Conference paper

Publication year: 2008

Journal: Studies in Nonlinear Dynamics & Econometrics

Volume number: 12

Issue number: 1

ISSN: 1081-1826

eISSN: 1558-3708

Conference: Conference on Nonlinear Dynamical Methods and Time Series Analysis

Publisher: De Gruyter

Abstract: 
It has been shown in the literature that the task of estimating the parameters of nonlinear models may be tackled with optimization heuristics. Thus, we attempt to carry these intuitions over to the estimation procedure of smooth transition autoregressive (STAR, Terasvirta, 1994) models by introducing the following three stochastic optimization algorithms: Simulated Annealing, (Kirkpatrick, Gelatt, and Vecchi, 1983), Threshold Accepting (Dueck and Scheuer, 1990) and Differential Evolution (Storn and Price, 1995, 1997). Besides considering the performance of these heuristics in estimating STAR model parameters, our paper additionally picks up the problem of identifying redundant parameters which, according to our view, has not been addressed in a satisfactory way by now. The resulting findings of our simulation studies seem to argue for an implementation of heuristic approaches within the STAR modeling cycle. In particular for the case of STAR model specification, an application of these heuristics might offer valuable information to empirical researchers.

Harvard Citation style: Maringer, D. and Meyer, M. (2008) Smooth transition autoregressive models - New approaches to the model selection problem, Studies in Nonlinear Dynamics & Econometrics, 12(1), Article 5

APA Citation style: Maringer, D., & Meyer, M. (2008). Smooth transition autoregressive models - New approaches to the model selection problem. Studies in Nonlinear Dynamics & Econometrics. 12(1), Article 5.