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Baynesian Leading Indicators: Measuring and Predicting Economic Conditions

Author

Listed:
  • Christopher Otrok

    (Univ. of Iowa)

  • Charles H. Whiteman

    (Univ. of Iowa)

Abstract

This paper designs and implements a Bayesian dynamic latent factor model for a vector of data describing the Iowa economy. Posterior distributions of parameters and the latent factor are analyzed by Markov Chain Monte Carlo methods, and coincident and leading indicators are given by posterior mean values of current and predictive distributions for the latent factor.

Suggested Citation

  • Christopher Otrok & Charles H. Whiteman, 1996. "Baynesian Leading Indicators: Measuring and Predicting Economic Conditions," Macroeconomics 9610002, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpma:9610002
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    References listed on IDEAS

    as
    1. Chib, Siddhartha & Greenberg, Edward, 1994. "Bayes inference in regression models with ARMA (p, q) errors," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 183-206.
    2. John Geweke, "undated". "Posterior Simulators in Econometrics," Computing in Economics and Finance 1996 _019, Society for Computational Economics.
    3. Chib, Siddhartha & Greenberg, Edward, 1996. "Markov Chain Monte Carlo Simulation Methods in Econometrics," Econometric Theory, Cambridge University Press, vol. 12(3), pages 409-431, August.
    4. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, March.
    5. Geweke, John, 1996. "Monte carlo simulation and numerical integration," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 15, pages 731-800, Elsevier.
    6. James H. Stock & Mark W. Watson, 1993. "Business Cycles, Indicators, and Forecasting," NBER Books, National Bureau of Economic Research, Inc, number stoc93-1, March.
    7. Stock, James H. & Watson, Mark W. (ed.), 1993. "Business Cycles, Indicators, and Forecasting," National Bureau of Economic Research Books, University of Chicago Press, edition 1, number 9780226774886, December.
    8. Thomas J. Sargent & Christopher A. Sims, 1977. "Business cycle modeling without pretending to have too much a priori economic theory," Working Papers 55, Federal Reserve Bank of Minneapolis.
    9. repec:cup:etheor:v:12:y:1996:i:3:p:409-31 is not listed on IDEAS
    10. James H. Stock & Mark W. Watson, 1992. "A procedure for predicting recessions with leading indicators: econometric issues and recent performance," Working Paper Series, Macroeconomic Issues 92-7, Federal Reserve Bank of Chicago.
    11. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409, National Bureau of Economic Research, Inc.
    12. James H. Stock & Mark W. Watson, 1993. "A Procedure for Predicting Recessions with Leading Indicators: Econometric Issues and Recent Experience," NBER Chapters, in: Business Cycles, Indicators, and Forecasting, pages 95-156, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Fuentes-Albero, Cristina & Melosi, Leonardo, 2013. "Methods for computing marginal data densities from the Gibbs output," Journal of Econometrics, Elsevier, vol. 175(2), pages 132-141.
    2. Ritschl, Albrecht & Sarferaz, Samad, 2009. "Crisis? What Crisis? Currency vs. Banking in the Financial Crisis of 1931," CEPR Discussion Papers 7610, C.E.P.R. Discussion Papers.
    3. Yong Li & Zeng Tao & Jun Yu, "undated". "Robust Deviance Information Criterion for Latent Variable Models," Working Papers CoFie-04-2012, Singapore Management University, Sim Kee Boon Institute for Financial Economics.

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