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Testing for Zeros in the Spectrum of an Univariate Stationary Process: Part II

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  • Renaud Lacroix

Abstract

It is well-known that traditional inference do not apply when the spectral density of a stationary process vanishes for some frequency. This paper examines some properties of several new non parametric tests of this hypothesis which have been recently proposed by Lacroix (1999). These tests exploit the asymptotic behavior of the periodigram for some well-chosen sequence of frequencies. In particular, we investigate the power properties of the tests from both theoretical and empirical approach.

Suggested Citation

  • Renaud Lacroix, 1999. "Testing for Zeros in the Spectrum of an Univariate Stationary Process: Part II," Working papers 71, Banque de France.
    • Handle: RePEc:bfr:banfra:71
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    References listed on IDEAS

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    1. DeJong, David N. & Nankervis, John C. & Savin, N. E. & Whiteman, Charles H., 1992. "The power problems of unit root test in time series with autoregressive errors," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 323-343.
    2. M. B. Priestley, 1988. "The Spectral Analysis of Time Series," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 151(3), pages 573-574, May.
    3. Renaud Lacroix, 1999. "Testing for Zeros in the Spectrum of an Univariate Stationary Process: Part I," Working papers 70, Banque de France.
    4. Pentti Saikkonen & Ritva Luukkonen, 1996. "Testing The Order Of Differencing In Time Series Regression," Journal of Time Series Analysis, Wiley Blackwell, vol. 17(5), pages 481-496, September.
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    Keywords

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    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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