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Inference for Noisy Long Run Component Process

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  • Gourieroux, Christian
  • Jasiak, Joann

Abstract

This paper introduces a new approach to the modelling of a stationary long run component, which is an autoregressive process with near unit root and small sigma innovation. We show that a combination of a noise and a long run component can explain the long run predictability puzzle pointed out in Fama-French (1988). Moreover in the presence of a long run component, spurious regressions arise and misleading long run predictions are obtained when standard statistical approaches are applied

Suggested Citation

  • Gourieroux, Christian & Jasiak, Joann, 2010. "Inference for Noisy Long Run Component Process," MPRA Paper 98987, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:98987
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    References listed on IDEAS

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    More about this item

    Keywords

    Long Run; Predictability Puzzle; Weak Identification; Deconvolution; Term Structure; Near Unit Root; Small Sigma.;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • E0 - Macroeconomics and Monetary Economics - - General

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