| title: “Ankit Output” |
| author: “Bishal Shr” |
| date: “2024-11-11” |
| output: |
| html_document:t |
| toc: TRUE |
| toc_depth: 2 |
library(AER)
library(plm)
library(systemfit)
library(readr)
library(stargazer)
library(lmtest)
library(dplyr)
library(psych)
library(writexl)
library(knitr)
library(readxl)setwd("/Users/bisshr/Desktop/Ankit Shrestha")
data <- data <- read_xlsx("dat_29_10_24.xlsx")
pdata<-pdata.frame(data, index=c("Year", "ABB"))
pdata$PIND<-as.double(pdata$PIND)#Descriptive Statistics
## [,1]
## mean_PIND 0.11400000
## mean_RDUAL 0.58000000
## mean_BSIZE 6.74500000
## mean_ATTEN 0.07500000
## mean_AGE 24.15000000
## mean_LEV 0.89294119
## mean_SIZE 25.52760000
## mean_growth 0.41966667
## sd_PIND 0.06927369
## sd_RDUAL 0.49479705
## sd_BSIZE 1.11182543
## sd_ATTEN 0.26405230
## sd_AGE 12.69184441
## sd_LEV 0.02714936
## sd_SIZE 0.73118768
## sd_growth 1.88834978
## min_PIND 0.00000000
## min_RDUAL 0.00000000
## min_BSIZE 5.00000000
## min_ATTEN 0.00000000
## min_AGE 12.69184441
## min_LEV 0.80500000
## min_SIZE 20.80000000
## min_growth -5.61000000
## max_PIND 0.20000000
## max_RDUAL 1.00000000
## max_BSIZE 10.00000000
## max_ATTEN 1.00000000
## max_AGE 12.69184441
## max_LEV 0.98100000
## max_SIZE 26.99000000
## max_growth 20.35000000
## median_PIND 0.14300000
## median_RDUAL 1.00000000
## median_BSIZE 7.00000000
## median_ATTEN 0.00000000
## median_AGE 12.69184441
## median_LEV 0.89800000
## median_SIZE 25.57000000
## median_growth 0.16500000ROA_pind_fixed<-plm(PIND~ROA+RDUAL+BSIZE+AGE+LEV+SIZE+Profit_Growth, model ="within", data=pdata)
ROA_pind_random<-plm(PIND~ROA+RDUAL+BSIZE+AGE+LEV+SIZE+Profit_Growth, model ="random", data=pdata)
##Table with PIND regressed on exogeneous variables (Fixed Vs Random)
stargazer(ROA_pind_fixed,ROA_pind_random, type="text", title="The Model Estimates ROA PIND", no.space = TRUE,
digits=4, align=TRUE, column.labels = c("Fixed Effect", "Random Effect"))##
## The Model Estimates ROA PIND
## ===================================================
## Dependent variable:
## -------------------------------------
## PIND
## Fixed Effect Random Effect
## (1) (2)
## ---------------------------------------------------
## ROA -0.0284 0.0728
## (0.0929) (0.0800)
## RDUAL -0.0077 -0.0053
## (0.0104) (0.0104)
## BSIZE -0.0080* -0.0103**
## (0.0047) (0.0045)
## AGE 0.00003 -0.0002
## (0.0005) (0.0005)
## LEV -0.5620** -0.6743***
## (0.2309) (0.2024)
## SIZE -0.0212 0.0063
## (0.0147) (0.0096)
## Profit_Growth 0.0022 0.0016
## (0.0025) (0.0025)
## Constant 0.6306**
## (0.2869)
## ---------------------------------------------------
## Observations 200 200
## R2 0.1003 0.1070
## Adjusted R2 0.0217 0.0745
## F Statistic 2.9161*** (df = 7; 183) 23.0121***
## ===================================================
## Note: *p<0.1; **p<0.05; ***p<0.01## Test of Ranodm Effect Model Vs Fixed Effect Model
test_ROA_pind<-phtest(ROA_pind_fixed, ROA_pind_random)
print(test_ROA_pind)##
## Hausman Test
##
## data: PIND ~ ROA + RDUAL + BSIZE + AGE + LEV + SIZE + Profit_Growth
## chisq = 19.972, df = 7, p-value = 0.00563
## alternative hypothesis: one model is inconsistentROA_pind_fixed$coefficients## ROA RDUAL BSIZE AGE LEV
## -2.839858e-02 -7.696380e-03 -7.967624e-03 2.643721e-05 -5.620078e-01
## SIZE Profit_Growth
## -2.120320e-02 2.155481e-03ROA_pind_random$coefficients## (Intercept) ROA RDUAL BSIZE AGE
## 0.6305566076 0.0727743070 -0.0053086574 -0.0102945281 -0.0001616473
## LEV SIZE Profit_Growth
## -0.6742653783 0.0062578686 0.0015857623##p-value ≥ 0.05: Indicates that the random effects model is appropriate,
##as it suggests that individual effects are not correlated with the regressors.
