Moment restrictions and identification in linear dynamic panel data models

Tue Gorgens, Chirok Han and Sen Xue

ANU Working Papers in Economics and Econometrics from Australian National University, College of Business and Economics, School of Economics

Abstract: 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.

Keywords: Dynamic panel data models; fixed effects; identification; generalized method of moments; Arellano-Bond estimator. (search for similar items in EconPapers)
JEL-codes: C23 (search for similar items in EconPapers)
Date: 2016-03
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (6)

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Related works:
Journal Article: Moment Restrictions and Identification in Linear Dynamic Panel Data Models (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:acb:cbeeco:2016-633

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