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Maximum Likelihood Estimation for the Asymmetric Exponential Power Distribution

Author

Listed:
  • Mahdi Teimouri

    (Gonbad Kavous University)

  • Saralees Nadarajah

    (University of Manchester)

Abstract

The asymmetric exponential power (AEP) distribution has received much attention in economics and finance. Simulation study shows that iterative methods developed for finding the maximum likelihood (ML) estimates of the AEP distribution sometimes fail to converge. In this paper, the expectation–maximization (EM) algorithm is proposed to find the ML estimates of the AEP distribution which always converges. Performance of the EM algorithm is demonstrated by simulations and a real data illustration. As an application, the proposed EM algorithm is applied to find the ML estimates for the regression coefficients when the error term in a linear regression model follows the AEP distribution. Performance of the AEP distribution in robust simple regression modelling is established through a real data illustration.

Suggested Citation

  • Mahdi Teimouri & Saralees Nadarajah, 2022. "Maximum Likelihood Estimation for the Asymmetric Exponential Power Distribution," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 665-692, August.
    • Handle: RePEc:kap:compec:v:60:y:2022:i:2:d:10.1007_s10614-021-10162-1
    DOI: 10.1007/s10614-021-10162-1
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    References listed on IDEAS

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