Trends and cycles in economic time series: a Bayesian approach

DOI10.1016/j.jeconom.2006.07.006zbMath1247.91149OpenAlexW1978475619MaRDI QIDQ451267

Andrew C. Harvey, Thomas M. Trimbur, Hermann K. Van Dijk

Publication date: 23 September 2012

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://repub.eur.nl/pub/6913/EI2005-27.pdf

zbMATH Keywords

Markov chain Monte CarloKalman filterturning pointsunobserved componentsoutput gapreal-time estimation

Mathematics Subject Classification ID

Economic time series analysis (91B84)

Related Items (13)

The relationship between the Beveridge-Nelson decomposition and other permanent-transitory decompositions that are popular in economics ⋮ The information content of capacity utilization for detrending total factor productivity ⋮ Modelled approximations to the ideal filter with application to GDP and its components ⋮ Reconciling output gaps: unobserved components model and Hodrick-Prescott filter ⋮ Detecting time variation in the price puzzle: a less informative prior choice for time varying parameter VAR models ⋮ On the Discretization of Continuous-Time Filters for Nonstationary Stock and Flow Time Series ⋮ Efficient Bayesian estimation of multivariate state space models ⋮ Multivariate time series analysis from a Bayesian machine learning perspective ⋮ The local quadratic trend model ⋮ Inference for the Hyperparameters of Structural Models Under Classical and Bayesian Perspectives: A Comparison Study ⋮ Bayesian non-parametric signal extraction for Gaussian time series ⋮ Bayesian estimation of an extended local scale stochastic volatility model ⋮ Unnamed Item

Uses Software

• Ox
• SsfPack

Cites Work

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• Effects of the Hodrick-Prescott filter on trend and difference stationary time series
• Testing for integration using evolving trend and seasonals models: A Bayesian approach.
• Bayesian Inference on Periodicities and Component Spectral Structure in Time Series
• Properties of higher order stochastic cycles
• DATA AUGMENTATION AND DYNAMIC LINEAR MODELS
• On Gibbs sampling for state space models
• Computing Bayes Factors by Combining Simulation and Asymptotic Approximations
• A simple and efficient simulation smoother for state space time series analysis
• The simulation smoother for time series models
• Bayes Factors
• Priors and Component Structures in Autoregressive Time Series Models
• Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)

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