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The performance of the adaptive optimal estimator under the extended balanced loss function

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  • Nimet Özbay
  • Selahattin Kaçıranlar

Abstract

The adaptive optimal estimator of Farebrother (1975) is discussed by many authors, but the goodness of fitted model criterion that is used to investigate the performance of estimators is quite often ignored. Shalabh, Toutenburg, and Heumann (2009) proposed the extended balanced loss function in which the mean squared error and the Zellner's balanced loss function are just special cases of it. In this paper, we discuss the performance of the adaptive optimal estimator of Farebrother (1975) under the extended balanced loss function. Moreover, a Monte Carlo simulation experiment is conducted to examine the performance of the estimator in finite samples.

Suggested Citation

  • Nimet Özbay & Selahattin Kaçıranlar, 2017. "The performance of the adaptive optimal estimator under the extended balanced loss function," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(22), pages 11315-11326, November.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:22:p:11315-11326
    DOI: 10.1080/03610926.2016.1267760
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