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I want to estimate a fixed effects model while using panel-corrected standard errors as well as Prais-Winsten (AR1) transformation in order to solve panel heteroscedasticity, contemporaneous spatial correlation and autocorrelation.

I have time-series cross-section data and want to perform regression analysis. I was able to estimate a fixed effects model, panel corrected standard errors and Prais-winsten estimates individually. And I was able to include panel corrected standard errors in a fixed effects model. But I want them all at once. 

# Basic ols model
ols1 <- lm(y ~ x1 + x2, data = data)
summary(ols1)

# Fixed effects model
library('plm')
plm1 <- plm(y ~ x1 + x2, data = data, model = 'within')
summary(plm1)

# Panel Corrected Standard Errors
library(pcse)
lm.pcse1 <- pcse(ols1, groupN = Country, groupT = Time)
summary(lm.pcse1)

# Prais-Winsten estimates
library(prais)
prais1 <- prais_winsten(y ~ x1 + x2, data = data)
summary(prais1)

# Combination of Fixed effects and Panel Corrected Standard Errors
ols.fe <- lm(y ~ x1 + x2 + factor(Country) - 1, data = data)
pcse.fe <- pcse(ols.fe, groupN = Country, groupT = Time)
summary(pcse.fe)

In the Stata command: xtpcse it is possible to include both panel corrected standard errors and Prais-Winsten corrected estimates, with something allong the following code: 

xtpcse y x x x i.cc, c(ar1)

I would like to achieve this in R as well.

Helix123
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Boaz Kaarsemaker
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1 Answers1

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I am not sure that my answer will completely address your concern, these days I've been trying to deal with the same problem that you mention.

In my case, I ran the Prais-Winsten function from the package prais where I included my model with the fixed effects. Afterwards, I correct for heteroskedasticity using the function vcovHC.prais which is analogous to vcovHC function from the package sandwich.

This basically will give you White's/sandwich heteroskedasticity-consistent covariance matrix which, if you later fit into the function coeftest from the package lmtest, it will give you the table output with the corrected standard errors. Taking your posted example, see below the code that I have used:

# Prais-Winsten estimates with Fixed Effects
library(prais)
prais.fe <- prais_winsten(y ~ x1 + x2  + factor(Country), data = data)

library(lmtest)
prais.fe.w <- coeftest(prais.fe, vcov = vcovHC.prais(prais.fe, "HC1")

h.m1 # run the object to see the output with the corrected standard errors.

Alas, I am aware that the sandwhich heteroskedasticity-consistent standard errors are not exactly the same as the Beck and Katz's PCSEs because PCSE deals with panel heteroskedasticity while sandwhich SEs addresses overall heteroskedasticity. I am not totally sure in how much these two differ in practice, but something is something.

I hope my answer was somehow helpful, this is actually my very first answer :D

Ramskin
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