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Selecting Nonlinear Time Series Models Using Information Criteria

Zacharias PsaradakisMartín SolaFabio SpagnoloNicola Spagnolo

出版	SSRN, 2009
URL	http://books.google.com.hk/books?id=YLrgzwEACAAJ&hl=&source=gbs_api

註釋

This article considers the problem of selecting among competing nonlinear time series models by using complexity-penalized likelihood criteria. An extensive simulation study is undertaken to assess the small-sample performance of several popular criteria in selecting among nonlinear autoregressive models belonging to some families that have been popular with practitioners.