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

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Author Info
Ionel Birgean
Lutz Kilian

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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.

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File URL: http://www.informaworld.com/openurl?genre=article&doi=10.1081/ETC-120015386&magic=repec&7C&7C8674ECAB8BB840C6AD35DC6213A474B5
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Publisher Info
Article provided by Taylor and Francis Journals in its journal Econometric Reviews.

Volume (Year): 21 (2002)
Issue (Month): 4 ()
Pages: 449-476
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Handle: RePEc:taf:emetrv:v:21:y:2002:i:4:p:449-476

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Related research
Keywords: Business cycle measurement; Model identification; Periodogram smoothing; Autocovariance smoothing; Autoregressive sieve; Bandwidth selection;

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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Jeremy Berkowitz & Ionel Birgean & Lutz Kilian, 1999. "On the finite-sample accuracy of nonparametric resampling algorithms for economic time series," Finance and Economics Discussion Series 1999-04, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
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