Robust Forecasting

T. Christensen, H. Moon, F. Schorfheide
{"title":"Robust Forecasting","authors":"T. Christensen, H. Moon, F. Schorfheide","doi":"10.2139/ssrn.3737629","DOIUrl":null,"url":null,"abstract":"We use a decision-theoretic framework to study the problem of forecasting discrete outcomes when the forecaster is unable to discriminate among a set of plausible forecast distributions because of partial identification or concerns about model misspecification or structural breaks. We derive \"robust\" forecasts which minimize maximum risk or regret over the set of forecast distributions. We show that for a large class of models including semiparametric panel data models for dynamic discrete choice, the robust forecasts depend in a natural way on a small number of convex optimization problems which can be simplified using duality methods. Finally, we derive \"efficient robust\" forecasts to deal with the problem of first having to estimate the set of forecast distributions and develop a suitable asymptotic efficiency theory. Forecasts obtained by replacing nuisance parameters that characterize the set of forecast distributions with efficient first-stage estimators can be strictly dominated by our efficient robust forecasts.","PeriodicalId":11485,"journal":{"name":"Econometrics: Applied Econometrics & Modeling eJournal","volume":"2 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Econometrics: Applied Econometrics & Modeling eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3737629","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

Abstract

We use a decision-theoretic framework to study the problem of forecasting discrete outcomes when the forecaster is unable to discriminate among a set of plausible forecast distributions because of partial identification or concerns about model misspecification or structural breaks. We derive "robust" forecasts which minimize maximum risk or regret over the set of forecast distributions. We show that for a large class of models including semiparametric panel data models for dynamic discrete choice, the robust forecasts depend in a natural way on a small number of convex optimization problems which can be simplified using duality methods. Finally, we derive "efficient robust" forecasts to deal with the problem of first having to estimate the set of forecast distributions and develop a suitable asymptotic efficiency theory. Forecasts obtained by replacing nuisance parameters that characterize the set of forecast distributions with efficient first-stage estimators can be strictly dominated by our efficient robust forecasts.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
可靠的预测
我们使用决策理论框架来研究预测离散结果的问题,当预测者由于部分识别或对模型错误规范或结构断裂的担忧而无法区分一组合理的预测分布时。我们推导出“稳健”的预测,使预测分布集的最大风险或遗憾最小化。我们证明了对于包括半参数面板数据模型在内的一类动态离散选择模型,鲁棒预测自然依赖于少量凸优化问题,这些问题可以用对偶方法简化。最后,我们导出了“有效稳健”预测,解决了预测分布集的估计问题,并提出了一个合适的渐近效率理论。通过用有效的第一阶段估计器代替表征预测分布集的干扰参数获得的预测可以严格地由我们的有效鲁棒预测控制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Discovering Causal Models with Optimization: Confounders, Cycles, and Feature Selection Improving the Wisdom of Crowds with Analysis of Variance of Predictions of Related Outcomes Canonical Correlation-based Model Selection for the Multilevel Factors Robust Forecasting Resurrecting the Size Effect: Firm Size, Profitability Shocks, and Expected Stock Returns
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1