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

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  • Ionel Birgean
  • Lutz Kilian

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 (DGP) include autoregressive-moving average (ARMA) models, fractionally integrated ARMA 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

  • Ionel Birgean & Lutz Kilian, 2002. "Data-Driven Nonparametric Spectral Density Estimators For Economic Time Series: A Monte Carlo Study," Econometric Reviews, Taylor & Francis Journals, vol. 21(4), pages 449-476.
    • Handle: RePEc:taf:emetrv:v:21:y:2002:i:4:p:449-476
    DOI: 10.1081/ETC-120015386
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    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. Hirotugu Akaike, 1969. "Power spectrum estimation through autoregressive model fitting," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 21(1), pages 407-419, December.
    3. Francis X. Diebold & Lutz Kilian, 2001. "Measuring predictability: theory and macroeconomic applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(6), pages 657-669.
    4. Wouter J. Den Haan & Andrew T. Levin, 1995. "Inferences from parametric and non-parametric covariance matrix estimation procedures," International Finance Discussion Papers 504, Board of Governors of the Federal Reserve System (U.S.).
    5. Kaizô I. BeltraTo & Peter Bloomfield, 1987. "Determining The Bandwidth Of A Kernel Spectrum Estimate," Journal of Time Series Analysis, Wiley Blackwell, vol. 8(1), pages 21-38, January.
    6. Cogley, Timothy & Nason, James M, 1995. "Output Dynamics in Real-Business-Cycle Models," American Economic Review, American Economic Association, vol. 85(3), pages 492-511, June.
    7. N. Beamish & M. B. Priestley, 1981. "A Study of Autoregressive and Window Spectral Estimation," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 30(1), pages 41-58, March.
    8. Robinson, P M, 1991. "Automatic Frequency Domain Inference on Semiparametric and Nonparametric Models," Econometrica, Econometric Society, vol. 59(5), pages 1329-1363, September.
    9. King, Robert G & Watson, Mark W, 1996. "Money, Prices, Interest Rates and the Business Cycle," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 35-53, February.
    10. Geweke, John, 1993. "Forecasting time series with common seasonal patterns," Journal of Econometrics, Elsevier, vol. 55(1-2), pages 201-202.
    11. Otrok, Christopher & Ravikumar, B. & Whiteman, Charles H., 2002. "Habit formation: a resolution of the equity premium puzzle?," Journal of Monetary Economics, Elsevier, vol. 49(6), pages 1261-1288, September.
    12. Wouter J. den Haan & Andrew T. Levin, 2000. "Robust Covariance Matrix Estimation with Data-Dependent VAR Prewhitening Order," NBER Technical Working Papers 0255, National Bureau of Economic Research, Inc.
    13. Jeremy Berkowitz & Lutz Kilian, 2000. "Recent developments in bootstrapping time series," Econometric Reviews, Taylor & Francis Journals, vol. 19(1), pages 1-48.
    14. Collard, Fabrice, 1998. "Spectral and persistence properties of cyclical growth," Journal of Economic Dynamics and Control, Elsevier, vol. 23(3), pages 463-488, November.
    15. Paul Newbold & Stephen J. Leybourne (ed.), 2003. "Recent Developments in Time Series," Books, Edward Elgar Publishing, volume 0, number 2674.
    16. Canova, Fabio, 1992. "An Alternative Approach to Modeling and Forecasting Seasonal Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(1), pages 97-108, January.
    17. Otrok, Christopher, 2001. "Spectral Welfare Cost Functions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 42(2), pages 345-367, May.
    18. Froeb, Luke & Koyak, Robert, 1994. "Measuring and comparing smoothness in time series the production smoothing hypothesis," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 97-122.
    19. repec:wop:calsdi:96-17 is not listed on IDEAS
    20. Wouter Denhaan & Andrew T. Levin, 1996. "VARHAC Covariance Matrix Estimator (GAUSS)," QM&RBC Codes 64, Quantitative Macroeconomics & Real Business Cycles.
    21. Canova, Fabio, 1993. "Forecasting time series with common seasonal patterns," Journal of Econometrics, Elsevier, vol. 55(1-2), pages 173-200.
    22. Geweke, John F, 1986. "The Superneutrality of Money in the United States: An Interpretation of the Evidence," Econometrica, Econometric Society, vol. 54(1), pages 1-21, January.
    23. Diebold, Francis X. & Rudebusch, Glenn D., 1989. "Long memory and persistence in aggregate output," Journal of Monetary Economics, Elsevier, vol. 24(2), pages 189-209, September.
    24. Wright, Jonathan H., 1999. "Frequency domain inference for univariate impulse responses," Economics Letters, Elsevier, vol. 63(3), pages 269-277, June.
    25. Paparoditis, Efstathios, 1996. "Bootstrapping Autoregressive and Moving Average Parameter Estimates of Infinite Order Vector Autoregressive Processes," Journal of Multivariate Analysis, Elsevier, vol. 57(2), pages 277-296, May.
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    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.
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    More about this item

    Keywords

    Business cycle measurement; Model identification; Periodogram smoothing; Autocovariance smoothing; Autoregressive sieve; Bandwidth selection;
    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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