ABC-based Forecasting in State Space Models

Chaya Weerasinghe (), Ruben Loaiza-Maya (), Gael Martin () and David Frazier ()

No 12/23, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: Approximate Bayesian Computation (ABC) has gained popularity as a method for conducting inference and forecasting in complex models, most notably those which are intractable in some sense. In this paper we use ABC to produce probabilistic forecasts in state space models (SSMs). Whilst ABC-based forecasting in correctly-specified SSMs has been studied, the misspecified case has not been investigated, and it is that case which we emphasize. We invoke recent principles of ‘focused’ Bayesian prediction, whereby Bayesian updates are driven by a scoring rule that rewards predictive accuracy; the aim being to produce predictives that perform well in that rule, despite misspecification. Two methods are investigated for producing the focused predictions. In a simulation setting, `coherent' predictions are in evidence for both methods: the predictive constructed via the use of a particular scoring rule predicts best according to that rule. Importantly, both focused methods typically produce more accurate forecasts than an exact, but misspecified, predictive. An empirical application to a truly intractable SSM completes the paper.

Keywords: Approximate Bayesian computation; auxiliary model; loss-based prediction; focused Bayesian prediction; proper scoring rules; stochastic volatility model (search for similar items in EconPapers)
JEL-codes: C11 C53 C58 (search for similar items in EconPapers)
Pages: 25
Date: 2023
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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