My knowledge of statistics has big gaps. What does "simulate the model (as a reduced model) and see which estimator is better" mean?
Anyway, thanks Rodrigo for your suggestion: I never thought about -xthtaylor-
It sounds like enjoying the benefits of the FE without actually imposing them.
I knew from the manual of -xtabond2- that if T is large, dynamic panel bias can be worked around with FE. First, my T=15 and I was not sure that it is large enough, given that according to the manual T=9 seems not (and, playing on web dataset abdata, I have found that FE actually exhacerbate the problem), but I will have a look at your suggested papers. More important, I thought the problem to be more complicated, given that past y influences not only present y, but also the likelihood that the event "change from regime 1 (z1=0) to 2 (z1>0)" happens. Can FE work this additional complication?
P.S. My exogenous variables are only four. I am not taking care of the possible unobservable in the model; rather, I needed something to use as instruments.
At 02.33 21/03/2007 -0400, "Rodrigo A. Alfaro" wrote:
>Nicola,
>
>It is crucial to put y(t-1) or t (something like that) in your RHS. For a
>large T, you can use -xtreg, fe-, it is known that the bias in the lagged
>dependent variable decreases with T. Two papers in Econometrica analyze
>this: Hahn and Kuersteiner (2002) and Alvarez and Arellano (2003). If you
>are interested in the z's coefficients maybe you can take a look of
>Hausman-Taylor estimator -help xthtaylor-, again you could modify to allow
>more instruments in the last step. For several exogenous variables, maybe
>you are taking care of the possible unobservable in the model then pooled
>least-square is an option. Anyway, if there is a few number of endogenous
>variables, I suggest you to simulate the model (as a reduced model) and see
>which estimator is better.
>
>R.
>
>- ----- Original Message -----
>>I have the following PANEL model:
>> Regime 1 (observed if z1 = 0): y = y(t-1) + exog
>> Regime 2 (observed if z1 > 0): y = y(t-1) + exog + z1 + z2 + endog
>> At t=0, all observations are in regime 1; changing from 1 to 2 depends on
>> y(t-1) and exog; changing from 2 to 1 not possible.
>> y is a count variable, y(t-1) is past y (I am uncertain about using lagged
>> y or a depreciated stock up to t-1; in regime 2, alternatively, I can use
>> a "years-since regime 2" time counter), z1 and z2 are proportions
>> (endogenous; my key independent variables), endog are additional
>> endogenous variables, exog are exogenous. To put it differently, in regime
>> 1 all endogenous are 0, while in regime 2 z1 is not 0 (and remaining
>> endogenous may be 0 or not).
>> All endogenous variables are almost time-invariant (e.g.
>> 0/0/0/0/0/0/0/0/50/50/50/50/50/50/50; 0/0/0/0/0/0/0/0/0/0/0/0/0/0/0;
>> 0/0/0/0/0/0/0/0/0/0/0/0/25/25/50; etc...).
>> I have thought about xtivreg y (z1 z2 endog y(t-1) = exog).
>> Do you have any suggestion or better idea?
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