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

  • Kilian, L.
  • Bergean, I.

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.

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Paper provided by Michigan - Center for Research on Economic & Social Theory in its series Papers with number 99-04.

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Length: 38 pages
Date of creation: 1999
Date of revision:
Handle: RePEc:fth:michet:99-04
Contact details of provider: Postal: UNIVERSITY OF MICHIGAN, DEPARTMENT OF ECONOMICS CENTER FOR RESEARCH ON ECONOMIC AND SOCIAL THEORY, ANN ARBOR MICHIGAN U.S.A.

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  11. 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.
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  16. 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.
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