A Bias-Corrected Method of Moments Approach to Estimation of Dynamic Short-T Panels

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. This paper contributes to the GMM literature by introducing the idea of self-instrumenting target variables instead of searching for instruments that are uncorrelated with the errors, in cases where the correlation between the target variables and the errors can be derived. The advantage of the proposed approach lies in the fact that, by construction, the instruments have maximum correlation with the target variables and the problem of weak instrument is thus avoided. The proposed approach can be applied to estimation of a variety of models such as spatial and dynamic panel data models. In this paper we focus on the latter and consider both univariate and multivariate panel data models with short time dimension. Simple Bias-corrected Methods of Moments (BMM) estimators are proposed and shown to be consistent and asymptotically normal, under very general conditions on the initialization of the processes, individual-speci.c e¤ects, and error variances allowing for heteroscedasticity over time as well as cross-sectionally. Monte Carlo evidence document BMM.s good small sample performance across di¤erent experimental designs and sample sizes, including in the case of experiments where the system GMM estimators are inconsistent. We also .nd that the proposed estimator does not su¤er size distortions and has satisfactory power performance as compared to other estimators. Terms of use: Documents inJEL-Codes: C120, C130, C230.

Estimation and Inference in Spatial Models with Dominant Units

“…We also propose a new bias-corrected method of moments (BMM), which is also applicable generally and is simple to implement. The BMM approach was …rst introduced in a recent paper by Chudik and Pesaran (2017) for the estimation of dynamic panel data models with short time-dimension. In the context of the SAR model, the spatial lag variable is endogenous.…”