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Approximate Bayesian forecasting

Author

Listed:
  • David T. Frazier
  • Worapree Maneesoonthorn
  • Gael M. Martin
  • Brendan P.M. McCabe

Abstract

Approximate Bayesian Computation (ABC) has become increasingly prominent as a method for conducting parameter inference in a range of challenging statistical problems, most notably those characterized by an intractable likelihood function. In this paper, we focus on the use of ABC not as a tool for parametric inference, but as a means of generating probabilistic forecasts; or for conducting what we refer to as "approximate Bayesian forecasting". The four key issues explored are: i) the link between the theoretical behavior of the ABC posterior and that of the ABC-based predictive; ii) the use of proper scoring rules to measure the (potential) loss of forecast accuracy when using an approximate rather than an exact predictive; iii) the performance of approximate Bayesian forecasting in state space models; and iv) the use of forecast accuracy to inform the selection of ABC summaries in empirical settings. The primary finding of the paper is that ABC can provide a computationally ecient means of generating probabilistic forecasts that are nearly identical to those produced by the exact predictive, and in a fraction of the time required to produce predictions via an exact method.

Suggested Citation

  • David T. Frazier & Worapree Maneesoonthorn & Gael M. Martin & Brendan P.M. McCabe, 2018. "Approximate Bayesian forecasting," Monash Econometrics and Business Statistics Working Papers 2/18, Monash University, Department of Econometrics and Business Statistics.
  • Handle: RePEc:msh:ebswps:2018-2
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    File URL: https://www.monash.edu/business/ebs/research/publications/ebs/wp02-2018v1.pdf
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Bayesian prediction; likelihood-free methods; predictive merging; proper scoring rules; particle filtering; jump-diffusion models.;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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