# [R] Panel data - replicating Stata's xtpcse in R

Florian Markowetz Florian.Markowetz at cancer.org.uk
Thu Apr 7 19:17:18 CEST 2011

```Dear list,

I am trying to replicate an econometrics study that was orginally done in Stata. (Blanton and Blanton. 2009. A Sectoral Analysis of Human Rights and FDI: Does Industry Type Matter?  International Studies Quarterley 53 (2):469 - 493.) The model I try to replicate is in Stata given as

xtpcse total_FDI lag_total ciri human_cap worker_rts polity_4 market income econ_growth log_trade fix_dollar fixed_xr xr_fluct lab_growth english, pairwise corr(ar1)

According to the paper, this is an OLS regression with panel corrected standard errors including a lagged dependent variable (lag_total is total_FDI t-1) and controlling first order correlations within each panel (corr(ar1)).

The BIG QUESTION is how to replicate this line in R.

Econometrics is a new field to me, but a bit of searching showed that  packages like plm, nlme, pcse should be able to handle this kind of problem. In particular, function gls() uses auto-correlation structure and pcse() corrects the standard errors of the fitted model. Below is some code to show what I have done, and some problems I ran into.

## setup and load data from web
library(foreign)
library(nlme)
library(pcse)
D[544,"year"] <- 2005 ## fixing an unexpected NA in the year column

## Model formula
form <- total_FDI ~ lag_total + ciri + human_cap + worker_rts + polity_4 + market_size + income + econ_growth + log_trade + fixed_xr + fix_dollar + xr_fluct + english + lab_growth

## Model 1: no auto-correlation
res1  <- gls(model=form, data=D,correlation=NULL,na.action=na.omit)
coefficients(res1)

## Model 2: with auto-correlation
corr <- corAR1(.1,~1|c_name)
res2  <- gls(model=form, data=D,correlation=corr,na.action=na.omit)
coefficients(res2)

Now, I know from the paper how the Stata coefficients looked like.  For example, for log_total it should be .852 and for market_size .21 (these were the two significant ones). The result of Model1 is closer to this than the result of Model 2, but there is still quite a gap.

The goal is to do OLS on panel data with AR(1) and PCSE - am I on the right track here? More specifically:

Question 1: Auto-correlation
- how to specify the parameter 'value' in corAR1 (the .1 above is completely arbitrary)
- Any other ideas how to translate Stata's corr(AR1) into R? (I'm not even completely sure what Stata does there and didn't find any details in the online manuals)

Question 2: PCSE
- the pcse function seems to work on objects of class 'lm' only. Any way to use it for gls-objects?

Any help is greatly appreciated!
Florian

--
Florian Markowetz

Cancer Research UK
Cambridge Research Institute
Li Ka Shing Centre
Robinson Way, Cambridge, CB2 0RE, UK

phone: +44 (0) 1223 40 4315
email: florian.markowetz at cancer.org.uk
web  : http://www.markowetzlab.org
skype: florian.markowetz

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