##Model Used for fitting estimated PIND (ePIND) value for main regression
summary(ROA_pind_random)## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = PIND ~ ROA + RDUAL + BSIZE + AGE + LEV + SIZE +
## Profit_Growth, data = pdata, model = "random")
##
## Balanced Panel: n = 10, T = 20, N = 200
##
## Effects:
## var std.dev share
## idiosyncratic 0.0039990 0.0632380 0.941
## individual 0.0002502 0.0158172 0.059
## theta: 0.3335
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -0.151558 -0.033504 0.028381 0.043401 0.113012
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) 0.63055661 0.28690168 2.1978 0.027962 *
## ROA 0.07277431 0.07999905 0.9097 0.362986
## RDUAL -0.00530866 0.01042303 -0.5093 0.610528
## BSIZE -0.01029453 0.00446058 -2.3079 0.021005 *
## AGE -0.00016165 0.00046514 -0.3475 0.728198
## LEV -0.67426538 0.20237587 -3.3317 0.000863 ***
## SIZE 0.00625787 0.00963850 0.6493 0.516172
## Profit_Growth 0.00158576 0.00247951 0.6395 0.522467
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 0.87632
## Residual Sum of Squares: 0.78253
## R-Squared: 0.10703
## Adj. R-Squared: 0.074471
## Chisq: 23.0121 on 7 DF, p-value: 0.0016964ePIND<-fitted.values(ROA_pind_random)ROA_lev_fixed<-plm(LEV~ROA+RDUAL+BSIZE+AGE+PIND+SIZE+Profit_Growth, model="within", data=pdata)
ROA_lev_random<-plm(LEV~ROA+RDUAL+BSIZE+AGE+PIND+SIZE+Profit_Growth, model="random", data=pdata)
stargazer(ROA_lev_fixed,ROA_lev_random, type="text", title="The Model Estimates ROA Leverage", no.space = TRUE,
digits=4, align=TRUE, column.labels = c("Fixed Effect", "Random Effect"))##
## The Model Estimates ROA Leverage
## ====================================================
## Dependent variable:
## --------------------------------------
## LEV
## Fixed Effect Random Effect
## (1) (2)
## ----------------------------------------------------
## ROA 0.0956*** 0.0085
## (0.0284) (0.0284)
## RDUAL 0.0039 0.0017
## (0.0033) (0.0039)
## BSIZE -0.0030** 0.0023
## (0.0015) (0.0016)
## AGE -0.0009*** -0.0009***
## (0.0001) (0.0002)
## PIND -0.0558** -0.0844***
## (0.0229) (0.0257)
## SIZE 0.0219*** 0.0032
## (0.0044) (0.0029)
## Profit_Growth -0.0005 0.0010
## (0.0008) (0.0009)
## Constant 0.8244***
## (0.0755)
## ----------------------------------------------------
## Observations 200 200
## R2 0.3389 0.2481
## Adjusted R2 0.2811 0.2207
## F Statistic 13.4044*** (df = 7; 183) 63.3639***
## ====================================================
## Note: *p<0.1; **p<0.05; ***p<0.01test_ROA_elev<-phtest(ROA_lev_fixed, ROA_lev_random)
print(test_ROA_elev)##
## Hausman Test
##
## data: LEV ~ ROA + RDUAL + BSIZE + AGE + PIND + SIZE + Profit_Growth
## chisq = 17.66, df = 7, p-value = 0.0136
## alternative hypothesis: one model is inconsistentROA_lev_fixed$coefficients## ROA RDUAL BSIZE AGE PIND
## 0.0956207615 0.0038959247 -0.0030429864 -0.0009415238 -0.0557826965
## SIZE Profit_Growth
## 0.0218573629 -0.0005242187ROA_lev_random$coefficients## (Intercept) ROA RDUAL BSIZE AGE
## 0.8243850362 0.0085410342 0.0016999560 0.0023026871 -0.0008636034
## PIND SIZE Profit_Growth
## -0.0843942360 0.0032093127 0.0009772258##p-value < 0.05: Indicates that the fixed effects model is appropriate,
##as it suggests that individual effects are not correlated with the regressors.
