library(wooldridge)
data(murder)library(rmarkdown)
paged_table(murder)I. Cinayet mahkumlarının geçmişteki infazları caydırıcı bir etkiye sahipse beta 1’in işareti ne olur? beta 2’nin sahip olması gereken işaret hakkında ne düşünüyorsunuz?
I. Bir eyalette infaz artıyorsa cinayetlerin azalmasını bekleriz bu yüzden beta 1’in işaretinin (-) olmasını bekleriz.
summary(lm(mrdrte ~ exec + unem + d90 + d93 , data = murder))##
## Call:
## lm(formula = mrdrte ~ exec + unem + d90 + d93, data = murder)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.130 -3.119 -1.211 1.379 67.810
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.8644 3.0695 -0.607 0.54452
## exec 0.1628 0.1939 0.839 0.40268
## unem 1.3908 0.4509 3.085 0.00243 **
## d90 2.6753 1.8169 1.472 0.14302
## d93 1.6073 1.7748 0.906 0.36659
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.955 on 148 degrees of freedom
## Multiple R-squared: 0.07609, Adjusted R-squared: 0.05112
## F-statistic: 3.047 on 4 and 148 DF, p-value: 0.01897library(plm)
murderpd <- pdata.frame(murder, index =c("state", "year"))
summary(murderpd)## id state year mrdrte exec
## Min. : 1 AK : 3 87:51 Min. : 0.800 Min. : 0.000
## 1st Qu.:13 AL : 3 90:51 1st Qu.: 3.900 1st Qu.: 0.000
## Median :26 AR : 3 93:51 Median : 6.400 Median : 0.000
## Mean :26 AZ : 3 Mean : 8.071 Mean : 1.229
## 3rd Qu.:39 CA : 3 3rd Qu.:10.200 3rd Qu.: 1.000
## Max. :51 CO : 3 Max. :78.500 Max. :34.000
## (Other):135
## unem d90 d93 cmrdrte
## Min. : 2.200 Min. :0.0000 Min. :0.0000 Min. :-2.6000
## 1st Qu.: 4.900 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:-0.4000
## Median : 5.800 Median :0.0000 Median :0.0000 Median : 0.3000
## Mean : 5.973 Mean :0.3333 Mean :0.3333 Mean : 0.8422
## 3rd Qu.: 7.000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.: 1.3000
## Max. :12.000 Max. :1.0000 Max. :1.0000 Max. :41.6000
## NA's :51
## cexec cunem cexec_1 cunem_1
## Min. :-11.0000 Min. :-5.80000 Min. :-11.0000 Min. :-5.8000
## 1st Qu.: 0.0000 1st Qu.:-1.07500 1st Qu.: 0.0000 1st Qu.:-1.9500
## Median : 0.0000 Median : 0.30000 Median : 0.0000 Median :-1.0000
## Mean : 0.1863 Mean : 0.00588 Mean : -0.2745 Mean :-0.8863
## 3rd Qu.: 0.0000 3rd Qu.: 1.00000 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## Max. : 23.0000 Max. : 3.60000 Max. : 5.0000 Max. : 3.1000
## NA's :51 NA's :51 NA's :102 NA's :102coklu <- plm(mrdrte ~ exec + unem + d90 + d93 , data = murder, model = "pooling")## Warning in pdata.frame(data, index): duplicate couples (id-time) in resulting pdata.frame
## to find out which, use, e.g., table(index(your_pdataframe), useNA = "ifany")summary(coklu)## Pooling Model
##
## Call:
## plm(formula = mrdrte ~ exec + unem + d90 + d93, data = murder,
## model = "pooling")
##
## Unbalanced Panel: n = 51, T = 3-3, N = 153
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -9.1301 -3.1194 -1.2107 1.3794 67.8099
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## (Intercept) -1.86439 3.06952 -0.6074 0.544523
## exec 0.16275 0.19393 0.8392 0.402685
## unem 1.39079 0.45087 3.0847 0.002432 **
## d90 2.67533 1.81693 1.4724 0.143024
## d93 1.60732 1.77477 0.9056 0.366594
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 12845
## Residual Sum of Squares: 11868
## R-Squared: 0.076089
## Adj. R-Squared: 0.051119
## F-statistic: 3.04715 on 4 and 148 DF, p-value: 0.018975Etkileşim terimlerinin çoğu anlamsız ve caydırıcı bir etki görünmüyor. En büyük katsayı 1990 almıştır, son üç yıldaki toplam infazdan ve yıllık infaz oranından fazladır.
