The relationship between the Beveridge-Nelson decomposition and other permanent-transitory decompositions that are popular in economics

DOI10.1016/j.jeconom.2008.08.021zbMath1429.62408OpenAlexW2041101172MaRDI QIDQ299212

Eric Zivot, Kum Hwa Oh, Drew D. Creal

Publication date: 22 June 2016

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

Full work available at URL: https://doi.org/10.1016/j.jeconom.2008.08.021

zbMATH Keywords

Kalman filterstate-space modelspermanent-transitory decompositiontrend-cycle decompositionunobserved components time series model

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Economic time series analysis (91B84)

Related Items (9)

What are the Differences in Trend Cycle Decompositions by Beveridge and Nelson and by Unobserved Component Models? ⋮ The multivariate Beveridge-Nelson decomposition with I(1) and I(2) series ⋮ Trend and cycle decomposition of Markov switching (co)integrated time series ⋮ Optimal signal extraction with correlated components ⋮ Bayesian analysis of structural correlated unobserved components and identification via heteroskedasticity ⋮ Bayesian multivariate Beveridge-Nelson decomposition of I(1) and I(2) series with cointegration ⋮ The estimation uncertainty of permanent-transitory decompositions in co-integrated systems ⋮ Predictability, real time estimation, and the formulation of unobserved components models ⋮ The Multistep Beveridge–Nelson Decomposition

Uses Software

• Ox
• SsfPack
• StFinMetrics

Cites Work

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• Trends and cycles in economic time series: a Bayesian approach
• Computation of the Beveridge--Nelson decomposition for multivariate economic time series
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• Modeling Financial Time Series with S-PLUS®
• Properties of higher order stochastic cycles
• An ARIMA-Model-Based Approach to Seasonal Adjustment
• The Relative Importance of Permanent and Transitory Components: Identification and Some Theoretical Bounds
• Estimation and Prediction for a Class of Dynamic Nonlinear Statistical Models
• Statistical algorithms for models in state space using SsfPack 2.2
• Signal extraction and the formulation of unobserved components models
• BEVERIDGE-NELSON-TYPE TRENDS FOR I(2) AND SOME SEASONAL MODELS
• Are Output Fluctuations Transitory?
• Trend–Cycle Decompositions with Correlated Components

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