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

Author

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  • Andrea Nocera

    (Birkbeck, University of London)

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.

Suggested Citation

  • Andrea Nocera, 2017. "Causes and Effects of Negative Definite Covariance Matrices in Swamy Type Random Coefficient Models," Birkbeck Working Papers in Economics and Finance 1704, Birkbeck, Department of Economics, Mathematics & Statistics.
    • Handle: RePEc:bbk:bbkefp:1704
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    File URL: https://eprints.bbk.ac.uk/id/eprint/26863
    File Function: First version, 2017
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    More about this item

    Keywords

    Finite-sample inference; Monte Carlo analysis; negative definite covariance matrices; panel data; random coefficient models.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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