Replication data for: Expectations, Learning, and Business Cycle Fluctuations
Principal Investigator(s): View help for Principal Investigator(s) Stefano Eusepi; Bruce Preston
Version: View help for Version V1
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| AER_replication_files-MS20080712 | 10/11/2019 04:12:PM | ||
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text/plain | 14.6 KB | 10/11/2019 12:15:PM |
Project Citation:
Eusepi, Stefano, and Preston, Bruce. Replication data for: Expectations, Learning, and Business Cycle Fluctuations. Nashville, TN: American Economic Association [publisher], 2011. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-10-11. https://doi.org/10.3886/E112470V1
Project Description
Summary:
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This paper develops a theory of expectations-driven business cycles based on learning. Agents have incomplete knowledge about how market prices are determined and shifts in expectations of future prices affect dynamics. Learning breaks the tight link between fundamentals and equilibrium prices, inducing periods of erroneous optimism or pessimism about future returns to capital and wages which subsequent data partially validate. In a real business cycle model, the theoretical framework amplifies and propagates technology shocks. Moreover, it produces agents' forecast errors consistent with business cycle properties of forecast errors for a wide range of variables from the Survey of Professional Forecasters. (JEL C53, D83, D84, E32, E37)
Scope of Project
JEL Classification:
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C53 Forecasting Models; Simulation Methods
D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
D84 Expectations; Speculations
E32 Business Fluctuations; Cycles
E37 Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
C53 Forecasting Models; Simulation Methods
D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
D84 Expectations; Speculations
E32 Business Fluctuations; Cycles
E37 Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
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