Robust Identification of Investor Beliefs

Xiaohong Chen (), Lars Hansen and Peter G. Hansen

No 27257, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: This paper develops a new method informed by data and models to recover information about investor beliefs. Our approach uses information embedded in forward-looking asset prices in conjunction with asset pricing models. We step back from presuming rational expectations and entertain potential belief distortions bounded by a statistical measure of discrepancy. Additionally, our method allows for the direct use of sparse survey evidence to make these bounds more informative. Within our framework, market-implied beliefs may differ from those implied by rational expectations due to behavioral/psychological biases of investors, ambiguity aversion, or omitted permanent components to valuation. Formally, we represent evidence about investor beliefs using a novel nonlinear expectation function deduced using model-implied moment conditions and bounds on statistical divergence. We illustrate our method with a prototypical example from macro-finance using asset market data to infer belief restrictions for macroeconomic growth rates.

JEL-codes: E03 E22 E44 G02 G12 G14 G40 (search for similar items in EconPapers)
Date: 2020-05
New Economics Papers: this item is included in nep-mac
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Citations: View citations in EconPapers (9)

Published as Xiaohong Chen & Lars Peter Hansen & Peter G. Hansen, 2020. "Robust identification of investor beliefs," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(52), pages 33130-33140, December.

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Related works:
Journal Article: Robust identification of investor beliefs (2020)
Working Paper: Robust Identification of Investor Beliefs (2020)
Working Paper: Robust Identification of Investor Beliefs (2020)
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