summary(ROA_lev_fixed)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = LEV ~ ROA + RDUAL + BSIZE + AGE + PIND + SIZE +
## Profit_Growth, data = pdata, model = "within")
##
## Balanced Panel: n = 10, T = 20, N = 200
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -0.06367495 -0.01132366 0.00045943 0.01038535 0.06934322
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## ROA 0.09562076 0.02839910 3.3670 0.0009261 ***
## RDUAL 0.00389592 0.00326888 1.1918 0.2348740
## BSIZE -0.00304299 0.00148322 -2.0516 0.0416318 *
## AGE -0.00094152 0.00013424 -7.0135 4.349e-11 ***
## PIND -0.05578270 0.02292115 -2.4337 0.0159072 *
## SIZE 0.02185736 0.00436586 5.0064 1.298e-06 ***
## Profit_Growth -0.00052422 0.00078943 -0.6640 0.5074948
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 0.10988
## Residual Sum of Squares: 0.072638
## R-Squared: 0.33895
## Adj. R-Squared: 0.28115
## F-statistic: 13.4044 on 7 and 183 DF, p-value: 6.3466e-14eLEV<-fitted.values(ROA_lev_fixed)
#cleaning the data with missing values
clean_pdata<-na.omit(pdata)reg1_ROA<-plm(ROA~RDUAL+BSIZE+MEET+ATTEN+AGE+eLEV+ePIND+SIZE+Profit_Growth, method="random", data=clean_pdata)
summary(reg1_ROA)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = ROA ~ RDUAL + BSIZE + MEET + ATTEN + AGE + eLEV +
## ePIND + SIZE + Profit_Growth, data = clean_pdata, method = "random")
##
## Balanced Panel: n = 10, T = 20, N = 200
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -0.0709749 -0.0137188 0.0034722 0.0171930 0.1549446
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## RDUAL -0.0273306 0.0051635 -5.2931 3.453e-07 ***
## BSIZE 0.0232808 0.0027349 8.5125 6.372e-15 ***
## MEET 0.0051705 0.0048377 1.0688 0.28658
## ATTEN 0.0016512 0.0087185 0.1894 0.85000
## AGE 0.0066913 0.0003755 17.8198 < 2.2e-16 ***
## eLEV 6.7778631 0.3555320 19.0640 < 2.2e-16 ***
## ePIND 0.3335610 0.1587884 2.1007 0.03706 *
## SIZE -0.1903178 0.0067164 -28.3364 < 2.2e-16 ***
## Profit_Growth 0.0056451 0.0011826 4.7736 3.719e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 0.92309
## Residual Sum of Squares: 0.15348
## R-Squared: 0.83373
## Adj. R-Squared: 0.81719
## F-statistic: 100.841 on 9 and 181 DF, p-value: < 2.22e-16reg1_ROA$coefficients## RDUAL BSIZE MEET ATTEN AGE
## -0.027330618 0.023280771 0.005170502 0.001651188 0.006691272
## eLEV ePIND SIZE Profit_Growth
## 6.777863070 0.333560982 -0.190317792 0.005645052#Wooldridge's test for serial correlation in FE panels: Alternative hypothesis: Serial Correlation
pwartest(reg1_ROA)##
## Wooldridge's test for serial correlation in FE panels
##
## data: reg1_ROA
## F = 4.3565, df1 = 1, df2 = 188, p-value = 0.03822
## alternative hypothesis: serial correlation#test for heteroskedasticity
#If the p-value is small (typically < 0.05), we reject the null hypothesis.
#This suggests that heteroskedasticity is present in the model, indicating that the variance of residuals is not constant.
bptest(reg1_ROA)##
## studentized Breusch-Pagan test
##
## data: reg1_ROA
## BP = 80.505, df = 9, p-value = 1.282e-13#Return on Equity
ROE_pind_fixed<-plm(PIND~ROE+RDUAL+BSIZE+AGE+LEV+SIZE+Profit_Growth, model ="within", data=pdata)
ROE_pind_random<-plm(PIND~ROE+RDUAL+BSIZE+AGE+LEV+SIZE+Profit_Growth, model ="random", data=pdata)
stargazer(ROE_pind_fixed,ROE_pind_random, type="text", title="The Model Estimates ROE PIND", no.space = TRUE,
digits=4, align=TRUE, column.labels = c("Fixed Effect", "Random Effect"))##
## The Model Estimates ROE PIND
## ===================================================
## Dependent variable:
## -------------------------------------
## PIND
## Fixed Effect Random Effect
## (1) (2)
## ---------------------------------------------------
## ROE -0.0069 -0.0085
## (0.0061) (0.0060)
## RDUAL -0.0092 -0.0073
## (0.0105) (0.0104)
## BSIZE -0.0083* -0.0098**
## (0.0046) (0.0044)
## AGE -0.0001 -0.0001
## (0.0005) (0.0005)
## LEV -0.5578** -0.6256***
## (0.2237) (0.2056)
## SIZE -0.0166 -0.0004
## (0.0106) (0.0084)
## Profit_Growth 0.0024 0.0024
## (0.0025) (0.0025)
## Constant 0.7559***
## (0.2661)
## ---------------------------------------------------
## Observations 200 200
## R2 0.1063 0.1083
## Adjusted R2 0.0281 0.0757
## F Statistic 3.1081*** (df = 7; 183) 23.3093***
## ===================================================
## Note: *p<0.1; **p<0.05; ***p<0.01test_ROE_pind<-phtest(ROE_pind_fixed, ROE_pind_random)
print(test_ROE_pind)##
