Frequentist model averaging for threshold models

Yan Gao, Xinyu Zhang, Shouyang Wang, Terence Tai Leung Chong and Guohua Zou

MPRA Paper from University Library of Munich, Germany

Abstract: This paper develops a frequentist model averaging approach for threshold model specifications. The resulting estimator is proved to be asymptotically optimal in the sense of achieving the lowest possible squared errors. In particular, when com-bining estimators from threshold autoregressive models, this approach is also proved to be asymptotically optimal. Simulation results show that for the situation where the existing model averaging approach is not applicable, our proposed model averaging approach has a good performance; for the other situations, our proposed model aver-aging approach performs marginally better than other commonly used model selection and model averaging methods. An empirical application of our approach on the US unemployment data is given.

Keywords: Asymptotic optimality · Generalized cross-validation · Model averaging; Threshold model (search for similar items in EconPapers)
JEL-codes: C13 C52 (search for similar items in EconPapers)
Date: 2017-11-28
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Journal Article: Frequentist model averaging for threshold models (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:92036

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