Spatial Errors in Count Data Regressions

38 Pages Posted: 12 Mar 2014 Last revised: 5 Dec 2014

Marinho Bertanha

University of Notre Dame - Department of Economics

Petra Moser

Leonard N. Stern School of Business - Department of Economics; National Bureau of Economic Research (NBER)

Multiple version iconThere are 2 versions of this paper

Date Written: December 4, 2014

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: Count-data, Poisson panel models, spatial correlation, patents, citations

JEL Classification: C10, C12, C23, O31, O33

Suggested Citation

Bertanha, Marinho and Moser, Petra, Spatial Errors in Count Data Regressions (December 4, 2014). Available at SSRN: https://ssrn.com/abstract=2406216 or http://dx.doi.org/10.2139/ssrn.2406216

Marinho Bertanha

University of Notre Dame - Department of Economics ( email )

Notre Dame, IN 46556
United States

Petra Moser (Contact Author)

Leonard N. Stern School of Business - Department of Economics ( email )

269 Mercer Street
New York, NY 10003
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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