ABC-based forecasting in misspecified state space models

IF 6.9 2区 经济学 Q1 ECONOMICS
Chaya Weerasinghe, Rubén Loaiza-Maya, Gael M. Martin, David T. Frazier
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引用次数: 0

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. It is this case that 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. That is, the predictive constructed using a particular scoring rule often predicts best according to that rule. Importantly, both focused methods typically produce more accurate forecasts than an exact but misspecified predictive, in particular when the degree of misspecification is marked. An empirical application to a truly intractable SSM completes the paper.
基于ABC的失范状态空间模型预测
近似贝叶斯计算(Approximate Bayesian Computation,ABC)作为一种在复杂模型中进行推理和预测的方法,尤其是那些在某种意义上难以处理的模型,已经越来越受欢迎。在本文中,我们使用近似贝叶斯计算在状态空间模型(SSM)中进行概率预测。虽然基于 ABC 的预测方法已经在正确规范的 SSM 中进行过研究,但对错误规范的情况还没有进行过研究。我们强调的正是这种情况。我们引用了最近的 "重点 "贝叶斯预测原则,即贝叶斯更新由奖励预测准确性的评分规则驱动;目的是产生在该规则中表现良好的预测结果,尽管存在规格错误。我们研究了两种方法来生成有针对性的预测。在模拟环境中,两种方法都能得出 "一致 "的预测结果。也就是说,使用特定评分规则构建的预测往往能根据该规则做出最佳预测。重要的是,这两种有针对性的方法通常都能比精确但误判的预测方法得出更准确的预测结果,尤其是在误判程度明显的情况下。本文最后对一个真正棘手的 SSM 进行了实证应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
17.10
自引率
11.40%
发文量
189
审稿时长
77 days
期刊介绍: The International Journal of Forecasting is a leading journal in its field that publishes high quality refereed papers. It aims to bridge the gap between theory and practice, making forecasting useful and relevant for decision and policy makers. The journal places strong emphasis on empirical studies, evaluation activities, implementation research, and improving the practice of forecasting. It welcomes various points of view and encourages debate to find solutions to field-related problems. The journal is the official publication of the International Institute of Forecasters (IIF) and is indexed in Sociological Abstracts, Journal of Economic Literature, Statistical Theory and Method Abstracts, INSPEC, Current Contents, UMI Data Courier, RePEc, Academic Journal Guide, CIS, IAOR, and Social Sciences Citation Index.
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