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Bayesian forecasting with highly correlated predictors

Author

Listed:
  • Dimitris Korobilis

Abstract

This paper considers Bayesian variable selection in regressions with a large number of possibly highly correlated macroeconomic predictors. I show that by acknowledging the correlation structure in the predictors can improve forecasts over existing popular Bayesian variable selection algorithms.

Suggested Citation

  • Dimitris Korobilis, 2012. "Bayesian forecasting with highly correlated predictors," Working Papers 2012_12, Business School - Economics, University of Glasgow.
    • Handle: RePEc:gla:glaewp:2012_12
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    Cited by:

    1. Dimitris, Korobilis, 2013. "Forecasting with Factor Models: A Bayesian Model Averaging Perspective," MPRA Paper 52724, University Library of Munich, Germany.
    2. Goodness C. Aye & Stephen M. Miller & Rangan Gupta & Mehmet Balcilar, 2016. "Forecasting US real private residential fixed investment using a large number of predictors," Empirical Economics, Springer, vol. 51(4), pages 1557-1580, December.
    3. Gary Koop, 2012. "Using VARs and TVP-VARs with Many Macroeconomic Variables," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 4(3), pages 143-167, September.
    4. repec:ipg:wpaper:2014-465 is not listed on IDEAS
    5. Konstantakis, Konstantinos & Michaelides, Panayotis G., 2014. "Combining Input-Output (IO) analysis with Global Vector Autoregressive (GVAR) modeling: Evidence for the USA (1992-2006)," MPRA Paper 67111, University Library of Munich, Germany.
    6. Korobilis, Dimitris, 2014. "Data-based priors for vector autoregressions with drifting coefficients," SIRE Discussion Papers 2014-022, Scottish Institute for Research in Economics (SIRE).
    7. Zhang, Yaojie & Wahab, M.I.M. & Wang, Yudong, 2023. "Forecasting crude oil market volatility using variable selection and common factor," International Journal of Forecasting, Elsevier, vol. 39(1), pages 486-502.
    8. Goodness C. Aye & Rangan Gupta, 2013. "Forecasting Real House Price of the U.S.: An Analysis Covering 1890 to 2012," Working Papers 201362, University of Pretoria, Department of Economics.
    9. Omokolade Akinsomi & Goodness C. Aye & Vassilios Babalos & Fotini Economou & Rangan Gupta, 2016. "Erratum to: Real estate returns predictability revisited: novel evidence from the US REITs market," Empirical Economics, Springer, vol. 51(3), pages 1191-1191, November.
    10. Kuo-Jung Lee & Yi-Chi Chen, 2018. "Of needles and haystacks: revisiting growth determinants by robust Bayesian variable selection," Empirical Economics, Springer, vol. 54(4), pages 1517-1547, June.
    11. Paul Hofmarcher & Jesús Crespo Cuaresma & Bettina Grün & Kurt Hornik, 2015. "Last Night a Shrinkage Saved My Life: Economic Growth, Model Uncertainty and Correlated Regressors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 133-144, March.
    12. Konstantakis, Konstantinos N. & Michaelides, Panayotis G., 2014. "Transmission of the debt crisis: From EU15 to USA or vice versa? A GVAR approach," Journal of Economics and Business, Elsevier, vol. 76(C), pages 115-132.
    13. Yuhan Cheng & Heyang Zhou & Yanchu Liu, 2025. "Large Language Models and Futures Price Factors in China," Papers 2509.23609, arXiv.org.
    14. Naik, Prasad A., 2015. "Marketing Dynamics: A Primer on Estimation and Control," Foundations and Trends(R) in Marketing, now publishers, vol. 9(3), pages 175-266, December.
    15. Nicholas Apergis & Ghassen El Montasser & Emmanuel Owusu-Sekyere & Ahdi N. Ajmi & Rangan Gupta, 2014. "Dutch Disease Effect of Oil Rents on Agriculture Value Added in MENA Countries," Working Papers 201408, University of Pretoria, Department of Economics.
    16. Yang Aijun & Xiang Ju & Yang Hongqiang & Lin Jinguan, 2018. "Sparse Bayesian Variable Selection in Probit Model for Forecasting U.S. Recessions Using a Large Set of Predictors," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 1123-1138, April.
    17. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Foundations and Trends(R) in Econometrics, now publishers, vol. 11(4), pages 230-354, June.
    18. Malefaki, Valia, 2015. "On Flexible Linear Factor Stochastic Volatility Models," MPRA Paper 62216, University Library of Munich, Germany.
    19. Ramazan EKİNCİ & Osman TÜZÜN & Fatih CEYLAN & Hakan KAHYAOĞLU, 2017. "Dışa Açıklık ile İşsizlik Arasındaki İlişki: Seçilmiş AB Ülkeleri ve Türkiye Üzerine Zamana Göre Değişen Parametreli Bir Analiz Algıları," Sosyoekonomi Journal, Sosyoekonomi Society, issue 25(31).
    20. Anoek Castelein & Dennis Fok & Richard Paap, 2020. "Heterogeneous variable selection in nonlinear panel data models: A semiparametric Bayesian approach," Tinbergen Institute Discussion Papers 20-061/III, Tinbergen Institute.

    More about this item

    Keywords

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: 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
    • 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

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