ROBUST LIKELIHOOD ESTIMATION OF DYNAMIC PANEL DATA MODELS

Javier Alvarez () and Manuel Arellano ()
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Javier Alvarez: http://www.cemfi.es

Working Papers from CEMFI

Abstract: 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 (RML) in levels and first differences, and estimators that impose mean stationarity and considered for AR(p) models with individual effects. We investigate identification under unit roots, and show that RML in levels may achieve substantial efficiency gains relative to estimators from data in differences. In an empirical application, we find evidence against unit roots in individual earnings processes from the PSID and the Spanish section of the European Panel.

Keywords: Autoregressive panel data model; bias corrected score; time series heteroskedasticity; random effects; unit root identification; mean stationarity; individual earnings. (search for similar items in EconPapers)
JEL-codes: C23 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cfn, nep-ecm and nep-ets
Date: 2004-12
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Persistent link: http://EconPapers.repec.org/RePEc:cmf:wpaper:wp2004_0421

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