A joint serial correlation test for linear panel data models

This paper proposes a joint error serial correlation test to be applied to linear panel data models after generalised method of moments estimation. This new test is an alternative inferential tool to both the m 2 test of [Arellano, M., Bond, S., 1991. Some tests of specification for panel data: Mont...

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Bibliographic Details
Published in:Econometrics 2008-09, Vol.146 (1), p.135-145
Main Author: Yamagata, Takashi
Format: Article
Language:English
Subjects:
[formula omitted] test
Applications
Correlation
Correlation analysis
Cross section dependence
Data models
Distribution theory
Dynamic panel data
Economic models
Estimating techniques
Exact sciences and technology
Generalized method of moments
Insurance, economics, finance
Linear models
Mathematics
Method of moments
Method of moments Dynamic panel data Serial correlation test Slope heterogeneity Cross section dependence m2 test Overidentifying restrictions test
Monte Carlo simulation
Multivariate analysis
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in probability and statistics
Overidentifying restrictions test
Panel data
Probability and statistics
Sciences and techniques of general use
Serial correlation test
Significance tests
Slope heterogeneity
Statistical models
Statistics
Studies
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Summary:This paper proposes a joint error serial correlation test to be applied to linear panel data models after generalised method of moments estimation. This new test is an alternative inferential tool to both the m 2 test of [Arellano, M., Bond, S., 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58, 277–297] and the overidentifying restrictions test. The proposed test, called the m ( 2 , p ) 2 test, involves an examination of the joint significance of estimates of second to p th-order (first differenced) error serial correlations. The small sample properties of the m ( 2 , p ) 2 test are investigated by means of Monte Carlo experiments. The evidence shows that the proposed test mostly outperforms the conventional m 2 test and has high power when the overidentifying restrictions test does not, under a variety of alternatives including slope heterogeneity and cross section dependence.
ISSN:0304-4076
2225-1146
1872-6895
DOI:10.1016/j.jeconom.2008.08.005