Robust Predictions for DSGE Models with Incomplete Information

Ryan Chahrour and Robert Ulbricht

No 925, Boston College Working Papers in Economics from Boston College Department of Economics

Abstract: We provide predictions for DSGE models with incomplete information that are robust across information structures. Our approach maps an incomplete-information model into a full-information economy with time-varying expectation wedges and provides conditions that ensure the wedges are rationalizable by some information structure. Using our approach, we quantify the potential importance of information as a source of business cycle fluctuations in an otherwise frictionless model. Our approach uncovers a central role for firm-specific demand shocks in supporting aggregate confidence fluctuations. Only if firms face unobserved local demand shocks can confidence fluctuations account for a significant portion of the US business cycle.

Keywords: Business cycles; DSGE models; incomplete-information; information-robust predictions (search for similar items in EconPapers)
JEL-codes: D84 E32 (search for similar items in EconPapers)
Date: 2017-03-16, Revised 2021-06-10
New Economics Papers: this item is included in nep-dge and nep-mac
Note: previously circulated as "Information-driven Business Cycles: A Primal Approach"
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Citations: View citations in EconPapers (8)

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Related works:
Journal Article: Robust Predictions for DSGE Models with Incomplete Information (2023)
Working Paper: Robust Predictions for DSGE Models with Incomplete Information (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:boc:bocoec:925

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