Cyclical components in economic time series: A Bayesian approach
AbstractCyclical components in economic time series are analysed in a Bayesian framework, thereby allowing prior notions about periodicity to be used. The method is based on a general class of unobserved component models that allow relatively smooth cycles to be extracted. Posterior densities of parameters and smoothed cycles are obtained using Markov chain Monte Carlo methods. An application to estimating business cycles in macroeconomic series illustrates the viability of the procedure for both univariate and bivariate mode
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Bibliographic InfoPaper provided by Econometric Society in its series Econometric Society 2004 Australasian Meetings with number 105.
Date of creation: 11 Aug 2004
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Band pass filter; Markov Chain Monte Carlo; State Space Model;
Other versions of this item:
- Harvey, A. & TTrimbur, T. & van Dijk, H., 2003. "Cyclical Components in Economic Time Series: a Bayesian Approach," Cambridge Working Papers in Economics 0302, Faculty of Economics, University of Cambridge.
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
This paper has been announced in the following NEP Reports:
- NEP-ALL-2004-08-16 (All new papers)
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Econometric Institute Research Papers
EI 2003-48, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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