Working with the Forecast Data

  • Michael P. Clements
Part of the Palgrave Texts in Econometrics book series (PTEC)


The forecast histograms reported by the respondents to some surveys, such as the US SPF, are typically viewed as estimates of the individuals’ subjective distributions. There are various ways of calculating the quantities of interest from the histograms, and these are reviewed. Quantities of interest might include first moments such as means and modes, as well as variances, and the forecast probability that Y will be less than some value. The last is typically required by popular methods of evaluating the histograms. The difficulties arise because the histograms do not fully reveal the respondents’ probability density/distribution function, and the methods include non-parametric techniques, as well as the fitting of parametric distributions, including distributions which allow for asymmetry in the individual’s underlying assessments.


  1. Boero, G., Smith, J., & Wallis, K. F. (2015). The measurement and characteristics of professional forecasters’ uncertainty. Journal of Applied Econometrics, 30(7), 1013–1234.CrossRefGoogle Scholar
  2. Balakrishnan, N., & Nevzorov, V. B. (2003). A primer on statistical distributions. Hoboken, NJ: Wiley.CrossRefGoogle Scholar
  3. Diebold, F. X., Tay, A. S., & Wallis, K. F. (1999). Evaluating density forecasts of inflation: The Survey of Professional Forecasters. In R. F. Engle & H. White (Eds.), Cointegration, causality and forecasting: A Festschrift in Honour of Clive Granger (pp. 76–90). