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System Priors for Econometric Time Series

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  • Michal Andrle
  • Miroslav Plasil

Abstract

This paper introduces "system priors" into Bayesian analysis of econometric time series and provides a simple and illustrative application. Unlike priors on individual parameters, system priors offer a simple and efficient way of formulating well-defined and economically meaningful priors about model properties that determine the overall behavior of the model. The generality of system priors is illustrated using an AR(2) process with a prior that its dynamics comes mostly from business-cycle frequencies.

Suggested Citation

  • Michal Andrle & Miroslav Plasil, 2017. "System Priors for Econometric Time Series," Working Papers 2017/01, Czech National Bank.
    • Handle: RePEc:cnb:wpaper:2017/01
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    References listed on IDEAS

    as
    1. Michal Andrle & Mr. Jaromir Benes, 2013. "System Priors: Formulating Priors about DSGE Models' Properties," IMF Working Papers 2013/257, International Monetary Fund.
    2. Joshua C. C. Chan & Gary Koop & Simon M. Potter, 2016. "A Bounded Model of Time Variation in Trend Inflation, Nairu and the Phillips Curve," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(3), pages 551-565, April.
    3. Clark, Todd E. & Doh, Taeyoung, 2014. "Evaluating alternative models of trend inflation," International Journal of Forecasting, Elsevier, vol. 30(3), pages 426-448.
    4. Planas, Christophe & Rossi, Alessandro & Fiorentini, Gabriele, 2008. "Bayesian Analysis of the Output Gap," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 18-32, January.
    5. Andrew C. Harvey & Thomas M. Trimbur, 2003. "General Model-Based Filters for Extracting Cycles and Trends in Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 244-255, May.
    6. Berger, Tino & Everaert, Gerdie & Vierke, Hauke, 2016. "Testing for time variation in an unobserved components model for the U.S. economy," Journal of Economic Dynamics and Control, Elsevier, vol. 69(C), pages 179-208.
    7. John Geweke, 2010. "Complete and Incomplete Econometric Models," Economics Books, Princeton University Press, edition 1, number 9218.
    8. Marco Del Negro & Frank Schorfheide, 2004. "Priors from General Equilibrium Models for VARS," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(2), pages 643-673, May.
    9. Kuttner, Kenneth N, 1994. "Estimating Potential Output as a Latent Variable," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 361-368, July.
    10. Edward Herbst & Frank Schorfheide, 2014. "Sequential Monte Carlo Sampling For Dsge Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1073-1098, November.
    11. Mattias Villani, 2009. "Steady-state priors for vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 630-650.
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    Cited by:

    1. Jarociński, Marek & Marcet, Albert, 2019. "Priors about observables in vector autoregressions," Journal of Econometrics, Elsevier, vol. 209(2), pages 238-255.
    2. Bruno Perdigão, 2019. "“Still" an Agnostic Procedure to Identify Monetary Policy Shocks with Sign Restrictions," Working Papers Series 494, Central Bank of Brazil, Research Department.
    3. Milan Szabo & Zlatuse Komarkova & Martin Casta, 2020. "Vulnerable growth: Bayesian GDP-at-Risk," Occasional Publications - Chapters in Edited Volumes,, Czech National Bank.

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    More about this item

    Keywords

    Bayesian analysis; system priors; time series;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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