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 models.
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Bibliographic InfoPaper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 0302.
Date of creation: Jan 2003
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band pass filter; Gibbs sampler; Kalman filter; Markov chain Monte Carlo; state space; unobserved components;
Other versions of this item:
- Herman K. van Dijk & Andrew Harvey & Thomas Trimbur, 2004. "Cyclical components in economic time series: A Bayesian approach," Econometric Society 2004 Australasian Meetings 105, Econometric Society.
- 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
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- Kleijn, R.H. & van Dijk, H.K., 2003.
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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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