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Trend-Cycle Decomposition: Implications from an Exact Structural Identification

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Listed:
  • Mardi Dungey
  • Jan P.A.M. Jacobs
  • Jing Jian
  • Simon van Norden

Abstract

A well-documented property of the Beveridge-Nelson trend-cycle decomposition is the perfect negative correlation between trend and cycle innovations. We show how this may be consistent with a structural model where trend shocks enter the cycle, or cycle shocks enter the trend and that identification restrictions are necessary to make this structural distinction. A reduced form unrestricted version such as Morley, Nelson and Zivot (2003) is compatible with either option, but cannot distinguish which is relevant. We discuss economic interpretations and implications using US real GDP data. Une caractéristique bien connue de la décomposition Beveridge-Nelson est la corrélation négative parfaite entre les innovations aux cycles et aux tendances. Nous montrons comment cette corrélation est compatible avec des modèles structurels où les chocs aux tendances entrent par les cycles, ou les chocs aux cycles entrent par les tendances et que des restrictions d'identification sont nécessaires pour faire cette distinction structurelle. Une forme réduite sans restriction comme celle de Morley, Nelson et Zivot (2003) est compatible avec les deux options, mais ne peut pas les distinguer. Nous discutons des interprétations économiques et les implications en utilisant des données réelles du PIB américain.

Suggested Citation

  • Mardi Dungey & Jan P.A.M. Jacobs & Jing Jian & Simon van Norden, 2013. "Trend-Cycle Decomposition: Implications from an Exact Structural Identification," CIRANO Working Papers 2013s-23, CIRANO.
  • Handle: RePEc:cir:cirwor:2013s-23
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    References listed on IDEAS

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    More about this item

    Keywords

    trend-cycle decomposition; data revision; state-space form;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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