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Learning in a Medium-Scale DSGE Model with Expectations Based on Small Forecasting Models

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  • Sergey Slobodyan
  • Raf Wouters

Abstract

This paper evaluates the empirical performance of a medium-scale DSGE model with agents forming expectations using small forecasting models updated by the Kalman filter. The adaptive learning model fits the data better than the rational expectations (RE) model. Beliefs about the inflation persistence explain the observed decline in the mean and the volatility of inflation as well as Phillips curve flattening. Learning about inflation results in lower estimates for the persistence of the exogenous shocks that drive price and wage dynamics in the RE version of the model. Expectations based on small forecasting models are closely related to the survey evidence on inflation expectations. (JEL C53, D83, D84, E13, E17, E31)

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Bibliographic Info

Article provided by American Economic Association in its journal American Economic Journal: Macroeconomics.

Volume (Year): 4 (2012)
Issue (Month): 2 (April)
Pages: 65-101

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Handle: RePEc:aea:aejmac:v:4:y:2012:i:2:p:65-101

Note: DOI: 10.1257/mac.4.2.65
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  1. Andrew Atkeson & Lee E. Ohanian., 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Win, pages 2-11.
  2. Claudio E. V. Borio & Andrew Filardo, 2007. "Globalisation and inflation: New cross-country evidence on the global determinants of domestic inflation," BIS Working Papers 227, Bank for International Settlements.
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Cited by:
  1. Hatcher, Michael C. & Minford, Patrick, 2013. "Stabilization policy, rational expectations and price-level versus inflation targeting: a survey," Cardiff Economics Working Papers E2013/14, Cardiff University, Cardiff Business School, Economics Section.
  2. Cogley, Timothy & Matthes, Christian & Sbordone, Argia M., 2014. "Optimized Taylor Rules for Disinflation When Agents are Learning," Working Paper 14-7, Federal Reserve Bank of Richmond.

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