library(plm)
murderpd <- pdata.frame(murder, index =c("state", "year"))
summary(murderpd)## id state year mrdrte exec
## Min. : 1 AK : 3 87:51 Min. : 0.800 Min. : 0.000
## 1st Qu.:13 AL : 3 90:51 1st Qu.: 3.900 1st Qu.: 0.000
## Median :26 AR : 3 93:51 Median : 6.400 Median : 0.000
## Mean :26 AZ : 3 Mean : 8.071 Mean : 1.229
## 3rd Qu.:39 CA : 3 3rd Qu.:10.200 3rd Qu.: 1.000
## Max. :51 CO : 3 Max. :78.500 Max. :34.000
## (Other):135
## unem d90 d93 cmrdrte
## Min. : 2.200 Min. :0.0000 Min. :0.0000 Min. :-2.6000
## 1st Qu.: 4.900 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:-0.4000
## Median : 5.800 Median :0.0000 Median :0.0000 Median : 0.3000
## Mean : 5.973 Mean :0.3333 Mean :0.3333 Mean : 0.8422
## 3rd Qu.: 7.000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.: 1.3000
## Max. :12.000 Max. :1.0000 Max. :1.0000 Max. :41.6000
## NA's :51
## cexec cunem cexec_1 cunem_1
## Min. :-11.0000 Min. :-5.80000 Min. :-11.0000 Min. :-5.8000
## 1st Qu.: 0.0000 1st Qu.:-1.07500 1st Qu.: 0.0000 1st Qu.:-1.9500
## Median : 0.0000 Median : 0.30000 Median : 0.0000 Median :-1.0000
## Mean : 0.1863 Mean : 0.00588 Mean : -0.2745 Mean :-0.8863
## 3rd Qu.: 0.0000 3rd Qu.: 1.00000 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## Max. : 23.0000 Max. : 3.60000 Max. : 5.0000 Max. : 3.1000
## NA's :51 NA's :51 NA's :102 NA's :102pdim(murderpd)## Balanced Panel: n = 51, T = 3, N = 15351 kişiden 3 yıl boyunca toplam 153 tane gözlem toplanmıştır.
withinmodel <- plm(mrdrte ~ exec + unem + d90 + d93 + factor(year)*exec + year , data = murder, model = "within")## Warning in pdata.frame(data, index): duplicate couples (id-time) in resulting pdata.frame
## to find out which, use, e.g., table(index(your_pdataframe), useNA = "ifany")summary(withinmodel)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = mrdrte ~ exec + unem + d90 + d93 + factor(year) *
## exec + year, data = murder, model = "within")
##
## Unbalanced Panel: n = 51, T = 3-3, N = 153
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -26.693360 -0.684805 -0.056137 0.687376 13.402922
##
## Coefficients: (3 dropped because of singularities)
## Estimate Std. Error t-value Pr(>|t|)
## exec -0.1660867 0.2768209 -0.6000 0.54993
## unem 0.2282087 0.3034049 0.7522 0.45380
## d90 1.5477421 0.7995912 1.9357 0.05585 .
## d93 1.7016588 0.7430442 2.2901 0.02420 *
## exec:factor(year)90 0.0075527 0.3136208 0.0241 0.98084
## exec:factor(year)93 0.0266765 0.1896223 0.1407 0.88842
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 1311.5
## Residual Sum of Squares: 1215
## R-Squared: 0.073558
## Adj. R-Squared: -0.46687
## F-statistic: 1.27037 on 6 and 96 DF, p-value: 0.278261990 yılı istatiksel olarak anlamsızdır. 1993 yılının 0,01 anlamlı olduğunu görüyoruz.
randommodel <- plm(mrdrte ~ exec + unem + d90 + d93 + I(cexec^2) + year , data = murderpd, model = "random")
summary(randommodel)## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = mrdrte ~ exec + unem + d90 + d93 + I(cexec^2) +
## year, data = murderpd, model = "random")
##
## Balanced Panel: n = 51, T = 2, N = 102
##
## Effects:
## var std.dev share
## idiosyncratic 0.5854 0.7651 0.005
## individual 106.1862 10.3047 0.995
## theta: 0.9476
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -1.36973 -0.46929 -0.12669 0.32637 3.83832
##
## Coefficients: (2 dropped because of singularities)
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) 8.9214366 1.7891607 4.9864 6.152e-07 ***
