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

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Author Info
Kilian, L.
Bergean, I.

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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 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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Publisher Info
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

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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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Related research
Keywords: BUSINESS CYCLES ; ECONOMIC MODELS ; TIME SERIES;

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Find related papers by 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
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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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!]
    Other versions:
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