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

Jose Diogo Barbosa and Marcelo Moreira
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Jose Diogo Barbosa: Institute for Fiscal Studies

No CWP04/17, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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 satis fied by subtracting initial observations from the data. We show that di fferencing out the data further erodes efficiency compared to drawing inference conditional on the rst observations. Finally, we compare the conditional method with standard random eff ects approaches for unobserved data. Standard approaches implicitly rely on normal approximations, which may not be reliable when unobserved data is very skewed with some mass at zero values. For example, panel data on fi rms naturally depend on the fi rst period in which the fi rm enters on a new state. It seems unreasonable then to assume that the process determining unobserved data is known or stationary. We can instead make inference on structural parameters by conditioning on the initial observations.

Keywords: Autoregressive; Panel Data; Invariance; Eciency. (search for similar items in EconPapers)
JEL-codes: C12 C30 (search for similar items in EconPapers)
Date: 2017-01-20
New Economics Papers: this item is included in nep-ecm and nep-ets
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Related works:
Journal Article: Likelihood inference and the role of initial conditions for the dynamic panel data model (2021)
Working Paper: Likelihood inference and the role of initial conditions for the dynamic panel data model (2017)
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