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BWPEF 1704
Andrea Nocera
Causes and Effects of Negative Definite Covariance Matrices in Swamy Type Random Coefficient Models

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.

JEL classification: C12, C15, C23.

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