Count data regressions are an important tool for empirical analyses ranging from analyses of patent counts to measures of health and unemployment. Along with negative binomial, Poisson panel regressions are a preferred method of analysis because the Poisson conditional fixed effects maximum likelihood estimator (PCFE) and its sandwich variance estimator are consistent even if the data are not Poisson-distributed, or if the data are correlated over time. Analyses of counts may however also be affected by correlation in the cross-section. For example, patent counts or publications may increase across related research fields in response to common shocks. This paper shows that the PCFE and its sandwich variance estimator are consistent in the presence of such dependence in the cross-section – as long as spatial dependence is time-invariant. We develop a test for time-invariant spatial dependence and provide code in STATA and MATLAB to implement the test.
We thank Han Hong, Guido Imbens, Aprajit Mahajan, and Scott Stern for helpful conversations and comments. Moser gratefully acknowledges support through NSF CAREER grant 1151180; Bertanha gratefully acknowledges the financial support received as a B.F. Haley and E.S. Shaw Fellow from the Stanford Institute for Economic Policy Research.
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The online version of this article (DOI: 10.1515/jem-2014-0015) offers supplementary material, available to authorized users.
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