Moment Restrictions and Identification in Linear Dynamic Panel Data Models

Tue Gørgens, Chirok Han and Sen Xue

Annals of Economics and Statistics, 2019, issue 134, 149-176

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: 2019
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Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Moment restrictions and identification in linear dynamic panel data models (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:adr:anecst:y:2019:i:134:p:149-176

DOI: 10.15609/annaeconstat2009.134.0149

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