## Hausman Test
##
## data: PIND ~ ROE + RDUAL + BSIZE + AGE + LEV + SIZE + Profit_Growth
## chisq = 7.5282, df = 7, p-value = 0.376
## alternative hypothesis: one model is inconsistentROE_pind_fixed$coefficients## ROE RDUAL BSIZE AGE LEV
## -0.0069251219 -0.0091967360 -0.0082628625 -0.0000602719 -0.5578459278
## SIZE Profit_Growth
## -0.0165774046 0.0023578188ROE_pind_random$coefficients## (Intercept) ROE RDUAL BSIZE AGE
## 0.7559065703 -0.0084640382 -0.0073455288 -0.0097568465 -0.0001263558
## LEV SIZE Profit_Growth
## -0.6255861454 -0.0003704199 0.0023812524##p-value ≥ 0.05: Indicates that the random effects model is appropriate,
summary(ROE_pind_random)## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = PIND ~ ROE + RDUAL + BSIZE + AGE + LEV + SIZE +
## Profit_Growth, data = pdata, model = "random")
##
## Balanced Panel: n = 10, T = 20, N = 200
##
## Effects:
## var std.dev share
## idiosyncratic 0.0039728 0.0630301 0.912
## individual 0.0003837 0.0195878 0.088
## theta: 0.4159
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -0.154475 -0.021008 0.028039 0.041924 0.102868
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) 0.75590657 0.26609220 2.8408 0.004500 **
## ROE -0.00846404 0.00602018 -1.4059 0.159741
## RDUAL -0.00734553 0.01043774 -0.7037 0.481590
## BSIZE -0.00975685 0.00444769 -2.1937 0.028258 *
## AGE -0.00012636 0.00045467 -0.2779 0.781082
## LEV -0.62558615 0.20557932 -3.0430 0.002342 **
## SIZE -0.00037042 0.00840695 -0.0441 0.964856
## Profit_Growth 0.00238125 0.00245466 0.9701 0.332000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 0.86173
## Residual Sum of Squares: 0.76844
## R-Squared: 0.10826
## Adj. R-Squared: 0.075748
## Chisq: 23.3093 on 7 DF, p-value: 0.0015055ePIND_ROE<-fitted.values(ROE_pind_random)ROE_lev_fixed<-plm(LEV~ROE+RDUAL+BSIZE+AGE+PIND+SIZE+Profit_Growth, model="within", data=pdata)
ROE_lev_random<-plm(LEV~ROE+RDUAL+BSIZE+AGE+PIND+SIZE+Profit_Growth, model="random", data=pdata)
stargazer(ROE_lev_fixed,ROE_lev_random, type="text", title="The Model Estimates ROE Leverage", no.space = TRUE,
digits=4, align=TRUE, column.labels = c("Fixed Effect", "Random Effect"))##
## The Model Estimates ROE Leverage
## ====================================================
## Dependent variable:
## --------------------------------------
## LEV
## Fixed Effect Random Effect
## (1) (2)
## ----------------------------------------------------
## ROE 0.0018 0.0037*
## (0.0020) (0.0022)
## RDUAL 0.0042 0.0027
## (0.0034) (0.0039)
## BSIZE -0.0022 0.0022
## (0.0015) (0.0016)
## AGE -0.0008*** -0.0008***
## (0.0001) (0.0002)
## PIND -0.0589** -0.0778***
## (0.0236) (0.0257)
## SIZE 0.0116*** 0.0026
## (0.0034) (0.0025)
## Profit_Growth -0.0001 0.0008
## (0.0008) (0.0009)
## Constant 0.8388***
## (0.0666)
## ----------------------------------------------------
## Observations 200 200
## R2 0.3013 0.2583
## Adjusted R2 0.2402 0.2312
## F Statistic 11.2720*** (df = 7; 183) 66.8474***
## ====================================================
## Note: *p<0.1; **p<0.05; ***p<0.01test_ROE_elev<-phtest(ROE_lev_fixed, ROE_lev_random)
print(test_ROE_elev)##
## Hausman Test
##
## data: LEV ~ ROE + RDUAL + BSIZE + AGE + PIND + SIZE + Profit_Growth
## chisq = 4.1981, df = 7, p-value = 0.7567
## alternative hypothesis: one model is inconsistentROE_lev_fixed$coefficients## ROE RDUAL BSIZE AGE PIND
## 1.828342e-03 4.249603e-03 -2.201905e-03 -8.333093e-04 -5.891207e-02
## SIZE Profit_Growth
## 1.162539e-02 -9.563791e-05ROE_lev_random$coefficients## (Intercept) ROE RDUAL BSIZE AGE
## 0.8388158710 0.0036750821 0.0026911063 0.0022364251 -0.0008235260
## PIND SIZE Profit_Growth
## -0.0778386370 0.0025528158 0.0007839072##p-value ≥ 0.05: Indicates that the random effects model is appropriate,
summary(ROE_lev_random)## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = LEV ~ ROE + RDUAL + BSIZE + AGE + PIND + SIZE +
## Profit_Growth, data = pdata, model = "random")
##
## Balanced Panel: n = 10, T = 20, N = 200
##
## Effects:
## var std.dev share
## idiosyncratic 0.0004196 0.0204830 1
## individual 0.0000000 0.0000000 0
## theta: 0
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -0.06324496 -0.01269380 -0.00045611 0.01492446 0.10318634
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) 0.83881587 0.06655248 12.6038 < 2.2e-16 ***
