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The comparison of optimization algorithms on unit root testing with smooth transition

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  • Omay, Tolga

Abstract

The aim of this study is to search for a better optimization algorithm in applying unit root tests that inherit nonlinear models in the testing process. The algorithms analyzed include Broyden, Fletcher, Goldfarb and Shanno (BFGS), Gauss-Jordan, Simplex, Genetic, and Extensive Grid-Search. The simulation results indicate that the derivative free methods, such as Genetic and Simplex, have advantages over hill climbing methods, such as BFGS and Gauss-Jordan, in obtaining accurate critical values for the Leybourne, Newbold and Vougos (1996, 1998) (LNV) and Sollis (2004) unit root tests. Moreover, when parameters are estimated under the alternative hypothesis of the LNV type of unit root tests the derivative free methods lead to an unbiased and efficient estimator as opposed to those obtained from other algorithms. Finally, the empirical analyses show that the derivative free methods, hill climbing and simple grid search can be used interchangeably when testing for a unit root since all three optimization methods lead to the same empirical test results.

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  • Omay, Tolga, 2012. "The comparison of optimization algorithms on unit root testing with smooth transition," MPRA Paper 42129, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:42129
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    Cited by:

    1. Tolga Omay & Dilem Yildirim, 2014. "Nonlinearity and Smooth Breaks in Unit Root Testing," Econometrics Letters, Bilimsel Mektuplar Organizasyonu (Scientific letters), vol. 1(1), pages 1-9.
    2. Tolga Omay & Furkan Emirmahmutoglu & Mubariz Hasanov, 2018. "Structural break, nonlinearity and asymmetry: a re-examination of PPP proposition," Applied Economics, Taylor & Francis Journals, vol. 50(12), pages 1289-1308, March.

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    More about this item

    Keywords

    Nonlinear trend; Deterministic smooth transition; Structural change; Estimation methods;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

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