Testing for Nonlinearity in Mean in the Presence of Heteroskedasticity

Stan Hurn

No 348, Econometric Society 2004 Australasian Meetings from Econometric Society

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

Keywords: nonlinearity in mean; heteroskedasticity; wild bootstrap; empirical size and power (search for similar items in EconPapers)
JEL-codes: C12 C22 C52 (search for similar items in EconPapers)
Date: 2004-08-11
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-fin
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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http://repec.org/esAUSM04/up.5320.1080798165.pdf (application/pdf)

Related works:
Journal Article: Testing for Nonlinearity in Mean in the Presence of Heteroskedasticity (2009)
Working Paper: Testing for nonlinearity in mean in the presence of heteroskedasticity (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:ecm:ausm04:348

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