Learning Time-Varying Forecast Combinations

Antoine Mandel and Amir Sani ()
Additional contact information
Amir Sani: Centre d'Economie de la Sorbonne - Paris School of Economics, http://www.amirsani.com

Documents de travail du Centre d'Economie de la Sorbonne from Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne

Abstract: Combining forecasts has been demonstrated as a robust solution to noisy data, structural breaks, unstable forecasters and shifting environmental dynamics. In practice, sophisticated combination methods have failed to consistently outperform the mean over multiple horizons, pools of varying forecasters and different endogenous variables. This paper addresses the challenge to “develop methods better geared to the intermittent and evolving nature of predictive relations”, noted in Stock and Watson (2001), by proposing an adaptive non-parametric “meta” approach that provides a time-varying hedge against the performance of the mean for any selected forecast combination approach. This approach arguably solves the so-called “Forecast Combination Puzzle” using a meta-algorithm that adaptively hedges weights between the mean and a specific forecast combination algorithm or pool of forecasters augmented with one or more forecast combination algorithms. Theoretical performance bounds are reported and empirical performance is evaluated on the seven country macroeconomic output and inflation dataset introduced in Stock and Watson (2001) as well as the Euro-area Survey of Professional Forecasters

Keywords: Forecast combinations; Forecast combination puzzle; Machine learning; Econometrics (search for similar items in EconPapers)
JEL-codes: C71 D85 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2016-04, Revised 2016-09
New Economics Papers: this item is included in nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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ftp://mse.univ-paris1.fr/pub/mse/CES2016/16036R.pdf (application/pdf)

Related works:
Working Paper: Learning Time-Varying Forecast Combinations (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:mse:cesdoc:16036r

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