The optimal choice of moments in dynamic panel data models

This paper derives an approximation of the mean square error (MSE) of the GMM estimator in dynamic panel data models. The approximation is based on higher-order asymptotic theory under double asymptotics. While first-order theory under double asymptotics provides information about the bias, it does...

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Bibliographic Details
Published in:Journal of econometrics 2009-07, Vol.151 (1), p.1-16
Main Author: Okui, Ryo
Format: Article
Language:English
Subjects:
Applications
Approximation
Asymptotic methods
Data models
Distribution theory
Dynamic panel data model
Economic models
Estimating techniques
Estimation
Exact sciences and technology
Generalized method of moments
GMM
GMM Dynamic panel data model Higher-order expansion Moment selection
Higher-order expansion
Insurance, economics, finance
Linear inference, regression
Mathematics
Moment selection
Panel data
Probability and statistics
Sciences and techniques of general use
Simulation
Statistics
Studies
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Description
Summary:This paper derives an approximation of the mean square error (MSE) of the GMM estimator in dynamic panel data models. The approximation is based on higher-order asymptotic theory under double asymptotics. While first-order theory under double asymptotics provides information about the bias, it does not provide enough information about the variance of the estimator. Higher-order theory enables us to obtain information about the variance. From this result, a procedure for choosing the number of instruments is proposed. The simulations confirm that the proposed procedure improves the precision of the estimator.
ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2009.04.002