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

Lutz Kilian and I. Bergean

Working Papers from Michigan - Center for Research on Economic & Social Theory

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.

Keywords: BUSINESS CYCLES; ECONOMIC MODELS; TIME SERIES (search for similar items in EconPapers)
JEL-codes: C30 C32 C52 E32 (search for similar items in EconPapers)
Pages: 38 pages
Date: 1999
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Journal Article: DATA-DRIVEN NONPARAMETRIC SPECTRAL DENSITY ESTIMATORS FOR ECONOMIC TIME SERIES: A MONTE CARLO STUDY (2002)
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Persistent link: https://EconPapers.repec.org/RePEc:fth:michet:99-04

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