setwd("C:/Users/marcogeovanni/Desktop/Modern Guide To Econometrics/Chapter 10")
library(plm)
## Warning: package 'plm' was built under R version 3.2.5
## Loading required package: Formula
## Warning: package 'Formula' was built under R version 3.2.3
library(readstata13)
## Warning: package 'readstata13' was built under R version 3.2.5
library(foreign)
Debtratio<-read.dta13("debtratio.dta")
summary(Debtratio)
## gvkey yeara mdr bdr
## Min. : 1003 Min. :1986 Min. :0.00000 Min. :0.00000
## 1st Qu.: 6158 1st Qu.:1990 1st Qu.:0.04998 1st Qu.:0.07913
## Median : 11142 Median :1994 Median :0.20733 Median :0.23511
## Mean : 18247 Mean :1994 Mean :0.26887 Mean :0.26083
## 3rd Qu.: 22913 3rd Qu.:1998 3rd Qu.:0.43419 3rd Qu.:0.39302
## Max. :233397 Max. :2001 Max. :0.94069 Max. :1.33643
##
## lagebit_ta lagmb lagdep_ta laglnta
## Min. :-1.587372 Min. : 0.2831 Min. :0.0006265 Min. :13.36
## 1st Qu.:-0.005718 1st Qu.: 0.8039 1st Qu.:0.0267735 1st Qu.:16.46
## Median : 0.076021 Median : 1.1373 Median :0.0413289 Median :17.81
## Mean : 0.025912 Mean : 1.6316 Mean :0.0481125 Mean :17.96
## 3rd Qu.: 0.130524 3rd Qu.: 1.8285 3rd Qu.:0.0604308 3rd Qu.:19.34
## Max. : 0.382066 Max. :14.0661 Max. :0.2644362 Max. :23.41
##
## lagfa_ta lagrd_dum lagrd_ta lagindmedian
## Min. :0.004627 Min. :0.0000 Min. :0.0000 Min. :0.01212
## 1st Qu.:0.131867 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.09110
## Median :0.256399 Median :0.0000 Median :0.0000 Median :0.19876
## Mean :0.310047 Mean :0.3905 Mean :0.0398 Mean :0.18967
## 3rd Qu.:0.446406 3rd Qu.:1.0000 3rd Qu.:0.0439 3rd Qu.:0.26590
## Max. :0.910084 Max. :1.0000 Max. :1.3699 Max. :0.68653
## NA's :1426
## lagrated
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.1679
## 3rd Qu.:0.0000
## Max. :1.0000
##
attach(Debtratio)
PanelData<- plm.data(Debtratio, indexes = c("gvkey", "yeara"))
#Modelo Pooling
POOLING<-plm(mdr~ lagebit_ta + lagmb + lagdep_ta + laglnta + lagfa_ta + lagrd_dum + lagrd_ta + lagindmedian + lagrated, data = PanelData, model = "pooling")
#Modelo Efectos Fijos
FixedEffectsWITHIN.<-plm(mdr~ lagebit_ta + lagmb + lagdep_ta + laglnta + lagfa_ta + lagrd_dum + lagrd_ta + lagindmedian + lagrated, data = PanelData, model = "within")
#Estimador Between
Between.<-plm(mdr~ lagebit_ta + lagmb + lagdep_ta + laglnta + lagfa_ta + lagrd_dum + lagrd_ta + lagindmedian + lagrated, data = PanelData, model = "between")
#Modelo efectos Aleatorios
RandomEffects<-plm(mdr~ lagebit_ta + lagmb + lagdep_ta + laglnta + lagfa_ta + lagrd_dum + lagrd_ta + lagindmedian + lagrated, data = PanelData, model = "random")
summary(POOLING)
