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On the Phase Dependence in Time-Varying Correlations Between Time-Series

Author

Listed:
  • Francisco Blasques

    (VU University Amsterdam)

Abstract

This paper proposes the use of a double correlation coefficient as a nonpara- metric measure of phase-dependence in time-varying correlations. An asymp- totically Gaussian test statistic for the null hypothesis of no phase-dependence is derived from the proposed measure. Finite-sample distributions, power and size are analyzed in a Monte-Carlo exercise. An application of this test provides evidence that correlation strength between major macroeconomic aggregates is both time-varying and phase dependent in the business cycle.

Suggested Citation

  • Francisco Blasques, 2013. "On the Phase Dependence in Time-Varying Correlations Between Time-Series," Tinbergen Institute Discussion Papers 13-054/III, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20130054
    as

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    File URL: https://papers.tinbergen.nl/13054.pdf
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    References listed on IDEAS

    as
    1. Kydland, Finn E & Prescott, Edward C, 1982. "Time to Build and Aggregate Fluctuations," Econometrica, Econometric Society, vol. 50(6), pages 1345-1370, November.
    2. David N. DeJong & Chetan Dave, 2007. "Introduction to Structural Macroeconometrics," Introductory Chapters, in: Structural Macroeconometrics, Princeton University Press.
    3. Engle, Robert F, 2000. "Dynamic Conditional Correlation - A Simple Class of Multivariate GARCH Models," University of California at San Diego, Economics Working Paper Series qt56j4143f, Department of Economics, UC San Diego.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    nonparametric; phase-dependence; time-varying correlation;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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