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Re: st: An econometric question


From   Tak-wai Chau <[email protected]>
To   [email protected]
Subject   Re: st: An econometric question
Date   Mon, 09 Apr 2007 13:45:31 -0400

Hi, Roy,

What are your exact reasons why you don't want to put in z1, z2 directly? How does it look like if you put z1 and z2 instead of their country means and use cluster(country)?

Tak Wai

Roy,Suryadipta wrote:

Hi everyone,

Thank you so much for your comments- I am really learning a lot out of this. Regarding Austin's suggestion of using the cluster(id) option, its true that it makes the reults look similar to the collapse version in terms of significance. While I really want to take heart from Maarten's suggestion of not worrying about the significance (my signs are coming out great and exactly as predicted by the theoretical model!), I am worried as to how the referee's would take to it. Actually the explanatory variables are more like institutional measures which take a long time to change, but they do differ across countries a lot.

In this regard, I was wondering if a cluster(region)- option instead of the cluster(country)- option would be appropriate. Any suggestion would be highly appreciated.

Thanks!
Suryadipta.



On Mon, 9 Apr 2007 12:46:00 -0400
"Austin Nichols" <[email protected]> wrote:

Suryadipta Roy--
If you use the 1500 observations, or 15 years of data on 100
countries, and you use the option cluster(country) on your regression
command, then you take account of dependence of the errors within
country over time, which allows the "effective sample size" to be some
number between 100 and 1500.  In the case where there is really no new
information across years, the cluster() option should make the results
look more like the -collapse-d version in terms of statistical
significance of coefficients.  The cluster() option is advisable for
the 1500 obs case even if you have a lot of within-country variation
because it is likely you have some kind of serial correlation.
Example:
webuse grunfeld, clear
egen mean_inv=mean(inv), by(comp)
egen mean_k=mean(k), by(comp)
reg mean_k mean_inv, nohe
reg mean_k mean_inv, nohe cluster(comp)
collapse k inv, by(comp)
reg kstock inv, nohe
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