Learning to Forecast and Cyclical Behavior of Output and Inflation

Klaus Adam ()

No 2003/01, CFS Working Paper Series from Center for Financial Studies

Abstract: This paper considers a sticky price model with a cash-in-advance constraint where agents forecast inflation rates with the help of econometric models. Agents use least squares learning to estimate two competing models of which one is consistent with rational expectations once learning is complete. When past performance governs the choice of forecast model, agents may prefer to use the inconsistent forecast model, which generates an equilibrium where forecasts are inefficient. While average output and inflation result the same as under rational expectations, higher moments differ substantially: output and inflation show persistence, inflation responds sluggishly to nominal disturbances, and the dynamic correlations of output and inflation match U.S. data surprisingly well.

Keywords: Learning; Business Cycles; Rational Expectations; Inefficient Forecasts; Output and Inflation Persistence (search for similar items in EconPapers)
JEL-codes: E31 E32 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets, nep-mac and nep-mon
Date: 2003-01-01
Note: I am grateful to Giuseppe Bertola, Matthias Brückner, Benjamin Friedman, Tullio Jappelli, Jordi Gali, Soeren Johansen, Albert Marcet, Ramon Marimon, Athanasios Orphanides, Philippe Weil and seminar participants at Bocconi, Harvard, ECB, European University Institute, FED, MIT, Pompeu Fabra, and ULB. All errors are mine.
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Related works:
Journal Article: LEARNING TO FORECAST AND CYCLICAL BEHAVIOR OF OUTPUT AND INFLATION (2005)
Working Paper: Learning to Forecast and Cyclical Behavior of Output and Inflation (2003)
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