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Counting Biased Forecasters: An Application of Multiple Testing Techniques

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  • Fabiana Gomez
  • David Pacini

Abstract

We investigate the problem of counting biased forecasters among a group of unbiased and biased forecasters of macroeconomic variables. The innovation is to implement a procedure controlling for the expected proportion of unbiased forecasters that could be erroneously classified as biased (i.e., the false discovery rate). Monte Carlo exercises illustrate the relevance of controlling the false discovery rate in this context. Using data from the Survey of Professional Forecasters, we find that up to 7 out of 10 forecasters classified as biased by a procedure not controlling the false discovery rate may actually be unbiased.

Suggested Citation

  • Fabiana Gomez & David Pacini, 2015. "Counting Biased Forecasters: An Application of Multiple Testing Techniques," Bristol Economics Discussion Papers 15/661, School of Economics, University of Bristol, UK.
    • Handle: RePEc:bri:uobdis:15/661
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    References listed on IDEAS

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    More about this item

    Keywords

    Biased Forecasters; Multiple Testing; False Discovery Rate.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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