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Transformed-likelihood estimators for dynamic panel models with a very small T

Published online by Cambridge University Press:  13 August 2020

Mark Pickup Open the ORCID record for Mark Pickup [Opens in a new window]  and
Vincent Hopkins
Mark Pickup
Affiliation:
Department of Political Science, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada
Vincent Hopkins*
Affiliation:
Department of Political Science, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada
*
Corresponding author. Email: mark.pickup@sfu.ca
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Abstract

Conventional OLS fixed-effects and GLS random-effects estimators of dynamic models that control for individual-effects are known to be biased when applied to short panel data (T ≤ 10). GMM estimators are the most used alternative but are known to have drawbacks. Transformed-likelihood estimators are unused in political science. Of these, orthogonal reparameterization estimators are only tangentially referred to in any discipline. We introduce these estimators and test their performance, demonstrating that the unused orthogonal reparameterization estimator in particular performs very well and is an improvement on the commonly used GMM estimators. When T and/or N are small, it provides efficiency gains and overcomes the issues GMM estimators encounter in the estimation of long-run effects when the coefficient on the lagged dependent variable is close to one.

Keywords

Bayesiandynamicindividual effectspanel datatransformed-likelihoodunobserved heterogeneity
Type
Original Article
Information
Political Science Research and Methods , Volume 10 , Issue 2 , April 2022 , pp. 333 - 352
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press on behalf of the European Political Science Association

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