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Forecasting with Factor Models: A Bayesian Model Averaging Perspective

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  • Dimitris, Korobilis

Abstract

We use Bayesian factor regression models to construct a financial conditions index (FCI) for the U.S. Within this context we develop Bayesian model averaging methods that allow the data to select which variables should be included in the FCI or not. We also examine the importance of different sources of instability in the factors, such as stochastic volatility and structural breaks. Our results indicate that ignoring structural breaks in the loadings can be quite costly in terms of the forecasting performance of the FCI. Additionally, Bayesian model averaging can improve in specific cases the performance of the FCI, by means of discarding irrelevant financial variables during the estimation of the factor.

Suggested Citation

  • Dimitris, Korobilis, 2013. "Forecasting with Factor Models: A Bayesian Model Averaging Perspective," MPRA Paper 52724, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:52724
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    File URL: https://mpra.ub.uni-muenchen.de/52724/1/MPRA_paper_52724.pdf
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    References listed on IDEAS

    as
    1. Korobilis, Dimitris, 2013. "Hierarchical shrinkage priors for dynamic regressions with many predictors," International Journal of Forecasting, Elsevier, vol. 29(1), pages 43-59.
    2. Dimitris Korobilis, 2013. "Assessing the Transmission of Monetary Policy Using Time-varying Parameter Dynamic Factor Models-super-," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(2), pages 157-179, April.
    3. Dimitris Korobilis, 2008. "Forecasting in vector autoregressions with many predictors," Advances in Econometrics, in: Bayesian Econometrics, pages 403-431, Emerald Group Publishing Limited.
    4. Korobilis, Dimitris, 2013. "Bayesian forecasting with highly correlated predictors," Economics Letters, Elsevier, vol. 118(1), pages 148-150.
    5. Chib, Siddhartha, 1998. "Estimation and comparison of multiple change-point models," Journal of Econometrics, Elsevier, vol. 86(2), pages 221-241, June.
    6. Carvalho, Carlos M. & Chang, Jeffrey & Lucas, Joseph E. & Nevins, Joseph R. & Wang, Quanli & West, Mike, 2008. "High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1438-1456.
    7. Kimberly Beaton & René Lalonde & Corinne Luu, 2009. "A Financial Conditions Index for the United States," Discussion Papers 09-11, Bank of Canada.
    8. M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2006. "Forecasting Time Series Subject to Multiple Structural Breaks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 73(4), pages 1057-1084.
    9. William English & Kostas Tsatsaronis & Edda Zoli, 2005. "Assessing the predictive power of measures of financial conditions for macroeconomic variables," BIS Papers chapters, in: Bank for International Settlements (ed.), Investigating the relationship between the financial and real economy, volume 22, pages 228-52, Bank for International Settlements.
    10. Esteban Gómez & Andrés Murcia & Nancy Zamundio, 2011. "Financial Conditions Index: Early and Leading Indicator for Colombia," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 29(66), pages 174-220, December.
    11. Dimitris Korobilis, 2013. "Var Forecasting Using Bayesian Variable Selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 204-230, March.
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    15. Mr. Stephan Danninger & Ms. Irina Tytell & Mr. Ravi Balakrishnan & Mr. Selim A Elekdag, 2009. "The Transmission of Financial Stress from Advanced to Emerging Economies," IMF Working Papers 2009/133, International Monetary Fund.
    16. Jan Hatzius & Peter Hooper & Frederic S. Mishkin & Kermit L. Schoenholtz & Mark W. Watson, 2010. "Financial Conditions Indexes: A Fresh Look after the Financial Crisis," NBER Working Papers 16150, National Bureau of Economic Research, Inc.
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    More about this item

    Keywords

    financial stress; stochastic search variable selection; early-warning system; forecasting;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • G01 - Financial Economics - - General - - - Financial Crises
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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