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Testing for Nonlinearity in Mean in the Presence of Heteroskedasticity Author info | Abstract | Publisher info | Download info | Related research | Statistics Stan Hurn
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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 2004Date of revision:
Handle: RePEc:ecm:ausm04:348Contact details of provider: Phone: 1 212 998 3820 Fax: 1 212 995 4487 Email: Web page: http://www.econometricsociety.org/pastmeetings.asp More information through EDIRC
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Keywords: nonlinearity in mean ; heteroskedasticity ; wild bootstrap ; empirical size and power ; Other versions of this item:
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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