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Some Pitfalls in Smooth Transition Models Estimation: A Monte Carlo Study

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  • Novella Maugeri

Abstract

Nonlinear regime switching models are becoming increasingly popular in recent applied literature, as they allow capturing state-dependent behaviors which would be otherwise impossible to model. However, despite their popularity, the specification and estimation of these type of models is computationally complex and it is far from being a univocally solved issue.This paper aims at contributing to this debate. In particular, we use Monte Carlo experiments to assess whether employing the standard trick of ‘concentrating the sum of squares’ by Leybourne et al. (Journal of Time Series Analysis, 19(1): 83–97, 1998 ) in the application of nonlinear least squares to smooth transition models yields estimates with desirable asymptotic properties. Our results confirm that this procedure needs to be used with caution as it may yield biased and inconsistent estimates, especially when faced with small samples. Copyright Springer Science+Business Media New York 2014

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  • Novella Maugeri, 2014. "Some Pitfalls in Smooth Transition Models Estimation: A Monte Carlo Study," Computational Economics, Springer;Society for Computational Economics, vol. 44(3), pages 339-378, October.
    • Handle: RePEc:kap:compec:v:44:y:2014:i:3:p:339-378
    DOI: 10.1007/s10614-013-9395-6
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    3. Murat Midilic, 2016. "Estimation Of Star-Garch Models With Iteratively Weighted Least Squares," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 16/918, Ghent University, Faculty of Economics and Business Administration.
    4. Ahmet Faruk Aysan & Ibrahim Guney & Nicoleta Isac & Asad ul Islam Khan, 2022. "The probabilities of type I and II error of null of cointegration tests: A Monte Carlo comparison," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-15, January.
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    6. Kulaksizoglu, Tamer, 2015. "Unit Roots and Smooth Transitions: A Replication," MPRA Paper 61867, University Library of Munich, Germany.

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