This paper investigates the relationship between moment restrictions and identification in simple linear AR(1) dynamic panel data models with fixed effects under standard minimal assumptions. The number of time periods is assumed to be small. The assumptions imply linear and quadratic moment restrictions which can be used for GMM estimation. The paper makes three points. First, contrary to common belief, the linear moment restrictions may fail to identify the autoregressive parameter even when it is known to be less than 1. Second, the quadratic moment restrictions provide full or partial identification in many of the cases where the linear moment restrictions do not. Third, the first moment restrictions can also be important for identification. Practical implications of the findings are illustrated using Monte Carlo simulations.
|Number of pages||28|
|Journal||Annals of Economics and Statistics|
|Publication status||Published - 2019|
Bibliographical noteFunding Information:
This research was supported in part by ARC grant (DP1096862), a grant from the Korean Government (NRF-2014S1A2A2027803), and the 2019 Guangzhou Philosophy and Social Science Planning Grant (2019GZYB25). aThe Australian National University, Acton ACT 2601, Australia. email@example.com bKorea University, 145 Anam-ro Seonbuk-gu, Seoul, Korea 02841. firstname.lastname@example.org cJinan University, 601 West Huangpu Road, Tianhe District, Guangzhou 510632, China. email@example.com
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- Arellano-Bond Estimator
- Dynamic Panel Data Models
- Fixed Effects
- Generalized Method of Moments
ASJC Scopus subject areas
- Statistics and Probability
- Social Sciences (miscellaneous)
- Economics and Econometrics
- Statistics, Probability and Uncertainty