Re: st: An econometric question

[Tak-wai Chau <tchau@troi.cc.rochester.edu>]

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" <austinnichols@gmail.com> 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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