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Stock Prices and Economic Fluctuations: A Markov Switching Structural Vector Autoregressive Analysis

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  • Markku Lanne
  • Helmut Lütkepohl

Abstract

The role of expectations for economic fluctuations has received considerable attention in recent business cycle analysis. We exploit Markov regime switching models to identify shocks in cointegrated structural vector autoregressions and investigate different identification schemes for bi-variate systems comprising U.S. stock prices and total factor productivity. The former variable is viewed as reflecting expectations of economic agents about future productivity. It is found that some previously used identification schemes can be rejected in our model setup. The results crucially depend on the measure used for total factor productivity.

Suggested Citation

  • Markku Lanne & Helmut Lütkepohl, 2008. "Stock Prices and Economic Fluctuations: A Markov Switching Structural Vector Autoregressive Analysis," CESifo Working Paper Series 2407, CESifo.
    • Handle: RePEc:ces:ceswps:_2407
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    References listed on IDEAS

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    Cited by:

    1. Paul Beaudry & Franck Portier, 2014. "News-Driven Business Cycles: Insights and Challenges," Journal of Economic Literature, American Economic Association, vol. 52(4), pages 993-1074, December.
    2. Adél Bosch & Franz Ruch, 2013. "An Alternative Business Cycle Dating Procedure for South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 81(4), pages 491-516, December.
    3. Yukai Yang, 2014. "Testing Constancy of the Error Covariance Matrix in Vector Models against Parametric Alternatives using a Spectral Decomposition," CREATES Research Papers 2014-11, Department of Economics and Business Economics, Aarhus University.

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    More about this item

    Keywords

    cointegration; Markov regime switching model; vector error correction model; structural vector autoregression; mixed normal distribution;
    All these keywords.

    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

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