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

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  • Markku Lanne
  • Helmut Luetkepohl

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

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    1. Bénédicte Vidaillet & V. D'Estaintot & P. Abécassis, 2005. "Introduction," Post-Print hal-00287137, HAL.
    2. Nir Jaimovich & Sergio Rebelo, 2009. "Can News about the Future Drive the Business Cycle?," American Economic Review, American Economic Association, vol. 99(4), pages 1097-1118, September.
    3. Paul Beaudry & Franck Portier, 2006. "Stock Prices, News, and Economic Fluctuations," American Economic Review, American Economic Association, vol. 96(4), pages 1293-1307, September.
    4. Christiano, Lawrence J. & Eichenbaum, Martin & Evans, Charles L., 1999. "Monetary policy shocks: What have we learned and to what end?," Handbook of Macroeconomics,in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 2, pages 65-148 Elsevier.
    5. Beaudry, Paul & Portier, Franck, 2005. "The "news view" of economic fluctuations: Evidence from aggregate Japanese data and sectoral US data," Journal of the Japanese and International Economies, Elsevier, vol. 19(4), pages 635-652, December.
    6. John B. Taylor, 1999. "Monetary Policy Rules," NBER Books, National Bureau of Economic Research, Inc, number tayl99-1, January.
    7. Lucke, Bernd & Haertel, Thomas, 2008. "Do News Shocks Drive Business Cycles? Evidence from German Data," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 2, pages 1-21.
    8. Lanne, Markku & Lütkepohl, Helmut, 2010. "Structural Vector Autoregressions With Nonnormal Residuals," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 159-168.
    9. Guido Lorenzoni, 2009. "A Theory of Demand Shocks," American Economic Review, American Economic Association, vol. 99(5), pages 2050-2084, December.
    10. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
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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. YANG, Yukai, 2014. "Testing constancy of the error covariance matrix in vector models against parametric alternatives using a spectral decomposition," CORE Discussion Papers 2014017, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. 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.

    More about this item

    Keywords

    cointegration; Markov regime switching model; vector error correction model; structural vector autoregression; mixed normal distribution;

    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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