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BVAR mapping

Author

Listed:
  • Demeshev, Boris

    () (National Research University Higher School of Economics, Moscow, Russian Federation;)

  • Malakhovskaya, Oxana

    () (National Research University Higher School of Economics, Moscow, Russian Federation;)

Abstract

This paper reviews estimation and forecasting with Bayesian vector autoregressions (BVARs). In the first part of the paper, we propose a clear classification of the most frequently used prior distributions and we show how the parameters of posterior distributions can be computed for the priors we consider in the paper. A separate section describes the endogenous choice of prior hyperparameters that is currently a key step to estimate a BVAR in a data-rich environment. The second part of this paper is devoted to forecasting with BVARs. We review both point and density forecasting.

Suggested Citation

  • Demeshev, Boris & Malakhovskaya, Oxana, 2016. "BVAR mapping," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 43, pages 118-141.
    • Handle: RePEc:ris:apltrx:0300
    as

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    File URL: http://pe.cemi.rssi.ru/pe_2016_43_118-141.pdf
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    References listed on IDEAS

    as
    1. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    2. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    3. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
    4. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2008. "Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?," Journal of Econometrics, Elsevier, vol. 146(2), pages 318-328, October.
    5. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Bayesian VARs: Specification Choices and Forecast Accuracy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 46-73, January.
    6. John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, issue Q1, pages 4-18.
    7. Sims, Christopher A & Zha, Tao, 1998. "Bayesian Methods for Dynamic Multivariate Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-968, November.
    8. Kadiyala, K Rao & Karlsson, Sune, 1997. "Numerical Methods for Estimation and Inference in Bayesian VAR-Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(2), pages 99-132, March-Apr.
    9. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    10. Slutskin, Lev, 2010. "Bayesian analysis in the case of an estimated parameter following a stochastic process," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 20(4), pages 119-131.
    11. Favero, Carlo A., 2001. "Applied Macroeconometrics," OUP Catalogue, Oxford University Press, number 9780198296850.
    12. Bauwens, Luc & Lubrano, Michel & Richard, Jean-Francois, 2000. "Bayesian Inference in Dynamic Econometric Models," OUP Catalogue, Oxford University Press, number 9780198773139.
    13. Aivazian, Sergei, 2008. "Bayesian Methods in Econometrics," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 9(1), pages 93-130.
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    Cited by:

    1. repec:scn:financ:y:2018:i:3:p:136-154 is not listed on IDEAS
    2. repec:scn:guhrje:2017_4_04 is not listed on IDEAS

    More about this item

    Keywords

    BVAR; prior distributions; point forecasting; density forecasting;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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