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    Causes and effects of negative definite covariance matrices in Swamy Type Random Coefficient Models

    Nocera, Andrea (2017) Causes and effects of negative definite covariance matrices in Swamy Type Random Coefficient Models. Working Paper. Birkbeck, University of London, London, UK.

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    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.

    Metadata

    Item Type: Monograph (Working Paper)
    Additional Information: BWPEF 1704
    Keyword(s) / Subject(s): Finite-sample inference, Monte Carlo analysis, negative definite covariance matrices, panel data, random coefficient models
    School: School of Business, Economics & Informatics > Economics, Mathematics and Statistics
    Depositing User: Administrator
    Date Deposited: 25 Mar 2019 13:46
    Last Modified: 22 Jul 2020 01:40
    URI: https://eprints.bbk.ac.uk/id/eprint/26863

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