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Testing for Nonlinearity in Mean in the Presence of Heteroskedasticity

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Author Info
Stan Hurn

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Abstract

This paper considers an important practical problem in testing time-series data for nonlinearity in mean. Most popular tests reject the null hypothesis of linearity too frequently if the the data are heteroskedastic. Two approaches to redressing this size distortion are considered, both of which have been proposed previously in the literature although not in relation to this particular problem. These are the heteroskedasticity-robust-auxiliary-regression approach and the wild bootstrap. Simulation results indicate that both approaches are effective in reducing the size distortion and that the wild bootstrap offers better performance in smaller samples. Two practical examples are then used to illustrate the procedures and demonstrate the dangers of using non-robust tests

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Paper provided by Econometric Society in its series Econometric Society 2004 Australasian Meetings with number 348.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:ausm04:348

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Related research
Keywords: nonlinearity in mean; heteroskedasticity; wild bootstrap; empirical size and power;

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Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing

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  2. G. S. Hongyi Li, 1996. "Bootstrapping time series models," Econometric Reviews, Taylor and Francis Journals, vol. 15(2), pages 115-158. [Downloadable!] (restricted)
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  16. GONÇALVES, Silvia & WHITE, Halbert, 2001. "The Bootstrap of Mean for Dependent Heterogeneous Arrays," Cahiers de recherche 2001-19, Universite de Montreal, Departement de sciences economiques. [Downloadable!]
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  19. W. A. Broock & J. A. Scheinkman & W. D. Dechert & B. LeBaron, 1996. "A test for independence based on the correlation dimension," Econometric Reviews, Taylor and Francis Journals, vol. 15(3), pages 197-235. [Downloadable!] (restricted)
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