## ROE 0.00367508 0.00223209 1.6465 0.099666 .
## RDUAL 0.00269111 0.00390846 0.6885 0.491117
## BSIZE 0.00223643 0.00162608 1.3753 0.169024
## AGE -0.00082353 0.00015803 -5.2111 1.877e-07 ***
## PIND -0.07783864 0.02573916 -3.0241 0.002493 **
## SIZE 0.00255282 0.00252304 1.0118 0.311633
## Profit_Growth 0.00078391 0.00091383 0.8578 0.390988
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 0.14668
## Residual Sum of Squares: 0.1088
## R-Squared: 0.25825
## Adj. R-Squared: 0.23121
## Chisq: 66.8474 on 7 DF, p-value: 6.3905e-12eLEV_ROE<-fitted.values(ROE_lev_random)reg2_ROE<-plm(ROE~RDUAL+BSIZE+MEET+ATTEN+AGE+eLEV_ROE+ePIND_ROE+SIZE+Profit_Growth, method="random", data=clean_pdata)
summary(reg2_ROE)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = ROE ~ RDUAL + BSIZE + MEET + ATTEN + AGE + eLEV_ROE +
## ePIND_ROE + SIZE + Profit_Growth, data = clean_pdata, method = "random")
##
## Balanced Panel: n = 10, T = 20, N = 200
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -1.638396 -0.287843 0.049731 0.233679 5.864322
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## RDUAL -0.4787209 0.1018554 -4.7000 5.137e-06 ***
## BSIZE -0.3062847 0.0520635 -5.8829 1.911e-08 ***
## MEET 0.0642069 0.0974307 0.6590 0.51073
## ATTEN 0.2521517 0.1752689 1.4387 0.15197
## AGE 0.0472813 0.0078966 5.9875 1.121e-08 ***
## eLEV_ROE 54.4175880 8.1051374 6.7140 2.362e-10 ***
## ePIND_ROE -17.2460828 3.2087519 -5.3747 2.342e-07 ***
## SIZE -0.2095260 0.1059134 -1.9783 0.04941 *
## Profit_Growth 0.0458436 0.0255982 1.7909 0.07498 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 115.47
## Residual Sum of Squares: 64.083
## R-Squared: 0.44502
## Adj. R-Squared: 0.38983
## F-statistic: 16.1263 on 9 and 181 DF, p-value: < 2.22e-16reg2_ROE$coefficients## RDUAL BSIZE MEET ATTEN AGE
## -0.47872085 -0.30628473 0.06420689 0.25215166 0.04728133
## eLEV_ROE ePIND_ROE SIZE Profit_Growth
## 54.41758798 -17.24608282 -0.20952596 0.04584360#Wooldridge's test for serial correlation in FE panels: Alternative hypothesis: Serial Correlation
pwartest(reg2_ROE)##
## Wooldridge's test for serial correlation in FE panels
##
## data: reg2_ROE
## F = 1.338, df1 = 1, df2 = 188, p-value = 0.2489
## alternative hypothesis: serial correlation#test for heteroskedasticity
#If the p-value is small (typically < 0.05), we reject the null hypothesis.
#This suggests that heteroskedasticity is present in the model, indicating that the variance of residuals is not constant.
