Moment restrictions and identification in linear dynamic panel data models

Tue Gørgens, Chirok Han, Sen Xue

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)149-176
Number of pages28
JournalAnnals of Economics and Statistics
Issue number134
DOIs
Publication statusPublished - 2019 Jan 1

Fingerprint

Panel Data
Data Model
Restriction
Moment
Partial Identification
simulation
Fixed Effects
Period of time
Dynamic panel data model
Monte Carlo Simulation
Imply

Keywords

  • Arellano-Bond Estimator
  • Dynamic Panel Data Models
  • Fixed Effects
  • Generalized Method of Moments
  • Identification

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

Cite this

Moment restrictions and identification in linear dynamic panel data models. / Gørgens, Tue; Han, Chirok; Xue, Sen.

In: Annals of Economics and Statistics, No. 134, 01.01.2019, p. 149-176.

Research output: Contribution to journalArticle

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