Causes and Effects of Negative Definite Covariance Matrices in Swamy Type Random Coefficient Models

Andrea Nocera
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Andrea Nocera: Birkbeck, University of London

No 1704, Birkbeck Working Papers in Economics and Finance from Birkbeck, Department of Economics, Mathematics & Statistics

Abstract: In this paper, we investigate the causes and the finite-sample consequences of negative definite covariance matrices in Swamy type random coefficient models. Monte Carlo experiments reveal that the negative definiteness problem is less severe when the degree of coefficient dispersion is substantial, and the precision of the regression disturbances is high. The sample size also plays a crucial role. We then demonstrate that relying on the asymptotic properties of a biased but consistent estimator of the random coefficient covariance may lead to poor inference.

Keywords: Finite-sample inference; Monte Carlo analysis; negative definite covariance matrices; panel data; random coefficient models. (search for similar items in EconPapers)
JEL-codes: C12 C15 C23 (search for similar items in EconPapers)
Date: 2017-06
New Economics Papers: this item is included in nep-ecm and nep-ore
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https://eprints.bbk.ac.uk/id/eprint/26863 First version, 2017 (application/pdf)

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