Nonnested model comparisons for time series

Tucker McElroy ()

Biometrika, 2016, vol. 103, issue 4, 905-914

Abstract: This paper addresses the topic of nonnested time series model comparisons. The main result is a central limit theorem for the likelihood ratio statistic when the models are nonnested and non-equivalent. The concepts of model equivalence and forecast equivalence, which are important for determining the parameter subset corresponding to the null hypothesis, are developed. The method is validated through a simulation study and illustrated on a retail time series.

Keywords: Akaike information criterion; Autoregressive integrated moving average model; Gaussian quadratic form; Generalized likelihood ratio; Goodness-of-fit (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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