Lahiri, Kajal and Sheng, Xuguang (2009): Learning and heterogeneity in GDP and inflation forecasts. Published in: International Journal of Forecasting No. 26 (2010): pp. 265-292.
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We estimate a Bayesian learning model with heterogeneity aimed at explaining the evolution of expert disagreement in forecasting real GDP growth and inflation over 24 monthly horizons for G7 countries during 1990-2007. Professional forecasters are found to begin and have relatively more success in predicting inflation than real GDP at significantly longer horizons; forecasts for real GDP contain little information beyond 6 quarters, but forecasts for inflation have predictive value beyond 24 months and even 36 months for some countries. Forecast disagreement arises from two primary sources in our model: differences in the initial prior beliefs of experts, and differences in the interpretation of new public information. Estimated model parameters, together with two separate case studies on (i) the dynamics of forecast disagreement in the aftermath of the 9/11 terrorist attack in the U.S. and (ii) the successful inflation targeting experience in Italy after 1997, firmly establish the importance of these two pathways to expert disagreement.
|Item Type:||MPRA Paper|
|Original Title:||Learning and heterogeneity in GDP and inflation forecasts|
|Keywords:||Bayesian learning, Public information, Panel data, Forecast disagreement, Forecast horizon; Content function; Forecast efficiency; GDP; Inflation targeting|
|Subjects:||E - Macroeconomics and Monetary Economics > E1 - General Aggregative Models > E17 - Forecasting and Simulation: Models and Applications
C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General
|Depositing User:||Kajal Lahiri|
|Date Deposited:||18. Mar 2010 18:25|
|Last Modified:||30. Apr 2015 07:41|
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