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Learning Trend Inflation – Can Signal Extraction Explain Survey Forecasts?

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  • Steffen Henzel

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 of the 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.

Suggested Citation

  • Steffen Henzel, 2008. "Learning Trend Inflation – Can Signal Extraction Explain Survey Forecasts?," ifo Working Paper Series 55, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
  • Handle: RePEc:ces:ifowps:_55
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    References listed on IDEAS

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    1. repec:fgv:epgrbe:v:66:n:3:a:2 is not listed on IDEAS
    2. Malikane, Christopher & Mokoka, Tshepo, 2012. "Monetary policy credibility: A Phillips curve view," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(3), pages 266-271.
    3. Carlos Hamilton V Araujo & Wagner P Gaglianone, 2010. "Survey-based inflation expectations in Brazil," BIS Papers chapters,in: Bank for International Settlements (ed.), Monetary policy and the measurement of inflation: prices, wages and expectations, volume 49, pages 107-114 Bank for International Settlements.
    4. Kohlscheen, Emanuel, 2012. "Uma nota sobre erros de previsão da inflação de curto‐prazo," Revista Brasileira de Economia - RBE, FGV/EPGE - Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil), vol. 66(3), October.

    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space 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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