Journal
JOURNAL OF ECONOMETRICS
Volume 226, Issue 1, Pages 21-61Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.jeconom.2021.03.005
Keywords
Autoregressive panel data models; Time series heteroskedasticity; Bias-corrected score; Random effects; Earnings process
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Likelihood-based estimators for autoregressive panel data models are developed to be consistent in the presence of time series heteroskedasticity. The empirical application suggests evidence against unit roots in individual earnings processes.
We develop likelihood-based estimators for autoregressive panel data models that are consistent in the presence of time series heteroskedasticity. Bias-corrected conditional score estimators, random effects maximum likelihood in levels and first differences, and estimators that impose mean stationarity are considered for general autoregressive models with individual effects. We investigate identification under unit roots, and show that random effects estimation in levels may achieve substantial efficiency gains relative to estimation from data in differences. In an empirical application, we find evidence against unit roots in individual earnings processes from the Panel Study of Income Dynamics and the Spanish section of the European Community Household Panel. (C) 2021 Elsevier B.V. All rights reserved.
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