A Generalized Poisson-Pseudo Maximum Likelihood Estimator

Ohyun Kwon, Jangsu Yoon () and Yoto Yotov
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Jangsu Yoon: Department of Economics, University of Wisconsin-Milwaukee

No 202512, Working Papers from Center for Global Policy Analysis, LeBow College of Business, Drexel University

Abstract: We examine the Constant Variance to Mean Ratio (CVMR) assumption – a key condition to make PPML an efficient estimator – and propose Generalized Poisson-Pseudo Maximum Likelihood (G-PPML) as a complementary estimator. We estimate the conditional variance of the dependent variable using an iterated GMM, thereby providing a specification test for the CVMR assumption. The proposed G-PPML estimator, which capitalizes on conditional variance estimates, is more efficient than existing PML estimators. After establishing the asymptotic properties of the G-PPML estimator, we verify that it performs well under fairly general assumptions about the conditional variance. Our empirical application to trade flows data demonstrates that the CVMR assumption is satisfied in most but not all cases. The standard errors of G-PPML are approximately 20% smaller than those of PPML, demonstrating its improved estimation efficiency.

Keywords: Poisson-Pseudo Maximum Likelihood; Iterated GMM; Gravity Models (search for similar items in EconPapers)
JEL-codes: C13 C50 F10 (search for similar items in EconPapers)
Pages: 68
Date: 2025-04
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https://www.lebow.drexel.edu/sites/default/files/2 ... eralized-poisson.pdf First version, 2022 (application/pdf)

Related works:
Working Paper: A Generalized Poisson-Pseudo Maximum Likelihood Estimator (2022)
Working Paper: A Generalized Poisson-Pseudo Maximum Likelihood Estimator (2022)
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