(dem_ind ~ log_gdppc, data = income_democracy_dat, effect = "time")
stargazer(two_fixed_md,type = 'text')
##
## ========================================
## Dependent variable:
## ---------------------------
## dem_ind
## ----------------------------------------
## log_gdppc 0.235***
## (0.008)
##
## ----------------------------------------
## Observations 958
## R2 0.449
## Adjusted R2 0.444
## F Statistic 772.299*** (df = 1; 948)
## ========================================
## Note: *p<0.1; **p<0.05; ***p<0.01
est4.c <- 0.2 * two_fixed_md$coefficients
kable(est4.c, caption = "dem_ind result in 4(c)")
dem_ind result in 4(c)
| log_gdppc |
0.0470213 |
are4.c <- 0.2*confint(two_fixed_md, 'log_gdppc', level = 0.95)
kable(are4.c, caption = '95% interval in 4(c)')
95% interval in 4(c)
| log_gdppc |
0.0437051 |
0.0503376 |
固定效应模型(d)
two_variable_md <- plm(dem_ind ~ log_gdppc+log_pop, data = income_democracy_dat, effect = "twoway")
stargazer(two_variable_md, type = 'text')
##
## ========================================
## Dependent variable:
## ---------------------------
## dem_ind
## ----------------------------------------
## log_gdppc 0.006
## (0.032)
##
## log_pop -0.093
## (0.066)
##
## ----------------------------------------
## Observations 895
## R2 0.003
## Adjusted R2 -0.209
## F Statistic 1.240 (df = 2; 737)
## ========================================
## Note: *p<0.1; **p<0.05; ***p<0.01
est4.d <- 0.2 * two_variable_md$coefficients
kable(est4.d, caption = "dem_ind result in 4(d)")
dem_ind result in 4(d)
| log_gdppc |
0.0012078 |
| log_pop |
-0.0185721 |
are4.d <- 0.2*confint(two_variable_md, 'log_gdppc', level = 0.95)
kable(are4.d, caption = '95% interval in 4(d)')
95% interval in 4(d)
| log_gdppc |
-0.0113089 |
0.0137245 |
固定效应模型(e)
two_variable_md2 <- plm(dem_ind ~ log_gdppc+educ, data = income_democracy_dat, effect = "twoway")
stargazer(two_variable_md2, type = 'text')
##
## ========================================
## Dependent variable:
## ---------------------------
## dem_ind
## ----------------------------------------
## log_gdppc 0.019
## (0.037)
##
## educ -0.007
## (0.017)
##
## ----------------------------------------
## Observations 688
## R2 0.001
## Adjusted R2 -0.180
## F Statistic 0.166 (df = 2; 582)
## ========================================
## Note: *p<0.1; **p<0.05; ***p<0.01
est4.e <- 0.2 * two_variable_md2$coefficients
kable(est4.e, caption = "dem_ind result in 4(e)")
dem_ind result in 4(e)
| log_gdppc |
0.0037077 |
| educ |
-0.0014747 |
are4.e <- 0.2*confint(two_variable_md2, 'log_gdppc', level = 0.95)
kable(are4.e, caption = '95% interval in 4(e)')
95% interval in 4(e)
| log_gdppc |
-0.0107379 |
0.0181533 |