## exec 0.0059700 0.1265524 0.0472 0.96237
## unem -0.0188165 0.1609622 -0.1169 0.90694
## d90 -0.3520064 0.2135355 -1.6485 0.09926 .
## I(cexec^2) -0.0068039 0.0076242 -0.8924 0.37217
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 64.721
## Residual Sum of Squares: 59.026
## R-Squared: 0.087985
## Adj. R-Squared: 0.050376
## Chisq: 9.35789 on 4 DF, p-value: 0.052751library(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=stargazerstargazer(murderpd, withinmodel, randommodel, type = "text", column.labels = c("OLS","RE","FE"))##
## ===========================================
## Statistic N Mean St. Dev. Min Max
## -------------------------------------------
## id 153 26.000 14.768 1 51
## mrdrte 153 8.071 9.193 0.800 78.500
## exec 153 1.229 3.791 0 34
## unem 153 5.973 1.681 2.200 12.000
## d90 153 0.333 0.473 0 1
## d93 153 0.333 0.473 0 1
## cmrdrte 102 0.842 4.290 -2.600 41.600
## cexec 102 0.186 2.951 -11 23
## cunem 102 0.006 1.658 -5.800 3.600
## cexec_1 51 -0.275 2.192 -11 5
## cunem_1 51 -0.886 1.734 -5.800 3.100
## -------------------------------------------
##
## ================================================
## Dependent variable:
## ----------------------------
## mrdrte
## OLS RE
## (1) (2)
## ------------------------------------------------
## exec -0.166 0.006
## (0.277) (0.127)
##
## unem 0.228 -0.019
## (0.303) (0.161)
##
## d90 1.548* -0.352*
## (0.800) (0.214)
##
## d93 1.702**
## (0.743)
##
## exec:factor(year)90 0.008
## (0.314)
##
## exec:factor(year)93 0.027
## (0.190)
##
## I(cexec2) -0.007
## (0.008)
##
## Constant 8.921***
## (1.789)
##
## ------------------------------------------------
## Observations 153 102
## R2 0.074 0.088
## Adjusted R2 -0.467 0.050
## F Statistic 1.270 (df = 6; 96) 9.358*
## ================================================
## Note: *p<0.1; **p<0.05; ***p<0.01library(plm)
data("airfare")
library(rmarkdown)
paged_table(airfare)airfaremodel <- pdata.frame(airfare, index = c("id","year" ) )
summary(airfaremodel)## year id dist passen fare
## 1997:1149 1 : 4 Min. : 95.0 Min. : 2.0 Min. : 37.0
## 1998:1149 2 : 4 1st Qu.: 505.0 1st Qu.: 215.0 1st Qu.:123.0
## 1999:1149 3 : 4 Median : 861.0 Median : 357.0 Median :168.0
## 2000:1149 4 : 4 Mean : 989.7 Mean : 636.8 Mean :178.8
## 5 : 4 3rd Qu.:1304.0 3rd Qu.: 717.0 3rd Qu.:225.0
## 6 : 4 Max. :2724.0 Max. :8497.0 Max. :522.0
## (Other):4572
## bmktshr ldist y98 y99 y00
## Min. :0.1605 Min. :4.554 Min. :0.00 Min. :0.00 Min. :0.00
## 1st Qu.:0.4650 1st Qu.:6.225 1st Qu.:0.00 1st Qu.:0.00 1st Qu.:0.00
## Median :0.6039 Median :6.758 Median :0.00 Median :0.00 Median :0.00
## Mean :0.6101 Mean :6.696 Mean :0.25 Mean :0.25 Mean :0.25
## 3rd Qu.:0.7531 3rd Qu.:7.173 3rd Qu.:0.25 3rd Qu.:0.25 3rd Qu.:0.25
## Max. :1.0000 Max. :7.910 Max. :1.00 Max. :1.00 Max. :1.00
##
## lfare ldistsq concen lpassen
## Min. :3.611 Min. :20.74 Min. :0.1605 Min. :0.6931
## 1st Qu.:4.812 1st Qu.:38.75 1st Qu.:0.4650 1st Qu.:5.3706
## Median :5.124 Median :45.67 Median :0.6039 Median :5.8777
## Mean :5.096 Mean :45.28 Mean :0.6101 Mean :6.0170
## 3rd Qu.:5.416 3rd Qu.:51.45 3rd Qu.:0.7531 3rd Qu.:6.5751
## Max. :6.258 Max. :62.57 Max. :1.0000 Max. :9.0475
## indexdata <- pdata.frame(airfare, index = c("id", "year"))
pdim(indexdata)## Balanced Panel: n = 1149, T = 4, N = 4596pvar(indexdata)## no time variation: id dist ldist ldistsq
## no individual variation: year y98 y99 y00poolmodel <- plm(lpassen ~ fare + dist + passen + bmktshr + I(ldist^2) + year, data = airfaremodel , airfaremodel = "pooling" )
summary(poolmodel)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = lpassen ~ fare + dist + passen + bmktshr + I(ldist^2) +
## year, data = airfaremodel, airfaremodel = "pooling")
##
## Balanced Panel: n = 1149, T = 4, N = 4596
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -2.17315683 -0.03750978 0.00081035 0.03994864 1.98735732
