Non-Linear Time Series Modelling and Distributional Flexibility

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.


Issue Date:
Dec 01 1991
Publication Type:
Working or Discussion Paper
Record Identifier:
http://ageconsearch.umn.edu/record/267376
Language:
English
Total Pages:
28
Series Statement:
Working Paper No. 14/91




 Record created 2018-01-30, last modified 2018-01-31

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