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Density-Conditional Forecasts in Dynamic Multivariate Models

Author

Listed:
  • Andersson, Michael K.

    (Monetary Policy Department, Central Bank of Sweden)

  • Palmqvist, Stefan

    (Monetary Policy Department, Central Bank of Sweden)

  • Waggoner, Daniel F.

    (Research Department)

Abstract

When generating conditional forecasts in dynamic models it is common to impose the conditions as restrictions on future structural shocks. However, these conditional forecasts often ignore that there may be uncertainty about the future development of the restricted variables. Our paper therefore proposes a generalization such that the conditions can be given as the full distribution of the restricted variables. We demonstrate, in two empirical applications, that ignoring the uncertainty about the conditions implies that the distributions of the unrestricted variables are too narrow.

Suggested Citation

  • Andersson, Michael K. & Palmqvist, Stefan & Waggoner, Daniel F., 2010. "Density-Conditional Forecasts in Dynamic Multivariate Models," Working Paper Series 247, Sveriges Riksbank (Central Bank of Sweden).
    • Handle: RePEc:hhs:rbnkwp:0247
    as

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    File URL: http://www.riksbank.com/upload/Dokument_riksbank/Kat_publicerat/WorkingPapers/2010/wp247.pdf
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    References listed on IDEAS

    as
    1. Thompson, Patrick A & Miller, Robert B, 1986. "Sampling the Future: A Bayesian Approach to Forecasting from Univariate Time Series Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(4), pages 427-436, October.
    2. Juan F. Rubio-Ramírez & Daniel F. Waggoner & Tao Zha, 2010. "Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(2), pages 665-696.
    3. Junior Maih, 2010. "Conditional forecasts in DSGE models," Working Paper 2010/07, Norges Bank.
    4. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
    5. Daniel F. Waggoner & Tao Zha, 1999. "Conditional Forecasts In Dynamic Multivariate Models," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 639-651, November.
    6. 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.
    7. John Geweke, 1995. "Bayesian inference for linear models subject to linear inequality constraints," Working Papers 552, Federal Reserve Bank of Minneapolis.
    8. Marek Jarocinski & Frank Smets, 2008. "House prices and the stance of monetary policy," Review, Federal Reserve Bank of St. Louis, vol. 90(Jul), pages 339-366.
    9. Mattias Villani, 2009. "Steady-state priors for vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 630-650.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Tallman, Ellis W. & Zaman, Saeed, 2020. "Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy," International Journal of Forecasting, Elsevier, vol. 36(2), pages 373-398.
    2. Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 791-897, Elsevier.
    3. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian Vector Autoregressions," The Warwick Economics Research Paper Series (TWERPS) 1159, University of Warwick, Department of Economics.
    4. Knüppel, Malte & Schultefrankenfeld, Guido, 2019. "Assessing the uncertainty in central banks’ inflation outlooks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1748-1769.
    5. Michael W. McCracken & Joseph T. McGillicuddy & Michael T. Owyang, 2022. "Binary Conditional Forecasts," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1246-1258, June.
    6. Antolín-Díaz, Juan & Petrella, Ivan & Rubio-Ramírez, Juan F., 2021. "Structural scenario analysis with SVARs," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 798-815.
    7. repec:wrk:wrkemf:32 is not listed on IDEAS
    8. repec:hal:spmain:info:hdl:2441/27od5pb99881folvtfs8s3k16l is not listed on IDEAS
    9. Ganics, Gergely & Odendahl, Florens, 2021. "Bayesian VAR forecasts, survey information, and structural change in the euro area," International Journal of Forecasting, Elsevier, vol. 37(2), pages 971-999.
    10. repec:spo:wpmain:info:hdl:2441/27od5pb99881folvtfs8s3k16l is not listed on IDEAS
    11. Sokol, Andrej, 2021. "Fan charts 2.0: flexible forecast distributions with expert judgement," Working Paper Series 2624, European Central Bank.

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    More about this item

    Keywords

    Central Bank; Market Expectation; Restrictions; Uncertainty;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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