bptest(reg2_ROE)##
## studentized Breusch-Pagan test
##
## data: reg2_ROE
## BP = 91.855, df = 9, p-value = 6.906e-16ROCED_pind_fixed<-plm(PIND~ROCED+RDUAL+BSIZE+AGE+LEV+SIZE+Profit_Growth, model ="within", data=pdata)
ROCED_pind_random<-plm(PIND~ROCED+RDUAL+BSIZE+AGE+LEV+SIZE+Profit_Growth, model ="random", data=pdata)
stargazer(ROCED_pind_fixed,ROCED_pind_random, type="text", title="The Model Estimates ROCED PIND", no.space = TRUE,
digits=4, align=TRUE, column.labels = c("Fixed Effect", "Random Effect"))##
## The Model Estimates ROCED PIND
## ===================================================
## Dependent variable:
## -------------------------------------
## PIND
## Fixed Effect Random Effect
## (1) (2)
## ---------------------------------------------------
## ROCED -0.1313 -0.7198
## (0.6640) (0.6255)
## RDUAL -0.0076 -0.0055
## (0.0104) (0.0104)
## BSIZE -0.0082* -0.0095**
## (0.0046) (0.0045)
## AGE -0.000005 -0.00001
## (0.0005) (0.0005)
## LEV -0.5915** -0.7182***
## (0.2316) (0.2099)
## SIZE -0.0177 -0.0009
## (0.0108) (0.0084)
## Profit_Growth 0.0027 0.0055
## (0.0041) (0.0040)
## Constant 0.8544***
## (0.2676)
## ---------------------------------------------------
## Observations 200 200
## R2 0.1001 0.1056
## Adjusted R2 0.0214 0.0730
## F Statistic 2.9074*** (df = 7; 183) 22.6700***
## ===================================================
## Note: *p<0.1; **p<0.05; ***p<0.01test_ROCED_pind<-phtest(ROCED_pind_fixed, ROCED_pind_random)
print(test_ROCED_pind)##
## Hausman Test
##
## data: PIND ~ ROCED + RDUAL + BSIZE + AGE + LEV + SIZE + Profit_Growth
## chisq = 6.4981, df = 7, p-value = 0.4829
## alternative hypothesis: one model is inconsistentROCED_pind_fixed$coefficients## ROCED RDUAL BSIZE AGE LEV
## -1.312581e-01 -7.601621e-03 -8.192911e-03 -4.910575e-06 -5.914969e-01
## SIZE Profit_Growth
## -1.769759e-02 2.654911e-03ROCED_pind_random$coefficients## (Intercept) ROCED RDUAL BSIZE AGE
## 8.544181e-01 -7.197848e-01 -5.476951e-03 -9.459872e-03 -1.456132e-05
## LEV SIZE Profit_Growth
## -7.182478e-01 -8.782098e-04 5.511907e-03stargazer(ROCED_pind_fixed,ROCED_pind_random, type="text", title="ROCED FE RE PIND", no.space = TRUE,
digits=4, align=TRUE, column.labels = c("Fixed Effect", "Random Effect"))##
## ROCED FE RE PIND
## ===================================================
## Dependent variable:
## -------------------------------------
## PIND
## Fixed Effect Random Effect
## (1) (2)
## ---------------------------------------------------
## ROCED -0.1313 -0.7198
## (0.6640) (0.6255)
## RDUAL -0.0076 -0.0055
## (0.0104) (0.0104)
## BSIZE -0.0082* -0.0095**
## (0.0046) (0.0045)
## AGE -0.000005 -0.00001
## (0.0005) (0.0005)
## LEV -0.5915** -0.7182***
## (0.2316) (0.2099)
## SIZE -0.0177 -0.0009
## (0.0108) (0.0084)
## Profit_Growth 0.0027 0.0055
## (0.0041) (0.0040)
## Constant 0.8544***
## (0.2676)
## ---------------------------------------------------
## Observations 200 200
## R2 0.1001 0.1056
## Adjusted R2 0.0214 0.0730
## F Statistic 2.9074*** (df = 7; 183) 22.6700***
## ===================================================
## Note: *p<0.1; **p<0.05; ***p<0.01##p-value ≥ 0.05: Indicates that the random effects model is appropriate,
summary(ROCED_pind_random)## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = PIND ~ ROCED + RDUAL + BSIZE + AGE + LEV + SIZE +
## Profit_Growth, data = pdata, model = "random")
##
## Balanced Panel: n = 10, T = 20, N = 200
##
## Effects:
## var std.dev share
## idiosyncratic 0.0040002 0.0632474 0.915
## individual 0.0003693 0.0192180 0.085
## theta: 0.4073
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -0.157061 -0.034413 0.026720 0.042998 0.100158
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) 8.5442e-01 2.6764e-01 3.1924 0.0014111 **
## ROCED -7.1978e-01 6.2553e-01 -1.1507 0.2498647
## RDUAL -5.4770e-03 1.0358e-02 -0.5288 0.5969689
## BSIZE -9.4599e-03 4.4709e-03 -2.1159 0.0343572 *
## AGE -1.4561e-05 4.5780e-04 -0.0318 0.9746258
## LEV -7.1825e-01 2.0994e-01 -3.4211 0.0006236 ***
## SIZE -8.7821e-04 8.3599e-03 -0.1051 0.9163359
## Profit_Growth 5.5119e-03 3.9594e-03 1.3921 0.1638921
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 0.86317
