Spatial Errors in Count Data Regressions

37 Pages Posted: 18 Aug 2014 Last revised: 29 Sep 2014

See all articles by Marinho Bertanha

Marinho Bertanha

University of Notre Dame - Department of Economics

Petra Moser

NYU Stern Department of Economics; National Bureau of Economic Research (NBER)

Multiple version iconThere are 2 versions of this paper

Date Written: August 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 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.

Suggested Citation

Bertanha, Marinho and Moser, Petra, Spatial Errors in Count Data Regressions (August 2014). NBER Working Paper No. w20374. Available at SSRN: https://ssrn.com/abstract=2482136

Marinho Bertanha (Contact Author)

University of Notre Dame - Department of Economics ( email )

Notre Dame, IN 46556
United States

Petra Moser

NYU Stern Department of Economics ( email )

44 West 4th 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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