Pooled REgression (Page 543)
library("foreign")
library("plm")
data = read.dta("Dataset/panelx.dta")
data=na.omit(data)
data = pdata.frame(data, index=c("firm_ident", "year"))
pdim(data)
## Unbalanced Panel: n = 1734, T = 1-11, N = 8856
pooled = plm(return~beta, model="pooling", data=data)
summary(pooled)
## Pooling Model
##
## Call:
## plm(formula = return ~ beta, data = data, model = "pooling")
##
## Unbalanced Panel: n = 1734, T = 1-11, N = 8856
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -1.0073393 -0.0133705 -0.0017671 0.0201393 0.7041667
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## (Intercept) 0.00184254 0.00307461 0.5993 0.549
## beta 0.00045439 0.00273471 0.1662 0.868
##
## Total Sum of Squares: 24.205
## Residual Sum of Squares: 24.204
## R-Squared: 3.1181e-06
## Adj. R-Squared: -0.00010982
## F-statistic: 0.0276078 on 1 and 8854 DF, p-value: 0.86804
Fixed Effects (Page 544)
within <- plm(return~beta, model="within", data=data)
summary(within)
## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = return ~ beta, data = data, model = "within")
##
## Unbalanced Panel: n = 1734, T = 1-11, N = 8856
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -0.891228 -0.015834 0.000000 0.016408 0.653811
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## beta -0.0118931 0.0041139 -2.8909 0.003852 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 18.371
## Residual Sum of Squares: 18.35
## R-Squared: 0.0011723
## Adj. R-Squared: -0.24205
## F-statistic: 8.35756 on 1 and 7121 DF, p-value: 0.0038525
F-Test, Lagrange Multiplier Test (Page 545)
pFtest(within, pooled) # F-test for individual (fixed) effects
##
## F test for individual effects
##
## data: return ~ beta
## F = 1.3111, df1 = 1733, df2 = 7121, p-value = 9.795e-14
## alternative hypothesis: significant effects
plmtest(pooled, effect="individual") # null hypothesis: No individual effects
##
## Lagrange Multiplier Test - (Honda)
##
## data: return ~ beta
## normal = 1.2444, p-value = 0.1067
## alternative hypothesis: significant effects
plmtest(pooled, effect="time") # null hypothesis: No time effects
##
## Lagrange Multiplier Test - time effects (Honda)
##
## data: return ~ beta
## normal = 122.62, p-value < 2.2e-16
## alternative hypothesis: significant effects
plmtest(pooled, effect="twoways") # null hypothesis: No individual or time effects
##
## Lagrange Multiplier Test - two-ways effects (Honda)
##
## data: return ~ beta
## normal = 87.588, p-value < 2.2e-16
## alternative hypothesis: significant effects
Random Effects (Page 546)
random = plm(return~beta, model="random", data=data)
summary(random)
## Oneway (individual) effect Random Effect Model
## (Swamy-Arora's transformation)
##
## Call:
## plm(formula = return ~ beta, data = data, model = "random")
##
## Unbalanced Panel: n = 1734, T = 1-11, N = 8856
##
## Effects:
## var std.dev share
## idiosyncratic 0.0025768 0.0507625 0.944
## individual 0.0001529 0.0123657 0.056
## theta:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.02841 0.12183 0.19262 0.16455 0.22215 0.22215
##
## Residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.98141 -0.01314 -0.00156 0.00031 0.01972 0.69480
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) 0.0032809 0.0032887 0.9976 0.3185
## beta -0.0014994 0.0029132 -0.5147 0.6068
##
## Total Sum of Squares: 23.117
## Residual Sum of Squares: 23.125
## R-Squared: 0.00048503
## Adj. R-Squared: 0.00037214
## Chisq: 0.264896 on 1 DF, p-value: 0.60678
##
## Hausman Test
##
## data: return ~ beta
## chisq = 12.804, df = 1, p-value = 0.0003459
## alternative hypothesis: one model is inconsistent
Page 556
data = read.dta("Dataset/macro.dta")
Y <- as.matrix(data[c("USTB10Y", "USTB1Y", "USTB3M", "USTB3Y", "USTB5Y", "USTB6M")])
purtest(Y, test = "levinlin", exo = "intercept", lags = "SIC", pmax = 5)
##
## Levin-Lin-Chu Unit-Root Test (ex. var.: Individual Intercepts)
##
## data: Y
## z = 1.4794, p-value = 0.9305
## alternative hypothesis: stationarity
purtest(Y, test = "madwu", exo = "intercept", lags = "SIC", pmax = 5)
##
## Maddala-Wu Unit-Root Test (ex. var.: Individual Intercepts)
##
## data: Y
## chisq = 3.2849, df = 12, p-value = 0.9932
## alternative hypothesis: stationarity
purtest(Y, test = "ips", exo = "intercept", lags = "SIC", pmax = 5)
##
## Im-Pesaran-Shin Unit-Root Test (ex. var.: Individual Intercepts)
##
## data: Y
## Wtbar = 1.5458, p-value = 0.9389
## alternative hypothesis: stationarity