## Residual Sum of Squares: 0.77202
## R-Squared: 0.1056
## Adj. R-Squared: 0.072996
## Chisq: 22.67 on 7 DF, p-value: 0.0019453ePIND_ROCED<-fitted.values(ROCED_pind_random)ROCED_lev_fixed<-plm(LEV~ROCED+RDUAL+BSIZE+AGE+PIND+SIZE+Profit_Growth, model="within", data=pdata)
ROCED_lev_random<-plm(LEV~ROCED+RDUAL+BSIZE+AGE+PIND+SIZE+Profit_Growth, model="random", data=pdata)
stargazer(ROCED_lev_fixed,ROCED_lev_random, type="text", title="The Model Estimates ROCED Lev", no.space = TRUE,
digits=4, align=TRUE, column.labels = c("Fixed Effect", "Random Effect"))##
## The Model Estimates ROCED Lev
## ====================================================
## Dependent variable:
## --------------------------------------
## LEV
## Fixed Effect Random Effect
## (1) (2)
## ----------------------------------------------------
## ROCED -0.7253*** -0.8574***
## (0.2013) (0.2067)
## RDUAL 0.0037 0.0010
## (0.0033) (0.0037)
## BSIZE -0.0018 0.0025
## (0.0015) (0.0016)
## AGE -0.0007*** -0.0007***
## (0.0001) (0.0002)
## PIND -0.0582** -0.0905***
## (0.0228) (0.0246)
## SIZE 0.0134*** 0.0012
## (0.0033) (0.0025)
## Profit_Growth 0.0036*** 0.0052***
## (0.0013) (0.0013)
## Constant 0.8855***
## (0.0653)
## ----------------------------------------------------
## Observations 200 200
## R2 0.3445 0.3096
## Adjusted R2 0.2872 0.2844
## F Statistic 13.7402*** (df = 7; 183) 86.1047***
## ====================================================
## Note: *p<0.1; **p<0.05; ***p<0.01test_ROCED_elev<-phtest(ROCED_lev_fixed, ROCED_lev_random)
print(test_ROCED_elev)##
## Hausman Test
##
## data: LEV ~ ROCED + RDUAL + BSIZE + AGE + PIND + SIZE + Profit_Growth
## chisq = 278.32, df = 7, p-value < 2.2e-16
## alternative hypothesis: one model is inconsistentROCED_lev_fixed$coefficients## ROCED RDUAL BSIZE AGE PIND
## -0.7253490677 0.0036522219 -0.0017674973 -0.0007329561 -0.0581979831
## SIZE Profit_Growth
## 0.0133971913 0.0036233129ROCED_lev_random$coefficients## (Intercept) ROCED RDUAL BSIZE AGE
## 0.8854645562 -0.8573648064 0.0010475261 0.0024548566 -0.0007163389
## PIND SIZE Profit_Growth
## -0.0905191089 0.0011592747 0.0052011897stargazer(ROCED_lev_fixed,ROCED_lev_random, type="text", title="ROCED FE RE LEV", no.space = TRUE,
digits=4, align=TRUE, column.labels = c("Fixed Effect", "Random Effect"))##
## ROCED FE RE LEV
## ====================================================
## Dependent variable:
## --------------------------------------
## LEV
## Fixed Effect Random Effect
## (1) (2)
## ----------------------------------------------------
## ROCED -0.7253*** -0.8574***
## (0.2013) (0.2067)
## RDUAL 0.0037 0.0010
## (0.0033) (0.0037)
## BSIZE -0.0018 0.0025
## (0.0015) (0.0016)
## AGE -0.0007*** -0.0007***
## (0.0001) (0.0002)
## PIND -0.0582** -0.0905***
## (0.0228) (0.0246)
## SIZE 0.0134*** 0.0012
## (0.0033) (0.0025)
## Profit_Growth 0.0036*** 0.0052***
## (0.0013) (0.0013)
## Constant 0.8855***
## (0.0653)
## ----------------------------------------------------
## Observations 200 200
## R2 0.3445 0.3096
## Adjusted R2 0.2872 0.2844
## F Statistic 13.7402*** (df = 7; 183) 86.1047***
## ====================================================
## Note: *p<0.1; **p<0.05; ***p<0.01##p-value ≥ 0.05: Indicates that the random effects model is appropriate,
summary(ROCED_lev_random)## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = LEV ~ ROCED + RDUAL + BSIZE + AGE + PIND + SIZE +
## Profit_Growth, data = pdata, model = "random")
##
## Balanced Panel: n = 10, T = 20, N = 200
##
## Effects:
## var std.dev share
## idiosyncratic 0.0003936 0.0198390 1
## individual 0.0000000 0.0000000 0
## theta: 0
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -0.06285661 -0.01296055 0.00023296 0.01216951 0.08607989
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) 0.88546456 0.06531451 13.5569 < 2.2e-16 ***
