Rolling window selection for out-of-sample forecasting with time-varying parameters

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• dc.contributor.author Inoue, Atsushica
• dc.contributor.author Jin, Luca
• dc.contributor.author Rossi, Barbara, 1971-ca
• dc.contributor.other Universitat Pompeu Fabra. Departament d'Economia i Empresa
• dc.date.accessioned 2017-07-26T10:50:17Z
• dc.date.available 2017-07-26T10:50:17Z
• dc.date.issued 2014-06-01
• dc.date.modified 2017-07-23T02:16:22Z
• dc.description.abstract While forecasting is a common practice in academia, government and business alike, practitioners are often left wondering how to choose the sample for estimating forecasting models. When we forecast inflation in 2018, for example, should we use the last 30 years of data or the last 10 years of data? There is strong evidence of structural changes in economic time series, and the forecasting performance is often quite sensitive to the choice of such window size. In this paper, we develop a novel method for selecting the estimation window size for forecasting. Specifically, we propose to choose the optimal window size that minimizes the forecaster's quadratic loss function, and we prove the asymptotic validity of our approach. Our Monte Carlo experiments show that our method performs quite well under various types of structural changes. When applied to forecasting US real output growth and inflation, the proposed method tends to improve upon conventional methods, especially for output growth.
• dc.format.mimetype application/pdfca
• dc.identifier https://econ-papers.upf.edu/ca/paper.php?id=1435
• dc.identifier.citation Journal of Econometrics, 196 (1), 2017, 55-67
• dc.identifier.uri http://hdl.handle.net/10230/22664
• dc.language.iso eng
• dc.relation.ispartofseries Economics and Business Working Papers Series; 1435
• dc.rights L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
• dc.subject.keyword macroeconomic forecasting; parameter instability; nonparametric estimation; bandwidth selection.
• dc.subject.keyword Macroeconomics and International Economics
• dc.title Rolling window selection for out-of-sample forecasting with time-varying parametersca
• dc.title.alternative
• dc.type info:eu-repo/semantics/workingPaper