Dynamic Benchmark Targeting

Karl Schlag and Andriy Zapechelnyuk

Working Papers from Business School - Economics, University of Glasgow

Abstract: We study decision making in complex discrete-time dynamic environments where Bayesian optimization is intractable. A decision maker is equipped with a finite set of benchmark strategies. She aims to perform similarly to or better than each of these benchmarks. Furthermore, she cannot commit to any decision rule, hence she must satisfy this goal at all times and after every history. We find such a rule for a sufficiently patient decision maker and show that it necessitates not to rely too much on observations from distant past. In this sense we find that it can be optimal to forget.

Keywords: Dynamic consistency; experts; regret minimization; forecast com- bination; non-Bayesian decision making. (search for similar items in EconPapers)
JEL-codes: C44 D81 (search for similar items in EconPapers)
Date: 2016-10
New Economics Papers: this item is included in nep-mic
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Journal Article: Dynamic benchmark targeting (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:gla:glaewp:2016_20

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