On Thu, 7 Apr 2011, Florian Markowetz wrote:
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)).
I'm not sure about the Stata command (because I haven't got Stata
installed myself) and how it translates to R. Other people might know
better.
From the verbal description "OLS plus panel-corrected standard errors" I
would have expected that the coefficients could be estimated by lm() but
that does not seem to be the case. Note sure why...it doesn't seem to be
_O_LS then. Did you check that the Stata command produces the same output
as indicated in the paper? (Maybe some data preprocessing is
necessary...?)
In any case, I've had success with replicating such results with the "plm"
package (see also http://www.jstatsoft.org/v27/i02/). Typically using the
model = "pooling" (i.e., OLS) and then computing the standard errors via
vcovBK(). The latter stands for "Beck & Katz" which is what the "pcse"
package also implements.
In a few other cases, I replicated the so-called panel-corrected standard
errors via geeglm() from "geepack" (http://www.jstatsoft.org/v15/i02/).
Using the default corstr = "independence" (i.e., again correspond to OLS).
Other corstr could be employed.
Just as additional information: Many econometricians don't know much about
the type of models the "nlme" estimates. Usually, least squares technology
is preferred in econometrics rather than likelihood-based ideas. Also,
other multi-level models are rarely used. If specified in the same way,
both approaches often yield similar results. There is a paragraph in the
above-mentioned JSS paper on "plm" that discusses (dis)similarities with
"nlme".
Finally, a JSS paper on the "pcse" package is also waiting for publication
in a special volume...hopefully online next month.
Good luck with the replication!
Best,
Z
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 <-
read.dta("http://umdrive.memphis.edu/rblanton/public/ISQ_data/blanton_isq08_data.dta";)
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.markow...@cancer.org.uk
web : http://www.markowetzlab.org
skype: florian.markowetz
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