Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/73794 
Authors: 
Year of Publication: 
2008
Series/Report no.: 
ifo Working Paper No. 55
Publisher: 
ifo Institute - Leibniz Institute for Economic Research at the University of Munich, Munich
Abstract: 
It can be shown that inflation expectations and associated forecast errors are characterized by a high degree of persistence. One reason may be that forecasters cannot directly observe the inflation target pursued by the central bank and, hence, face a complicated forecasting problem. In particular, they have to infer whether the observed movement ofthe inflation rate is due to a permanent change of policy parameters or whether it is the result of a transient shock. Consequently, it is assumed that agents behave like econometricians who filter noisy information by estimating an unobserved components model. This constitutes the trend learning algorithm employed by the forecaster. To examine whether this is a valid assumption, I fit a simple learning model to US survey expectations. The second part contains an out-of-sample forecasting experiment which shows that learning by signal extraction matches survey measures closer than other standard models. Moreover, it turns out that a weighted average of different expectation formation processes with a prominent role for signal extraction behaviour is well suited to explain survey measures of inflation expectations.
JEL: 
C32
C53
E31
E37
Document Type: 
Working Paper

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