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Testing for Nonlinearity in Mean in the Presence of Heteroskedasticity

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  • Stan Hurn

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

Suggested Citation

  • Stan Hurn, 2004. "Testing for Nonlinearity in Mean in the Presence of Heteroskedasticity," Econometric Society 2004 Australasian Meetings 348, Econometric Society.
  • Handle: RePEc:ecm:ausm04:348
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    References listed on IDEAS

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    Cited by:

    1. Carlo Altavilla & Paul De Grauwe, 2010. "Non-linearities in the relation between the exchange rate and its fundamentals," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(1), pages 1-21.
    2. Giulio Cainelli & Andrea Fracasso & Giuseppe Vittucci Marzetti, 2015. "Spatial agglomeration and productivity in Italy: A panel smooth transition regression approach," Papers in Regional Science, Wiley Blackwell, vol. 94, pages 39-67, November.

    More about this item

    Keywords

    nonlinearity in mean; heteroskedasticity; wild bootstrap; empirical size and power;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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