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Biases in Macroeconomic Forecasts: Irrationality or Asymmetric Loss?

Author

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  • Graham Elliott
  • Ivana Komunjer
  • Allan Timmermann

Abstract

Empirical studies using survey data on expectations have frequently observed that forecasts are biased and have concluded that agents are not rational. We establish that existing rationality tests are not robust to even small deviations from symmetric loss and hence have little ability to tell whether the forecaster is irrational or the loss function is asymmetric. We quantify the trade-off between forecast inefficiency and asymmetric loss leading to identical outcomes of standard rationality tests and explore new and more general methods for testing forecast rationality jointly with flexible families of loss functions that embed squared loss as a special case. Empirical applications to survey data on forecasts of real output growth and inflation suggest that rejections of rationality may largely have been driven by the assumption of squared loss. Moreover, our results suggest that agents are averse to "bad" outcomes such as lower-than-expected real output growth and higher-than-expected inflation and that they incorporate such loss aversion into their forecasts. (JEL: C22, C53, E37) (c) 2008 by the European Economic Association.

Suggested Citation

  • Graham Elliott & Ivana Komunjer & Allan Timmermann, 2008. "Biases in Macroeconomic Forecasts: Irrationality or Asymmetric Loss?," Journal of the European Economic Association, MIT Press, vol. 6(1), pages 122-157, March.
  • Handle: RePEc:tpr:jeurec:v:6:y:2008:i:1:p:122-157
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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