Alternative over-identifying restriction test in the GMM estimation of panel data models
| Autor: | Kazuhiko Hayakawa |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Economics and Econometrics 05 social sciences Monte Carlo method Block matrix System of linear equations 01 natural sciences Weighting Moment (mathematics) 010104 statistics & probability Matrix (mathematics) 0502 economics and business Test statistic 0101 mathematics Statistics Probability and Uncertainty Algorithm 050205 econometrics Mathematics Generalized method of moments |
| Zdroj: | Econometrics and Statistics. 10:71-95 |
| ISSN: | 2452-3062 |
| DOI: | 10.1016/j.ecosta.2018.06.002 |
| Popis: | A new over-identifying restriction test in the generalized method of moments (GMM) estimation of panel data models is proposed. In contrast to the conventional over-identifying restriction test, where the sample covariance matrix of the moment conditions is used in the weighting matrix, the proposed test uses a block diagonal weighting matrix constructed from the efficient optimal weighting matrix. It is shown that the proposed test statistic asymptotically follows the weighted sum of the chi-square distribution with one degree of freedom. A detailed local power analysis is provided for dynamic panel data models, and it is demonstrated that the new test has a comparable power to the conventional J test in many cases. The Monte Carlo simulations reveal that the proposed test has a substantially better size property than the conventional test does. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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