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Re: st: Autocorrelation in Poisson regression

From   David Greenberg <[email protected]>
To   [email protected]
Subject   Re: st: Autocorrelation in Poisson regression
Date   Wed, 06 Aug 2008 17:42:53 -0400

Antonio, there is a potential for unmeasured variables that have some tendency to persist over time to be influencing the number of organizations formed in a year (perhaps the state of the economy, to take one example that comes to mind). That would lead to serial correlation of errors. It is true that the poisson command does not generate deviance residuals. You can get them by using the glm command, specifying poisson  as the family. See help glm post-estimation or the user's manual to get the syntax for obtaining the deviance residuals. The correlogram displays autocorrelations and partial autocorrelations of different lags, and your output will contain a significance test for the significance of those autocorrelations. You will find an explanation in time series textbooks that cover ARIMA modeling. David Greenberg, Sociology Department, New York University

----- Original Message -----
From: Antonio Silva <[email protected]>
Date: Wednesday, August 6, 2008 5:02 pm
Subject: st: Autocorrelation in Poisson regression
To: [email protected]

> I am very impressed with the quality of responses I have gotten, thank 
> you so much.
> In response, I have some comments and a few more questions. First, for 
> Kieran, let me clarify: My dependent variable is “# of groups 
> founded.” What this means is that for each year of the study (there 
> are 40 years) there is a number, which represents the number of new 
> organizations that come into existence in that year. So, for example, 
> in 1965, 3 new groups were formed, in 1966, 2 new groups were formed, 
> and in 1967 0 new groups were formed. So I have a number for each year 
> in the period under study. Does that make sense?  I have tested for 
> overdispersion and this is not a problem.
> Second, in theory, there is an unlimited number of groups that could 
> be formed in any given year, and thus there appears to be no 
> heterogeneity of risk, if I understand that concept correctly. Of 
> course, Kieran may feel that this particular count variable is simply 
> not appropriate for use in Poisson regression, and I am curious to 
> hear your thoughts on this.
> As for Stas’s comments, I wholeheartedly agree that the reviewer in 
> question is not much of a reviewer. In fact, he/she even included in 
> his/her review that he/she “was not sure if autocorrelation is even a 
> problem in Poisson regression,” but that I should discuss it anyway. I 
> have looked everywhere, and all the books and articles I read on 
> Poisson basically imply (but do not explicitly state in a way that is 
> quotable) what Kieran said—they say that if a process truly is 
> Poisson, autocorrelation is not a problem. I think that Stas’ 
> suggestion (that I include some language about there not being a 
> standard test for autocorrelation) is a very good one, and may well 
> work. Though I have to be honest, I am not sure what he means by 
> discretization. Could you indulge me a little more and tell me what 
> you mean by this?
> Finally, David, can you give me an idea of how I can generate the 
> deviance residuals after using the Poisson command in Stata? I thought 
> this option was available for other methods but not Poisson. And what 
> should I look for in the correlogram?
> I am sorry to be asking such basic questions, but this is the first 
> time I have ever used Poisson regression. And to be honest, the reason 
> I am using it is because some other reviewer told me to because my 
> dependent variable was a count variable it was the best way to go.
> Thanks again. This has been very helpful and useful to me.
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