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st: Re: xtpoisson with endogenous covariate?
Dear Alice,
Note that if Y~Poisson(lambda) then E(Y)=lambda and in MLE framework
lambda_hat = mean(Y). When you add exogenous covariates you are adding the
following equation to your problem lambda = exp(X*beta). exp() function is
because lambda should be positive. On the other hand, IV process assumes
that your problem is linear and unbounded, this means that Y is distribute
over real line and the relation that you have E(Y) = X*beta. As you can see
this is not that you want, which is E(Y) =exp(X*beta).
The "right" solution for your problem is GMM. Basically you have the
following moment condition E{[Y-exp(beta*X)]&Z}=0, where & is outer product.
In words, the errors of your expected value in the poisson process are
orthogonal to some instruments variables (Z). Note GMM in this case is
non-linear, but not too difficult to implement by hand using the sample
counterpart.
The lazy solution is change the dependent variable and use IV process, using
log(Y) instead of Y. Note that this is "statistical" wrong because log[E(Y)]
is not equal to E[log(Y)]. Even worse, Y could be zero, in which case you
have to use log(Y+a) with a>0.
I hope this helps you
Rodrigo.
----- Original Message -----
From: "ALICE DOBSON" <[email protected]>
To: <[email protected]>
Sent: Tuesday, March 28, 2006 8:43 AM
Subject: st: xtpoisson with endogenous covariate?
Hi all,
I use Stata 9.1. What can I do if my count dependent variable is
simultaneously determined with a continuous covariate in a panel data
setting? In such a case my xtpoisson estimates would be biased. I have
good instrumental variables in the data. Should I ignore my count data
dependent variable and simply use xtivreg2 instead?
Best,
Alice
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