Non-Linear Time Series Modelling and Distributional Flexibility

Jenny N. Lye and Vance L. Martin

No 267376, Department of Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: Most of the existing work in non-linear time series analysis has concentrated on generating flexible functional models by specifying non-linear specifications for the mean of a particular process without much, if any, attention given to the distributional properties of the model. However, as Martin (1991) has shown, greater flexibility in perhaps a more natural way, can be achieved by consideration of distributions from the generalized exponential class. This paper represents an extension of the earlier work of Martin by introducing a flexible class of non-linear time series models which can capture a wide range of empirical behaviour such as skewed, fat-tailed .and even multimodal distributions. This class of models is referred to as GENTS: Generalized Exponential Non-linear Time Series. A maximum likelihood algorithm is given for estimating the parameters of the model, and the framework is applied to estimating the distribution of the movements of the exchange rate.

Keywords: Research; Methods/Statistical; Methods (search for similar items in EconPapers)
Pages: 28
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Persistent link: https://EconPapers.repec.org/RePEc:ags:monebs:267376

DOI: 10.22004/ag.econ.267376

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