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Re: st: xtnbreg question, part 2


From   David Jacobs <jacobs.184@sociology.osu.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: xtnbreg question, part 2
Date   Mon, 15 Mar 2004 15:01:48 -0500

In theory there's no reason why you can't use the Stata NBREG count estimator for time-series data. One potential problem concerns autocorrelation. As you probably already know, OLS assumes independence between cases, but this assumption often is violated when the data is longitudinal. Corrections are available for least squares estimators , but I am only aware one article that attempted to correct negative binomial estimates for autocorrelation and that correction evidently was an ad hoc method supplied to the author by a colleague. Another possibility is the Newey West which corrects for both autocorrelation and heteroskedasticity and is available for NBREG in Stata (or at least this was the case in version 7)

Often, however, there are alternatives. First, if your counts are large the number of zero limit scores is modest, your counts probably won't be poisson distributed. If they are normal or if a transformation will make them normal you should use least squares and this estimator can readily be corrected for autocorrelation. Use normality tests on your counts and see if they reject normality. Or try transformations like loging or square rooting and then test for normality.

If your counts are low and probably poisson distributed, you might get a break and not have autocorrelation.

Dave Jacobs

At 11:15 AM 3/15/2004 -0800, you wrote:

Hi, I posted the following message on Friday and it was suggested by a list member that I should be using the time series modules, not the panel modules. I was also referred to a Stata volume on time series models.

I am still uncertain about the answer though: my dependent variable is count data, which is not appropriately analyzed by linear models. I looked at the table of contents and index for the time series volume before I sent my original message and I did not see any commands for count data (such as negative binomial models). However, the xt series does have xtnbreg.

So, my questions still remain: (1) is there a time series (but not cross sectional time series) command in stata for negative binomial models of count data (and if so, what is the command); and (2) if there is not, can I approximate this by manipulating how i and t are set in the xtnbreg command?

Cheers,

Jennifer


------------------------------

Date: Fri, 12 Mar 2004 16:44:18 -0800
From: "Jennifer S. Earl" <jearl@soc.ucsb.edu>
Subject: st: xtnbreg question

I have time series data on a set of variables for one nation for a period
of about 40 years. My dependent variable is a count variable. After
looking over the Stata manuals for xtnbreg, I am not sure how to
accommodate the fact that while I have time series data, these data are
not composed of cross-sections with multiple units in each cross section
(e.g. I do not have data for multiple nations in each year, just data on
one nation for multiple years).

The manuals seems to suggest that Stata wants to handle data where i=panel
identifier (e.g., an id number for a person who contributes data for
multiple years, or a country id when multiple countries are present in the
time series) and t=time identifier (e.g., year) in xtnbreg. Further
xtnbreg *requires* that "i" is set either in the xtnbreg command or using
"iis (varname)", even though it does not require "t" to be set.

I have two questions: (1) should I be using a command other than xtnbreg
to analyze this time-series count data; and (2) if xtnbreg is appropriate,
will using the following settings:

iis year
tis year
xtnbreg dep_count_var ind_var1 ind_var2...

produce appropriate results?

Thanks in advance,

Jennifer


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------------------------------

Date: Fri, 12 Mar 2004 20:03:13 -0500
From: David Greenberg <dg4@nyu.edu>
Subject: Re: st: xtnbreg question

If you have data for a singlenation for multiple years, you should be
estimating time series models, not models for panel data. Stata Press
publishes an entire manual devoted to such models.

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