Spatial Errors in Count Data Regressions

Marinho Bertanha and Moser Petra
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Moser Petra: Department of Economics, Stanford University, 579 Serra Mall, Stanford, CA, 94305, USA

Journal of Econometric Methods, 2016, vol. 5, issue 1, 49-69

Abstract: 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.

Keywords: citations; count data; patents; Poisson panel models; spatial correlation (search for similar items in EconPapers)
JEL-codes: C10 C12 C23 O31 O33 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (16)

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Working Paper: Spatial Errors in Count Data Regressions (2014)
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DOI: 10.1515/jem-2014-0015

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