Information Aggregation in a DSGE Model
Tarek A. Hassan
University of Chicago - Booth School of Business; National Bureau of Economic Research (NBER); Centre for Economic Policy Research (CEPR)
Thomas M. Mertens
New York University (NYU) - Department of Finance
May 28, 2014
We introduce the information microstructure of a canonical noisy rational expectations model (Hellwig, 1980) into the framework of a conventional real business cycle model. Each household receives a private signal about future productivity. In equilibrium, the stock price serves to aggregate and transmit this information. We find that dispersed information about future productivity affects the quantitative properties of our real business cycle model in three dimensions. First, households' ability to learn about the future affects their consumption-savings decision. The equity premium falls and the risk-free interest rate rises when the stock price perfectly reveals innovations to future productivity. Second, when noise trader demand shocks limit the stock market's capacity to aggregate information, households hold heterogeneous expectations in equilibrium. However, for a reasonable size of noise trader demand shocks the model cannot generate the kind of disagreement observed in the data. Third, even moderate heterogeneity in the equilibrium expectations held by households has a sizable effect on the level of all economic aggregates and on the correlations and standard deviations produced by the model. For example, the correlation between consumption and investment growth is 0.29 when households have no information about the future, but 0.41 when information is dispersed.
Number of Pages in PDF File: 46
Keywords: Noisy Rational Expectations, Dispersed Information, Business Cycles, Asset Prices, Investment, Portfolio Choice
JEL Classification: C63, D83, E2, E3, E44, G11working papers series
Date posted: March 22, 2014 ; Last revised: May 29, 2014
© 2014 Social Science Electronic Publishing, Inc. All Rights Reserved.
This page was processed by apollo6 in 0.359 seconds