Chapter

Parametric nonlinear models

Timo Teräsvirta, Dag Tjøstheim and W. J. Granger

in Modelling Nonlinear Economic Time Series

Published in print December 2010 | ISBN: 9780199587148
Published online May 2011 | e-ISBN: 9780191595387 | DOI: http://dx.doi.org/10.1093/acprof:oso/9780199587148.003.0003

Series: Advanced Texts in Econometrics

Parametric nonlinear models

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In this chapter, a number of most commonly applied nonlinear time series models are being considered. As opposed to the previous chapter, these models do not generally have their origin in economic theory. Many of the models nest a linear model are therefore relatively easily interpretable. The models include regression models such as the smooth transition, switching regression and Markov switching models. They also include models based on rather general functional forms such as artificial neural network models and polynomial models. More rarely applied models such as bilinear or max‐min models are also mentioned. Models with stochastic coefficients also receive attention. Areas of application of these models to economic time series are briefly mentioned.

Keywords: artificial neural network; bilinear model; hidden Markov model; Kolmogorov‐Gabor polynomial; neural network; nonlinear autoregressive model; smooth transition regression; switching regression; threshold autoregression; time‐varying parameter model

Chapter.  12364 words. 

Subjects: Econometrics and Mathematical Economics

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