## ROCED -0.85736481 0.20674912 -4.1469 3.370e-05 ***
## RDUAL 0.00104753 0.00373610 0.2804 0.7791865
## BSIZE 0.00245486 0.00156825 1.5653 0.1175009
## AGE -0.00071634 0.00015499 -4.6219 3.803e-06 ***
## PIND -0.09051911 0.02464192 -3.6734 0.0002394 ***
## SIZE 0.00115927 0.00246174 0.4709 0.6376998
## Profit_Growth 0.00520119 0.00133309 3.9016 9.556e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 0.14668
## Residual Sum of Squares: 0.10127
## R-Squared: 0.30961
## Adj. R-Squared: 0.28444
## Chisq: 86.1047 on 7 DF, p-value: 7.7871e-16eLEV_ROCED<-fitted.values(ROCED_lev_random)reg3_ROCED<-plm(ROCED~RDUAL+BSIZE+MEET+ATTEN+AGE+eLEV_ROCED+ePIND_ROCED+SIZE+Profit_Growth, method="random", data=clean_pdata)
summary(reg3_ROCED)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = ROCED ~ RDUAL + BSIZE + MEET + ATTEN + AGE + eLEV_ROCED +
## ePIND_ROCED + SIZE + Profit_Growth, data = clean_pdata, method = "random")
##
## Balanced Panel: n = 10, T = 20, N = 200
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -0.02236912 -0.00283809 -0.00047088 0.00259795 0.01732853
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## RDUAL 5.9767e-04 8.2397e-04 0.7253 0.4691756
## BSIZE 1.6037e-03 4.2712e-04 3.7547 0.0002337 ***
## MEET 5.3075e-04 7.8113e-04 0.6795 0.4977118
## ATTEN 6.8048e-04 1.4458e-03 0.4707 0.6384451
## AGE -3.7018e-04 5.0634e-05 -7.3109 8.249e-12 ***
## eLEV_ROCED -6.2265e-01 4.2306e-02 -14.7180 < 2.2e-16 ***
## ePIND_ROCED -6.6499e-02 2.4498e-02 -2.7145 0.0072798 **
## SIZE 2.2477e-03 8.3638e-04 2.6875 0.0078707 **
## Profit_Growth 5.6014e-03 1.9546e-04 28.6570 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 0.028325
## Residual Sum of Squares: 0.0041731
## R-Squared: 0.85267
## Adj. R-Squared: 0.83802
## F-statistic: 116.394 on 9 and 181 DF, p-value: < 2.22e-16reg3_ROCED$coefficient## RDUAL BSIZE MEET ATTEN AGE
## 0.0005976646 0.0016037185 0.0005307522 0.0006804748 -0.0003701816
## eLEV_ROCED ePIND_ROCED SIZE Profit_Growth
## -0.6226542531 -0.0664991034 0.0022477397 0.0056014035#Wooldridge's test for serial correlation in FE panels: Alternative hypothesis: Serial Correlation
pwartest(reg3_ROCED)##
## Wooldridge's test for serial correlation in FE panels
##
## data: reg3_ROCED
## F = 3.1335, df1 = 1, df2 = 188, p-value = 0.07832
## alternative hypothesis: serial correlation#test for heteroskedasticity
#If the p-value is small (typically < 0.05), we reject the null hypothesis.
#This suggests that heteroskedasticity is present in the model, indicating that the variance of residuals is not constant.
bptest(reg3_ROCED)##
## studentized Breusch-Pagan test
##
## data: reg3_ROCED
## BP = 110.41, df = 9, p-value < 2.2e-16stargazer(reg1_ROA,reg2_ROE,reg3_ROCED, type="text", title="The Model Estimates", no.space = TRUE,
digits=4, align=TRUE)##
## The Model Estimates
## =============================================================
## Dependent variable:
## -----------------------------------
## ROA ROE ROCED
## (1) (2) (3)
## -------------------------------------------------------------
## RDUAL -0.0273*** -0.4787*** 0.0006
## (0.0052) (0.1019) (0.0008)
## BSIZE 0.0233*** -0.3063*** 0.0016***
## (0.0027) (0.0521) (0.0004)
## MEET 0.0052 0.0642 0.0005
## (0.0048) (0.0974) (0.0008)
## ATTEN 0.0017 0.2522 0.0007
## (0.0087) (0.1753) (0.0014)
## AGE 0.0067*** 0.0473*** -0.0004***
## (0.0004) (0.0079) (0.0001)
## eLEV 6.7779***
## (0.3555)
## ePIND 0.3336**
## (0.1588)
## eLEV_ROE 54.4176***
## (8.1051)
## ePIND_ROE -17.2461***
## (3.2088)
## eLEV_ROCED -0.6227***
## (0.0423)
## ePIND_ROCED -0.0665***
## (0.0245)
## SIZE -0.1903*** -0.2095** 0.0022***
## (0.0067) (0.1059) (0.0008)
## Profit_Growth 0.0056*** 0.0458* 0.0056***
## (0.0012) (0.0256) (0.0002)
## -------------------------------------------------------------
## Observations 200 200 200
## R2 0.8337 0.4450 0.8527
## Adjusted R2 0.8172 0.3898 0.8380
## F Statistic (df = 9; 181) 100.8415*** 16.1263*** 116.3940***
## =============================================================
## Note: *p<0.1; **p<0.05; ***p<0.01