Arbeitspapier
Learning Trend Inflation: Can Signal Extraction Explain Survey Forecasts?
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: ifo Working Paper ; No. 55
- Klassifikation
-
Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Forecasting Models; Simulation Methods
Price Level; Inflation; Deflation
Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Henzel, Steffen
- Ereignis
-
Veröffentlichung
- (wer)
-
ifo Institute - Leibniz Institute for Economic Research at the University of Munich
- (wo)
-
Munich
- (wann)
-
2008
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:41 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Henzel, Steffen
- ifo Institute - Leibniz Institute for Economic Research at the University of Munich
Entstanden
- 2008
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