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## fare -4.3260e-03 1.3776e-04 -31.4030 < 2.2e-16 ***
## passen 5.4114e-04 2.2967e-05 23.5623 < 2.2e-16 ***
## bmktshr 1.6054e-01 3.9765e-02 4.0374 5.523e-05 ***
## year1998 1.5337e-02 6.0027e-03 2.5550 0.01066 *
## year1999 5.0585e-02 6.1888e-03 8.1737 4.160e-16 ***
## year2000 1.0955e-01 6.6880e-03 16.3808 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 128.1
## Residual Sum of Squares: 70.409
## R-Squared: 0.45035
## Adj. R-Squared: 0.26602
## F-statistic: 469.9 on 6 and 3441 DF, p-value: < 2.22e-16withinmodel <- plm(lpassen ~ fare + dist + passen + bmktshr + I(ldist^2) + year, data = airfaremodel , airfaremodel = "within" )
summary(withinmodel)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = lpassen ~ fare + dist + passen + bmktshr + I(ldist^2) +
## year, data = airfaremodel, airfaremodel = "within")
##
## Balanced Panel: n = 1149, T = 4, N = 4596
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -2.17315683 -0.03750978 0.00081035 0.03994864 1.98735732
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## fare -4.3260e-03 1.3776e-04 -31.4030 < 2.2e-16 ***
## passen 5.4114e-04 2.2967e-05 23.5623 < 2.2e-16 ***
## bmktshr 1.6054e-01 3.9765e-02 4.0374 5.523e-05 ***
## year1998 1.5337e-02 6.0027e-03 2.5550 0.01066 *
## year1999 5.0585e-02 6.1888e-03 8.1737 4.160e-16 ***
## year2000 1.0955e-01 6.6880e-03 16.3808 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 128.1
## Residual Sum of Squares: 70.409
## R-Squared: 0.45035
## Adj. R-Squared: 0.26602
## F-statistic: 469.9 on 6 and 3441 DF, p-value: < 2.22e-16randommodel <- plm(lpassen ~ fare + dist + passen + bmktshr + I(ldist^2) + year, data = airfaremodel, airfaremodel = "random" )
summary(randommodel)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = lpassen ~ fare + dist + passen + bmktshr + I(ldist^2) +
## year, data = airfaremodel, airfaremodel = "random")
##
## Balanced Panel: n = 1149, T = 4, N = 4596
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -2.17315683 -0.03750978 0.00081035 0.03994864 1.98735732
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## fare -4.3260e-03 1.3776e-04 -31.4030 < 2.2e-16 ***
## passen 5.4114e-04 2.2967e-05 23.5623 < 2.2e-16 ***
## bmktshr 1.6054e-01 3.9765e-02 4.0374 5.523e-05 ***
## year1998 1.5337e-02 6.0027e-03 2.5550 0.01066 *
## year1999 5.0585e-02 6.1888e-03 8.1737 4.160e-16 ***
## year2000 1.0955e-01 6.6880e-03 16.3808 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 128.1
## Residual Sum of Squares: 70.409
## R-Squared: 0.45035
## Adj. R-Squared: 0.26602
## F-statistic: 469.9 on 6 and 3441 DF, p-value: < 2.22e-16library(stargazer)
stargazer(poolmodel, withinmodel, randommodel, type = "text", column.labels = c("OLS","RE","FE"))##
## ===========================================================
## Dependent variable:
## --------------------------------
## lpassen
## OLS RE FE
## (1) (2) (3)
## -----------------------------------------------------------
## fare -0.004*** -0.004*** -0.004***
## (0.0001) (0.0001) (0.0001)
##
## passen 0.001*** 0.001*** 0.001***
## (0.00002) (0.00002) (0.00002)
##
## bmktshr 0.161*** 0.161*** 0.161***
## (0.040) (0.040) (0.040)
##
## year1998 0.015** 0.015** 0.015**
## (0.006) (0.006) (0.006)
##
## year1999 0.051*** 0.051*** 0.051***
## (0.006) (0.006) (0.006)
##
## year2000 0.110*** 0.110*** 0.110***
## (0.007) (0.007) (0.007)
##
## -----------------------------------------------------------
## Observations 4,596 4,596 4,596
## R2 0.450 0.450 0.450
## Adjusted R2 0.266 0.266 0.266
## F Statistic (df = 6; 3441) 469.900*** 469.900*** 469.900***
## ===========================================================
## Note: *p<0.1; **p<0.05; ***p<0.01