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The estimation uncertainty of permanent-transitory decompositions in cointegrated systems

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  • Sven Schreiber

    () (Macroeconomic Policy Institute (IMK) in the Hans Boeckler Foundation)

Abstract

The topic of this paper is the estimation uncertainty of the Stock-Watsonand Gonzalo-Granger permanent-transitory decompositions in the frameworkof the cointegrated vector-autoregression. Specifically, we suggest an approach to construct the confidence interval of the transitory component in agiven period (e.g. the latest observation) by conditioning on the observed datain that period. To calculate asymptotically valid confidence intervals we usethe delta method and two bootstrap variants. As an illustration we analyze theuncertainty of (US) output gap estimates in a system of output, consumption, and investment.

Suggested Citation

  • Sven Schreiber, 2011. "The estimation uncertainty of permanent-transitory decompositions in cointegrated systems," IMK Working Paper 3-2011, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
  • Handle: RePEc:imk:wpaper:3-2011
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    References listed on IDEAS

    as
    1. Hecq, Alain & Palm, Franz C & Urbain, Jean-Pierre, 2000. " Permanent-Transitory Decomposition in VAR Models with Cointegration and Common Cycles," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 62(4), pages 511-532, September.
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    More about this item

    Keywords

    transitory components; VECM; delta method; bootstrap;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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