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Data-Driven Nonparametric Spectral Density Estimators for Economic Time Series: A Monte Carlo Study

Author

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  • Kilian, L.
  • Bergean, I.

Abstract

Spectral analysis at frequencies other than zero plays an increasingly important role in econometrics. A number of alternative automated data-driven procedures for nonparametric spectral density estimation have been suggested in the literature, but little is known about their finite-sample accuracy. We compare five such procedures in terms of their mean-squared percentage error across frequencies. Our data generating processes include autoregressive-moving average models and nonparametric models based on 16 commonly used macroeconomic time series. We find that for both quarterly and monthly data the autoregressive sieve estimator is the most reliable method overall.

Suggested Citation

  • Kilian, L. & Bergean, I., 1999. "Data-Driven Nonparametric Spectral Density Estimators for Economic Time Series: A Monte Carlo Study," Papers 99-04, Michigan - Center for Research on Economic & Social Theory.
    • Handle: RePEc:fth:michet:99-04
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    Cited by:

    1. Berkowitz, J. & Birgean, I. & Kilian, L., 1999. "On the Finite-Sample Accuracy of Nonparametric Resampling Algorithms for Economic Time Series," Papers 99-01, Michigan - Center for Research on Economic & Social Theory.
    2. Castro, Tomás del Barrio & Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2013. "The Impact Of Persistent Cycles On Zero Frequency Unit Root Tests," Econometric Theory, Cambridge University Press, vol. 29(6), pages 1289-1313, December.
    3. Jiang, Shangrong & Li, Yuze & Lu, Quanying & Wang, Shouyang & Wei, Yunjie, 2022. "Volatility communicator or receiver? Investigating volatility spillover mechanisms among Bitcoin and other financial markets," Research in International Business and Finance, Elsevier, vol. 59(C).

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    More about this item

    Keywords

    BUSINESS CYCLES ; ECONOMIC MODELS ; TIME SERIES;
    All these keywords.

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - 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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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