## Oneway (individual) effect Pooling Model
##
## Call:
## plm(formula = mdr ~ lagebit_ta + lagmb + lagdep_ta + laglnta +
## lagfa_ta + lagrd_dum + lagrd_ta + lagindmedian + lagrated,
## data = PanelData, model = "pooling")
##
## Unbalanced Panel: n=5315, T=1-16, N=26336
##
## Residuals :
## Min. 1st Qu. Median 3rd Qu. Max.
## -0.6050 -0.1570 -0.0437 0.1290 0.8950
##
## Coefficients :
## Estimate Std. Error t-value Pr(>|t|)
## (Intercept) 0.17710386 0.01571315 11.2711 < 2.2e-16 ***
## lagebit_ta -0.21615173 0.00759335 -28.4659 < 2.2e-16 ***
## lagmb -0.04442370 0.00093290 -47.6188 < 2.2e-16 ***
## lagdep_ta -0.38445584 0.04719257 -8.1465 3.910e-16 ***
## laglnta 0.00518063 0.00087774 5.9022 3.631e-09 ***
## lagfa_ta 0.10152296 0.00742808 13.6675 < 2.2e-16 ***
## lagrd_dum 0.03410466 0.00297046 11.4813 < 2.2e-16 ***
## lagrd_ta -0.45061215 0.02000920 -22.5202 < 2.2e-16 ***
## lagindmedian 0.29113083 0.01328357 21.9166 < 2.2e-16 ***
## lagrated 0.06587650 0.00439359 14.9938 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 1591.3
## Residual Sum of Squares: 1203.2
## R-Squared: 0.2439
## Adj. R-Squared: 0.2438
## F-statistic: 943.55 on 9 and 26326 DF, p-value: < 2.22e-16
summary(FixedEffectsWITHIN.)
## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = mdr ~ lagebit_ta + lagmb + lagdep_ta + laglnta +
## lagfa_ta + lagrd_dum + lagrd_ta + lagindmedian + lagrated,
## data = PanelData, model = "within")
##
## Unbalanced Panel: n=5315, T=1-16, N=26336
##
## Residuals :
## Min. 1st Qu. Median 3rd Qu. Max.
## -0.6500 -0.0634 0.0000 0.0559 0.6290
##
## Coefficients :
## Estimate Std. Error t-value Pr(>|t|)
## lagebit_ta -0.14802345 0.00739869 -20.0067 < 2.2e-16 ***
## lagmb -0.01791343 0.00096937 -18.4794 < 2.2e-16 ***
## lagdep_ta 0.16472767 0.05363045 3.0715 0.002132 **
## laglnta 0.06493096 0.00186636 34.7901 < 2.2e-16 ***
## lagfa_ta 0.14336940 0.01193630 12.0112 < 2.2e-16 ***
## lagrd_dum 0.00302356 0.00560105 0.5398 0.589327
## lagrd_ta -0.15080700 0.02428128 -6.2108 5.369e-10 ***
## lagindmedian 0.45252539 0.01925019 23.5076 < 2.2e-16 ***
## lagrated 0.05063942 0.00458029 11.0559 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 439.01
## Residual Sum of Squares: 375.62
## R-Squared: 0.14441
## Adj. R-Squared: 0.11521
## F-statistic: 394.039 on 9 and 21012 DF, p-value: < 2.22e-16
summary(Between.)
