Efficiency improvements for minimum distance estimation of causal and invertible ARMA models Articles uri icon

publication date

  • January 2018

start page

  • 150

end page

  • 152


  • 162

International Standard Serial Number (ISSN)

  • 0165-1765

Electronic International Standard Serial Number (EISSN)

  • 1873-7374


  • In this note we analyze efficiency improvements over the Gaussian maximum likelihood (ML) estimator for frequency domain minimum distance (MD) estimation for causal and invertible autoregressive moving average (ARMA) models. The analysis complements Velasco and Lobato (2017) where optimal MD estimation, which employs information in higher order moments, is studied for the general possibly non causal or non-invertible case. We consider MD estimation that combines in two manners the information contained in second, third, and fourth moments. We show that for both MD estimators efficiency improvements over the Gaussian ML occur when the distribution of the innovations is platykurtic. In addition, we show that asymmetry alone is not associated with efficiency improvements. (C) 2017 Elsevier B.V. All rights reserved.


  • Economics


  • higher-order moments; efficiency; kurtosis