Robust Predictions for DSGE Models with Incomplete Information
Ryan Chahrour and
No 925, Boston College Working Papers in Economics from Boston College Department of Economics
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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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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