Chahrour, Ryan and Ulbricht, Robert
(2018)
Robust Predictions for DSGE Models with Incomplete Information.
TSE Working Paper, n. 18-971, Toulouse
Preview |
Text
Download (529kB) | Preview |
Abstract
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 Classification: | D84 - Expectations; Speculations E32 - Business Fluctuations; Cycles |
Subjects: | B- ECONOMIE ET FINANCE |
Divisions: | TSE-R (Toulouse) |
Institution: | Université Toulouse Capitole |
Site: | UT1 |
Date Deposited: | 30 Nov 2018 09:14 |
Last Modified: | 27 Oct 2021 13:37 |
OAI Identifier: | oai:tse-fr.eu:33124 |
URI: | https://publications.ut-capitole.fr/id/eprint/26530 |