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Asymmetric Double Pareto Distributions: Maximum Likelihood Estimation with Application to the Growth Rate Distribution of Firms

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This paper considers a flexible class of asymmetric double Pareto distributions (ADP) that allows for skewness and asymmetric heavy tails. The inference problem is examined for maximum likelihood. Consistency is proven for the general case when all parameters are unknown. After deriving the Fisher information matrix, asymptotic normality and efficiency are established for a restricted model with the location parameter known. The asymptotic properties of the estimators are then examined using Monte Carlo simulations. To assess its goodness of fit, the ADP is applied to companies’ growth rates, for which it is unequivocally favored over competing models

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  • Halvarsson, Daniel, 2019. "Asymmetric Double Pareto Distributions: Maximum Likelihood Estimation with Application to the Growth Rate Distribution of Firms," Ratio Working Papers 327, The Ratio Institute.
    • Handle: RePEc:hhs:ratioi:0327
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    More about this item

    Keywords

    Distribution Theory; Double Pareto Distribution; Maximum Likelihood; Firm Growth;
    All these keywords.

    JEL classification:

    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions

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