DATA-DRIVEN NONPARAMETRIC SPECTRAL DENSITY ESTIMATORS FOR ECONOMIC TIME SERIES: A MONTE CARLO STUDY

Ionel Birgean and Lutz Kilian

Econometric Reviews, 2002, vol. 21, issue 4, 449-476

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.

Keywords: Business cycle measurement; Model identification; Periodogram smoothing; Autocovariance smoothing; Autoregressive sieve; Bandwidth selection (search for similar items in EconPapers)
Date: 2002
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Working Paper: Data-Driven Nonparametric Spectral Density Estimators for Economic Time Series: A Monte Carlo Study (1999)
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DOI: 10.1081/ETC-120015386

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