Estimation and Inference in Mixed Fixed and Random Coefficient Panel Data Models

Andrea Nocera
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Andrea Nocera: Birkbeck, University of London

No 1703, Birkbeck Working Papers in Economics and Finance from Birkbeck, Department of Economics, Mathematics & Statistics

Abstract: In this paper, we propose to implement the EM algorithm to compute restricted maximum likelihood estimates of both the average effects and the unit-specific coefficients as well as of the variance components in a wide class of heterogeneous panel data models. Compared to existing methods, our approach leads to unbiased and more efficient estimation of the variance components of the model without running into the problem of negative definite covariance matrices typically encountered in random coefficient models. This in turn leads to more accurate estimated standard errors and hypothesis tests. Monte Carlo simulations reveal that the proposed estimator has relatively good finite sample properties. In evaluating the merits of our method, we also provide an overview of the sampling and Bayesian methods commonly used to estimate heterogeneous panel data. A novel approach to investigate heterogeneity of the sensitivity of sovereign spreads to government debt is presented.

Keywords: EM algorithm; restricted maximum likelihood; correlated random coefficient models; heterogeneous panels; debt intolerance; sovereign credit spreads. (search for similar items in EconPapers)
JEL-codes: C13 C23 C63 F34 G15 H63 (search for similar items in EconPapers)
Date: 2017-06
New Economics Papers: this item is included in nep-ecm
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https://eprints.bbk.ac.uk/id/eprint/26864 First version, 2017 (application/pdf)

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