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Localized level crossing random walk test robust to the presence of structural breaks

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  • Alexeev, Vitali
  • Maynard, Alex

Abstract

A modified version of the nonparametric level crossing random walk test is proposed, in which the crossing level is determined locally. This modification results in a test that is robust to unknown multiple structural breaks in the level and slope of the trend function under both the null and alternative hypotheses. No knowledge regarding the number or timing of the breaks is required. An algorithm is proposed to select the degree of localization in order to maximize bootstrapped power in a proximate model. A computational procedure is then developed to adjust the critical values for the effect of this selection procedure by replicating it under the null hypothesis. The test is applied to Canadian nominal inflation and nominal interest rate series with implications for the Fisher hypothesis.

Suggested Citation

  • Alexeev, Vitali & Maynard, Alex, 2012. "Localized level crossing random walk test robust to the presence of structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3322-3344.
  • Handle: RePEc:eee:csdana:v:56:y:2012:i:11:p:3322-3344
    DOI: 10.1016/j.csda.2010.06.026
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    Cited by:

    1. Harvey, David I. & Leybourne, Stephen J. & Taylor, A.M. Robert, 2014. "On infimum Dickey–Fuller unit root tests allowing for a trend break under the null," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 235-242.

    More about this item

    Keywords

    Level crossing; Random walk; Structural breaks; Unit root; Robustness;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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