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Forecasting with dimension switching VARs

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  • Koop, Gary

Abstract

This paper develops methods for VAR forecasting when the researcher is uncertain about which variables enter the VAR, and the dimension of the VAR may be changing over time. It considers the case where there are N variables which might potentially enter a VAR and the researcher is interested in forecasting N∗ of them. Thus, the researcher is faced with 2N−N∗ potential VARs. If N is large, conventional Bayesian methods can be infeasible due to the computational burden of dealing with a huge model space. Allowing for the dimension of the VAR to change over time only increases this burden. In light of these considerations, this paper uses computationally practical approximations adapted from the dynamic model averaging literature in order to develop methods for dynamic dimension selection (DDS) in VARs. We then show the benefits of DDS in a macroeconomic forecasting application. In particular, DDS switches between different parsimonious VARs and forecasts appreciably better than various small and large dimensional VARs.

Suggested Citation

  • Koop, Gary, 2014. "Forecasting with dimension switching VARs," International Journal of Forecasting, Elsevier, vol. 30(2), pages 280-290.
    • Handle: RePEc:eee:intfor:v:30:y:2014:i:2:p:280-290
    DOI: 10.1016/j.ijforecast.2013.09.005
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    References listed on IDEAS

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    1. Gary M. Koop, 2013. "Forecasting with Medium and Large Bayesian VARS," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 177-203, March.
    2. Marta Bańbura, 2008. "Large Bayesian VARs," 2008 Meeting Papers 334, Society for Economic Dynamics.
    3. Dimitris Korobilis, 2013. "Var Forecasting Using Bayesian Variable Selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 204-230, March.
    4. Geweke, John & Koop, Gary & van Dijk, Herman (ed.), 2011. "The Oxford Handbook of Bayesian Econometrics," OUP Catalogue, Oxford University Press, number 9780199559084, Decembrie.
    5. 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.
    6. 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.
    7. 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.
    8. Koop, Gary & Korobilis, Dimitris, 2013. "Large time-varying parameter VARs," Journal of Econometrics, Elsevier, vol. 177(2), pages 185-198.
    9. Giannone, Domenico & Lenza, Michele & Momferatou, Daphne & Onorante, Luca, 2014. "Short-term inflation projections: A Bayesian vector autoregressive approach," International Journal of Forecasting, Elsevier, vol. 30(3), pages 635-644.
    10. Carriero, A. & Kapetanios, G. & Marcellino, M., 2009. "Forecasting exchange rates with a large Bayesian VAR," International Journal of Forecasting, Elsevier, vol. 25(2), pages 400-417.
    11. Tatevik Sekhposyan & Barbara Rossi, 2008. "Has modelsí forecasting performance for US output growth and inflation changed over time, and when?," Working Papers 09-02, Duke University, Department of Economics.
    12. 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.
    13. 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.
    14. Rossi, Barbara & Sekhposyan, Tatevik, 2010. "Have economic models' forecasting performance for US output growth and inflation changed over time, and when?," International Journal of Forecasting, Elsevier, vol. 26(4), pages 808-835, October.
    15. Dimitris Korobilis, 2008. "Forecasting in vector autoregressions with many predictors," Advances in Econometrics, in: Bayesian Econometrics, pages 403-431, Emerald Group Publishing Limited.
    16. 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.
    17. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 821-852.
    18. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
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    Cited by:

    1. Gary Koop & Dimitris Korobilis, 2019. "Forecasting with High‐Dimensional Panel VARs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(5), pages 937-959, October.
    2. Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
    3. Chan, Joshua C.C. & Eisenstat, Eric & Koop, Gary, 2016. "Large Bayesian VARMAs," Journal of Econometrics, Elsevier, vol. 192(2), pages 374-390.
    4. Hwang, Youngjin, 2019. "Forecasting recessions with time-varying models," Journal of Macroeconomics, Elsevier, vol. 62(C).
    5. Guérin, Pierre & Leiva-Leon, Danilo, 2017. "Model averaging in Markov-switching models: Predicting national recessions with regional data," Economics Letters, Elsevier, vol. 157(C), pages 45-49.
    6. María Dolores Gadea-Rivas & Ana Gómez-Loscos & Danilo Leiva-Leon, 2017. "The evolution of regional economic interlinkages in Europe," Working Papers 1705, Banco de España.
    7. Drachal, Krzysztof, 2021. "Forecasting crude oil real prices with averaging time-varying VAR models," Resources Policy, Elsevier, vol. 74(C).
    8. Chan, Joshua C.C. & Eisenstat, Eric & Koop, Gary, 2014. "Large Bayesian VARMAs," 2007 Annual Meeting, July 29-August 1, 2007, Portland, Oregon TN 2015-06, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    9. Gadea-Rivas, María Dolores & Gómez-Loscos, Ana & Leiva-Leon, Danilo, 2019. "Increasing linkages among European regions. The role of sectoral composition," Economic Modelling, Elsevier, vol. 80(C), pages 222-243.
    10. Davidson, Sharada Nia, 2020. "Interdependence or contagion: A model switching approach with a focus on Latin America," Economic Modelling, Elsevier, vol. 85(C), pages 166-197.

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