Robust Predictions for DSGE Models with Incomplete Information

Chahrour, Ryan and Ulbricht, Robert (2018) Robust Predictions for DSGE Models with Incomplete Information. TSE Working Paper, n. 18-971, Toulouse

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We study the quantitative potential of DSGE models with incomplete information. In contrast to existing literature, we offer predictions that are robust across all possible private information structures that agents may have. Our approach maps DSGE models with information-frictions into a parallel economy where deviations from fullinformation are captured by time-varying wedges. We derive exact conditions that ensure the consistency of these wedges with some information structure. We apply our approach to an otherwise frictionless business cycle model where firms and households have incomplete information. We show how assumptions about information interact with the presence of idiosyncratic shocks to shape the potential for confidence-driven fluctuations. For a realistic calibration, we find that correlated confidence regarding idiosyncratic shocks (aka “sentiment shocks”) can account for up to 51 percent of U.S. business cycle fluctuations. By contrast, confidence about aggregate productivity can account for at most 3 percent.

Item Type: Monograph (Working Paper)
Language: English
Date: November 2018
Place of Publication: Toulouse
Uncontrolled Keywords: Business cycles, DSGE models, incomplete-information, information-robust predictions
JEL codes: D84 - Expectations; Speculations
E32 - Business Fluctuations; Cycles
Divisions: TSE-R (Toulouse)
Institution: Université Toulouse Capitole
Site: UT1
Date Deposited: 30 Nov 2018 09:14
Last Modified: 11 Jul 2019 12:18

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