题目：“经济学系列讲座”第48期 A Generalized Poisson-Pseudo Maximum Likelihood Estimator
主讲人：Ohyun Kwon 美国德雷塞尔大学Lebow商学院
We propose a Generalized Poisson-Pseudo Maximum Likelihood (G-PPML) estimator that relaxes PPML estimator's assumption that the dependent variable's conditional variance is proportional to its conditional mean. Instead, we employ an iterated Generalized Method of Moments (iGMM) to estimate the conditional variance of the dependent variable directly from the data, thus encompassing the common estimators in international trade literature (i.e., PPML, Gamma-PML and OLS) as special cases. With the conditional variance estimates, G-PPML generates coefficient estimates that are more efficient and robust to the underlying data generating process. After establishing the consistency and the asymptotic properties of GPPML estimator, we use Monte Carlo simulations to demonstrate that G-PPML is less sensitive to the underlying assumption about the conditional variance. Estimations of a canonical gravity model with trade data reinforce the properties of G-PPML and validate the practical importance of our methods.
Ohyun Kwon is an Assistant Professor at the Drexel University - LeBow College of Business. His recent research focus includes the role of currency in international trade/finance, the impact of trade liberalization on environment and the economic impact of international sanctions. He received his bachelor’s degree in Finance from Peking University in 2012 and his Ph.D. from University of Wisconsin-Madison in 2019.