## Oneway (individual) effect Between Model
##
## Call:
## plm(formula = mdr ~ lagebit_ta + lagmb + lagdep_ta + laglnta +
## lagfa_ta + lagrd_dum + lagrd_ta + lagindmedian + lagrated,
## data = PanelData, model = "between")
##
## Unbalanced Panel: n=5315, T=1-16, N=26336
##
## Residuals :
## Min. 1st Qu. Median 3rd Qu. Max.
## -0.4980 -0.1440 -0.0301 0.1150 0.8740
##
## Coefficients :
## Estimate Std. Error t-value Pr(>|t|)
## (Intercept) 0.1702732 0.0342263 4.9749 6.733e-07 ***
## lagebit_ta -0.1560919 0.0153322 -10.1806 < 2.2e-16 ***
## lagmb -0.0417573 0.0017905 -23.3221 < 2.2e-16 ***
## lagdep_ta -0.3745024 0.0981618 -3.8152 0.0001376 ***
## laglnta 0.0057147 0.0019251 2.9686 0.0030052 **
## lagfa_ta 0.1007511 0.0156185 6.4508 1.212e-10 ***
## lagrd_dum 0.0385828 0.0063941 6.0341 1.707e-09 ***
## lagrd_ta -0.4484953 0.0398929 -11.2425 < 2.2e-16 ***
## lagindmedian 0.3212609 0.0285320 11.2597 < 2.2e-16 ***
## lagrated 0.0897370 0.0112109 8.0044 1.463e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 295.7
## Residual Sum of Squares: 205.73
## R-Squared: 0.30428
## Adj. R-Squared: 0.30371
## F-statistic: 257.797 on 9 and 5305 DF, p-value: < 2.22e-16
summary(RandomEffects)
## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = mdr ~ lagebit_ta + lagmb + lagdep_ta + laglnta +
## lagfa_ta + lagrd_dum + lagrd_ta + lagindmedian + lagrated,
## data = PanelData, model = "random")
##
## Unbalanced Panel: n=5315, T=1-16, N=26336
##
## Effects:
## var std.dev share
## idiosyncratic 0.01788 0.13370 0.405
## individual 0.02623 0.16197 0.595
## theta :
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.3634 0.6537 0.7198 0.6898 0.7768 0.7979
##
## Residuals :
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.61300 -0.09370 -0.02880 -0.00294 0.07190 0.66200
##
## Coefficients :
## Estimate Std. Error t-value Pr(>|t|)
## (Intercept) -0.29740448 0.02185319 -13.6092 < 2.2e-16 ***
## lagebit_ta -0.15969017 0.00673671 -23.7045 < 2.2e-16 ***
## lagmb -0.02590238 0.00086346 -29.9983 < 2.2e-16 ***
## lagdep_ta -0.03071767 0.04760814 -0.6452 0.5188
## laglnta 0.02857482 0.00120073 23.7978 < 2.2e-16 ***
## lagfa_ta 0.11437344 0.00921718 12.4087 < 2.2e-16 ***
## lagrd_dum 0.02232125 0.00409629 5.4491 5.107e-08 ***
## lagrd_ta -0.27878557 0.02061268 -13.5250 < 2.2e-16 ***
## lagindmedian 0.39929940 0.01574078 25.3672 < 2.2e-16 ***
## lagrated 0.05746302 0.00430679 13.3424 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 617.49
## Residual Sum of Squares: 497.41
## R-Squared: 0.19551
## Adj. R-Squared: 0.19543
## F-statistic: 706.152 on 9 and 26326 DF, p-value: < 2.22e-16
plmtest(POOLING)
##
## Lagrange Multiplier Test - (Honda)
##
## data: mdr ~ lagebit_ta + lagmb + lagdep_ta + laglnta + lagfa_ta + lagrd_dum + ...
## normal = 2312.6, p-value < 2.2e-16
## alternative hypothesis: significant effects
pFtest(FixedEffectsWITHIN.,POOLING)
##
## F test for individual effects
##
## data: mdr ~ lagebit_ta + lagmb + lagdep_ta + laglnta + lagfa_ta + lagrd_dum + ...
## F = 8.7121, df1 = 5314, df2 = 21012, p-value < 2.2e-16
## alternative hypothesis: significant effects
#Hausman Test, sirve para ver qué tipos son los adecuados para la data
phtest(RandomEffects, FixedEffectsWITHIN.)
##
## Hausman Test
##
## data: mdr ~ lagebit_ta + lagmb + lagdep_ta + laglnta + lagfa_ta + lagrd_dum + ...
## chisq = 961.89, df = 9, p-value < 2.2e-16
## alternative hypothesis: one model is inconsistent