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Re: st: xtabond


From   Mark Schaffer <M.E.Schaffer@hw.ac.uk>
To   statalist@hsphsun2.harvard.edu, Antonio Rodrigues Andres <ara@sam.sdu.dk>
Subject   Re: st: xtabond
Date   Sun, 25 Apr 2004 18:10:39 +0100 (BST)

Antonio,

Quoting Antonio Rodrigues Andres <ara@sam.sdu.dk>:

> Dear Stata users
> 
> I am trying to estimate a dynamic panel data model where the
> lagged
> value of the endogenous variables is used as further regressor.
> The
> right command should be xtabond in STATA. I did it but I got the
> following message:
> 
> Does anybody know 
> 
> I am using an unbalanced panel data where countries are not
> observed
> consecutively. I dont know if this may cause a serious problem.
> 
> tsset country year
>        panel variable:  country, 1 to 21
>         time variable:  year, 1950 to 1998, but with gaps
> 
> . xtabond srt gini income, lag(1)
> matsize too small
>     You have attempted to create a matrix with more than 800 rows
> or
> columns or to estimate a model with more than 800
>     variables plus ancillary parameters.  You need to increase
> matsize
> using the set matsize command; see help matsize.
> matsize must be at least 1061
> (you have 1061 instruments)

I might be wrong but I think you're falling into the xtabond trap of 
creating too many instruments.  You have a dataset of 21*49=1029 
observations.  The problem is that you aren't making use of the maxlags() 
option, and the default - which in my view is a dangerous one! - is to 
make use of as many lags of the dependent/predetermined variables as 
possible when constructing the instrument set.

The lags option is set to 1, so this means are making use of 49-1-2=46 
lags of the dependent/predetermined variables as instruments.  Because of 
the way the instrument set is constructed when using xtabond, the number 
of instruments adds up *very* quickly, and in your case the result is that 
you have more instruments than observations.  (This is why I think the 
default is dangerous.)  This is clearly not allowed, but even if you got 
the number of instruments down to 800 or whatever, you would still have 
major problems because of finite sample bias - the bias gets bigger as the 
number of instruments goes up.

I'm not sure what the consensus in the literature is on this, but I would 
guess that if you have 1000 observations, then you probably don't want 
more than 100 or so instruments.  Use the maxlags() option to get this.

Hope this helps.

Cheers,
Mark

> 
> Antonio
> 
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> 



Prof. Mark Schaffer
Director, CERT
Department of Economics
School of Management & Languages
Heriot-Watt University, Edinburgh EH14 4AS
tel +44-131-451-3494 / fax +44-131-451-3008
email: m.e.schaffer@hw.ac.uk
web: http://www.sml.hw.ac.uk/ecomes
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