A Generalized Poisson-Pseudo Maximum Likelihood Estimator
Date: Thursday, Sep 22, 2022, 10:30 ~ 12:00
Speaker: 윤장수(University of Winsconsin-Milwaukee)
Location: Zoom을 통한 온라인 세미나
※ 세미나 참여 신청은 조교에게 신분 및 소속을 기재하여 메일(jaebeoman2856@gmail.com) 송부해 주시기 바랍니다.
※ 경제학부 대학원생의 경우 연구실 단체 공지를 통해 Zoon link를 안내할 예정이니, 메일을 보내지 않으셔도 됩니다.
※ 세미나 참석 인정을(졸업 요건 및 BK) 받기 원하시는 경제학부 대학원생은 Zoom 입장 시 참가자명을 “이름-학번”으로 변경해 주시기 바랍니다.
(예시: 김경제-2020-20002) Zoom Website 기록에 남아있는 참가자 목록으로 참석을 증빙합니다.
주 관 : BK21사업단, 경제학부, 경제연구소 한국경제혁신센터
Abstract
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.
※ 경제학부 대학원생의 경우 연구실 단체 공지를 통해 Zoon link를 안내할 예정이니, 메일을 보내지 않으셔도 됩니다.
※ 세미나 참석 인정을(졸업 요건 및 BK) 받기 원하시는 경제학부 대학원생은 Zoom 입장 시 참가자명을 “이름-학번”으로 변경해 주시기 바랍니다.
(예시: 김경제-2020-20002) Zoom Website 기록에 남아있는 참가자 목록으로 참석을 증빙합니다.
주 관 : BK21사업단, 경제학부, 경제연구소 한국경제혁신센터
Abstract
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.