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Bayesian Inference and Prediction of a Multiple-Change-Point Panel Model with Nonparametric Priors

Author

Listed:
  • Mark Fisher

    () (Federal Reserve Bank of Atlanta, USA)

  • Mark J. Jensen

    () (Federal Reserve Bank of Atlanta, USA; Rimini Centre for Economic Analysis)

Abstract

Change point models using hierarchical priors share in the information of each regime when estimating the parameter values of a regime. Because of this sharing hierarchical priors have been very successful when estimating the parameter values of short-lived regimes and predicting the out-of-sample behavior of the regime parameters. However, the hierarchical priors have been parametric. Their parametric nature leads to global shrinkage that biases the estimates of the parameter coefficient of extraordinary regimes towards the value of the average regime. To overcome this shrinkage we model the hierarchical prior nonparametrically by letting the hyperparameter's prior, in other words, the hyperprior, be unknown and modeling it with a Dirichlet processes prior. To apply a nonparametric hierarchical prior to the probability of a break occurring we extend the change point model to a multiple-change-point panel model. The hierarchical prior then shares in the cross-sectional information of the break processes to estimate the transition probabilities. We apply our multiple-change-point panel model to a longitudinal data set of actively managed, US equity, mutual fund returns to measure fund performance and investigate what the chances are of a skilled fund being skilled in the future.

Suggested Citation

  • Mark Fisher & Mark J. Jensen, 2018. "Bayesian Inference and Prediction of a Multiple-Change-Point Panel Model with Nonparametric Priors," Working Paper series 18-12, Rimini Centre for Economic Analysis.
  • Handle: RePEc:rim:rimwps:18-12
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    References listed on IDEAS

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    1. Chan, Joshua C.C. & Koop, Gary, 2014. "Modelling breaks and clusters in the steady states of macroeconomic variables," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 186-193.
    2. Markus Jochmann, 2015. "Modeling U.S. Inflation Dynamics: A Bayesian Nonparametric Approach," Econometric Reviews, Taylor & Francis Journals, vol. 34(5), pages 537-558, May.
    3. Arnaud Dufays, 2016. "Infinite-State Markov-Switching for Dynamic Volatility," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 14(2), pages 418-460.
    4. Luc Bauwens & Gary Koop & Dimitris Korobilis & Jeroen V.K. Rombouts, 2015. "The Contribution of Structural Break Models to Forecasting Macroeconomic Series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 596-620, June.
    5. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. Van Dijk, 2016. "Interconnections Between Eurozone and us Booms and Busts Using a Bayesian Panel Markov‐Switching VAR Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1352-1370, November.
    6. Luc Bauwens & Gary Koop & Dimitris Korobilis & Jeroen V.K. Rombouts, 2015. "The Contribution of Structural Break Models to Forecasting Macroeconomic Series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 596-620, June.
    7. Sylvia Kaufmann & Sylvia Frühwirth-Schnatter, 2006. "How do changes in monetary policy affect bank lending? An analysis of Austrian bank data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(3), pages 275-305.
    8. Yong Song, 2014. "Modelling Regime Switching And Structural Breaks With An Infinite Hidden Markov Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 825-842, August.
    9. Geweke, John, 2007. "Interpretation and inference in mixture models: Simple MCMC works," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3529-3550, April.
    10. Lijoi, Antonio & Prunster, Igor & Walker, Stephen G., 2005. "On Consistency of Nonparametric Normal Mixtures for Bayesian Density Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1292-1296, December.
    11. Jones, Christopher S. & Shanken, Jay, 2005. "Mutual fund performance with learning across funds," Journal of Financial Economics, Elsevier, vol. 78(3), pages 507-552, December.
    12. Maheu, John M. & Song, Yong, 2014. "A new structural break model, with an application to Canadian inflation forecasting," International Journal of Forecasting, Elsevier, vol. 30(1), pages 144-160.
    13. Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
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    More about this item

    Keywords

    Bayesian nonparametric analysis; change points; Dirichlet process; hierarchical priors; mutual fund performance;

    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
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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