Learning Trend Inflation – Can Signal Extraction Explain Survey Forecasts?
AbstractIt 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.
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Bibliographic InfoPaper provided by Ifo Institute for Economic Research at the University of Munich in its series Ifo Working Paper Series with number Ifo Working Paper No. 55.
Date of creation: 2008
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Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
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