Finite-Sample Bias of the Conditional Gaussian Maximum Likelihood Estimator in ARMA Models

Yong Bao

A chapter in Essays in Honor of Aman Ullah, 2016, vol. 36, pp 207-244 from Emerald Group Publishing Limited

Abstract: I derive the finite-sample bias of the conditional Gaussian maximum likelihood estimator in ARMA models when the error follows some unknown non-normal distribution. The general procedure relies on writing down the score function and its higher order derivative matrices in terms of quadratic forms in the non-normal error vector with the help of matrix calculus. Evaluation of the bias can then be straightforwardly conducted. I give further simplified bias results for some special cases and compare with the existing results in the literature. Simulations are provided to confirm my simplified bias results.

Keywords: ARMA; conditional Gaussian maximum likelihood estimator; bias; C32; C12 (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-905320160000036015

DOI: 10.1108/S0731-905320160000036015

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