Likelihood inference and the role of initial conditions for the dynamic panel data model

José Diogo Barbosa and Marcelo Moreira

Journal of Econometrics, 2021, vol. 221, issue 1, 160-179

Abstract: Lancaster (2002) proposes an estimator for the dynamic panel data model with homoskedastic errors and zero initial conditions. In this paper, we show this estimator is invariant to orthogonal transformations, but is inefficient because it ignores additional information available in the data. The zero initial condition is trivially satisfied by subtracting initial observations from the data. We show that differencing out the data further erodes efficiency compared to drawing inference conditional on the first observations.

Keywords: Autoregressive; Panel data; Invariance; Efficiency (search for similar items in EconPapers)
JEL-codes: C12 C30 (search for similar items in EconPapers)
Date: 2021
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Related works:
Working Paper: Likelihood inference and the role of initial conditions for the dynamic panel data model (2017)
Working Paper: Likelihood inference and the role of initial conditions for the dynamic panel data model (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:221:y:2021:i:1:p:160-179

DOI: 10.1016/j.jeconom.2020.04.039

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