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Learning and heterogeneity in GDP and inflation forecasts

Author

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  • Lahiri, Kajal
  • Sheng, Xuguang
Abstract
Using a Bayesian learning model with heterogeneity across agents, our study aims to identify the relative importance of alternative pathways through which professional forecasters disagree and reach consensus on the term structure of inflation and real GDP forecasts, resulting in different patterns of forecast accuracy. There are two primary sources of forecast disagreement in our model: differences in prior beliefs, 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 US, and (ii) the successful inflation targeting experience of Italy after 1997, firmly establish the importance of these two pathways to expert disagreement, and help to explain the relative forecasting accuracy of these two macroeconomic variables.

Suggested Citation

  • Lahiri, Kajal & Sheng, Xuguang, 2010. "Learning and heterogeneity in GDP and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 26(2), pages 265-292, April.
    • Handle: RePEc:eee:intfor:v:26:y::i:2:p:265-292
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    More about this item

    Keywords

    Bayesian learning Public information Panel data Forecast disagreement Forecast horizon Forecast efficiency GDP Inflation targeting;

    JEL classification:

    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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