library(wooldridge)library(rmarkdown)data(murder)paged_table(murder)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(wooldridge)library(plm)library yardımı ile plm komutunu indiriyoruz .
data("airfare")library(rmarkdown)
paged_table (airfare)wooldridge airfare veri setini kullanıyoruz . pagedtable ile airfare ait tabloyu indirmiş oluruz
aysee <-pdata.frame(airfare ,index = c("id" , "year"))Bu komut sayesinde kullanıcı numarası ve zaman indexlerini verisetine tanıtabildik . Artık bu verisetini kullanarak plm komutunu kullanabiliriz . plm paketinin içinde bulunan pdim komutu sayesinde verisetinin balansını kontrol edebilir , kaç kişi için toplam kaç yıl veri toplandığını görebiliriz . n burada 1149 kişiden , T ; 4 yıl boyunca toplam 4596 tane gözlem toplanıldığını gösterir .
pdim (aysee)## Balanced Panel: n = 1149, T = 4, N = 4596pvar(aysee)## no time variation: id dist ldist ldistsq
## no individual variation: year y98 y99 y00plm paketinin içindeki pvar komutu zamanla değişmeyen ve bireye göre değişmeyen değişkenleri göstermektedir.
gungor <- plm(lpassen ~ fare + dist + passen +bmktshr + I (ldist^2) + year , data = aysee ,model ="pooling")gungor1 <- plm(lpassen ~ fare + dist + passen +bmktshr + I (ldist^2) + year , data = aysee ,model ="random")gungor2 <- plm(lpassen ~ fare + dist + passen +bmktshr + I (ldist^2) + year , data = aysee ,model ="within")library(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(gungor, gungor1, gungor2, type = "text", column.labels = c("OLS","RE","FE"))##
## ===============================================================================
## Dependent variable:
## ------------------------------------------------------------------
## lpassen
## OLS RE FE
## (1) (2) (3)
## -------------------------------------------------------------------------------
## fare -0.001*** -0.003*** -0.004***
## (0.0001) (0.0001) (0.0001)
##
## dist 0.0001** 0.0003***
## (0.00004) (0.0001)
##
## passen 0.001*** 0.001*** 0.001***
## (0.00001) (0.00001) (0.00002)
##
## bmktshr 0.223*** 0.200*** 0.161***
## (0.047) (0.038) (0.040)
##
## I(ldist2) 0.001 -0.004
## (0.003) (0.006)
##
## year1998 0.004 0.010 0.015**
## (0.021) (0.006) (0.006)
##
## year1999 0.018 0.034*** 0.051***
## (0.021) (0.006) (0.006)
##
## year2000 0.041* 0.079*** 0.110***
## (0.021) (0.007) (0.007)
##
## Constant 5.370*** 5.858***
## (0.110) (0.196)
##
## -------------------------------------------------------------------------------
## Observations 4,596 4,596 4,596
## R2 0.677 0.515 0.450
## Adjusted R2 0.676 0.514 0.266
## F Statistic 1,199.361*** (df = 8; 4587) 4,875.385*** 469.900*** (df = 6; 3441)
## ===============================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01phtest( gungor2, gungor1)##
## Hausman Test
##
## data: lpassen ~ fare + dist + passen + bmktshr + I(ldist^2) + year
## chisq = 253.23, df = 6, p-value < 2.2e-16
## alternative hypothesis: one model is inconsistent