THE ASYMPTOTIC PROPERTIES OF THE SYSTEM GMM ESTIMATOR IN DYNAMIC PANEL DATA MODELS WHEN BOTH N AND T ARE LARGE

Autor:	Kazuhiko Hayakawa
Rok vydání:	2014
Předmět:	Economics and Econometrics Thesaurus (information retrieval) media_common.quotation_subject Applied mathematics Estimator Infinity Social Sciences (miscellaneous) Mathematics media_common Panel data
Zdroj:	Econometric Theory. 31:647-667
ISSN:	1469-4360 0266-4666
DOI:	10.1017/s0266466614000449
Popis:	In this paper, we derive the asymptotic properties of the system generalized method of moments (GMM) estimator in dynamic panel data models with individual and time effects when both N and T, the dimensions of cross-section and time series, are large. Specifically, we show that the two-step system GMM estimator is consistent when a suboptimal weighting matrix where off-diagonal blocks are set to zero is used. Such consistency results theoretically support the use of the system GMM estimator in large N and T contexts even though it was originally developed for large N and small T panels. Simulation results indicate that the large N and large T asymptotic results approximate the finite sample behavior reasonably well unless persistency of data is strong and/or the variance ratio of individual effects to the disturbances is large.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::10a44e6e8ebb88da29644c4770ad7f51 https://doi.org/10.1017/s0266466614000449 Zobrazit plný text záznamu