Spatial Errors in Count Data Regressions

Marinho Bertanha and Petra Moser

No 20374, NBER Working Papers from National Bureau of Economic Research, Inc

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 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. In addition to the PCFE, this result also applies to the commonly used Logit model of panel data with fixed effects. We develop a test for time-invariant spatial dependence and provide code in STATA and MATLAB to implement the test.

JEL-codes: C23 C33 O3 (search for similar items in EconPapers)
Date: 2014-08
New Economics Papers: this item is included in nep-dcm and nep-ecm
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Citations: View citations in EconPapers (4)

Published as Bertanha Marinho & Moser Petra, 2016. "Spatial Errors in Count Data Regressions," Journal of Econometric Methods, De Gruyter, vol. 5(1), pages 49-69, January.

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Journal Article: Spatial Errors in Count Data Regressions (2016)
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