Efficient estimation of dynamic panel data models: Alternative assumptions and simplified estimation

Seung Ahn; Peter Schmidt

doi:10.1016/0304-4076(95)01793-3

Efficient estimation of dynamic panel data models: Alternative assumptions and simplified estimation

Seung Ahn, Peter Schmidt

Economics

Research output: Contribution to journal › Article › peer-review

61 Scopus citations

Abstract

This paper considers the estimation of dynamic models for panel data. It shows how to count and express the moment conditions implied by a variety of covariance restrictions. These conditions can be imposed in a GMM framework. Many of the moment conditions are nonlinear in the parameters. We derive a simple linearized estimator that is asymptotically as efficient as the nonlinear GMM estimator, and convenient tests of the validity of the nonlinear restrictions.

Original language	English (US)
Pages (from-to)	309-321
Number of pages	13
Journal	Journal of Econometrics
Volume	76
Issue number	1-2
DOIs	https://doi.org/10.1016/0304-4076(95)01793-3
State	Published - 1997

Keywords

Conditional moment tests
Dynamic models
GMM estimation
Panel data
Stationarity

ASJC Scopus subject areas

Economics and Econometrics

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10.1016/0304-4076(95)01793-3

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title = "Efficient estimation of dynamic panel data models: Alternative assumptions and simplified estimation",

abstract = "This paper considers the estimation of dynamic models for panel data. It shows how to count and express the moment conditions implied by a variety of covariance restrictions. These conditions can be imposed in a GMM framework. Many of the moment conditions are nonlinear in the parameters. We derive a simple linearized estimator that is asymptotically as efficient as the nonlinear GMM estimator, and convenient tests of the validity of the nonlinear restrictions.",

keywords = "Conditional moment tests, Dynamic models, GMM estimation, Panel data, Stationarity",

author = "Seung Ahn and Peter Schmidt",

note = "Funding Information: * Corresponding author. The first author gratefully acknowledges the financial support of the College of Business, Arizona State University. The second author gratefully acknowledges the financial support of the National Science Foundation. Both authors are grateful for the helpful comments of the editor and two referees.",

year = "1997",

doi = "10.1016/0304-4076(95)01793-3",

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T2 - Alternative assumptions and simplified estimation

AU - Ahn, Seung

AU - Schmidt, Peter

N1 - Funding Information: * Corresponding author. The first author gratefully acknowledges the financial support of the College of Business, Arizona State University. The second author gratefully acknowledges the financial support of the National Science Foundation. Both authors are grateful for the helpful comments of the editor and two referees.

PY - 1997

Y1 - 1997

N2 - This paper considers the estimation of dynamic models for panel data. It shows how to count and express the moment conditions implied by a variety of covariance restrictions. These conditions can be imposed in a GMM framework. Many of the moment conditions are nonlinear in the parameters. We derive a simple linearized estimator that is asymptotically as efficient as the nonlinear GMM estimator, and convenient tests of the validity of the nonlinear restrictions.

AB - This paper considers the estimation of dynamic models for panel data. It shows how to count and express the moment conditions implied by a variety of covariance restrictions. These conditions can be imposed in a GMM framework. Many of the moment conditions are nonlinear in the parameters. We derive a simple linearized estimator that is asymptotically as efficient as the nonlinear GMM estimator, and convenient tests of the validity of the nonlinear restrictions.

KW - Conditional moment tests

KW - Dynamic models

KW - GMM estimation

KW - Panel data

KW - Stationarity

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JO - Journal of Econometrics

JF - Journal of Econometrics

IS - 1-2

ER -

Efficient estimation of dynamic panel data models: Alternative assumptions and simplified estimation

Abstract

Keywords

ASJC Scopus subject areas

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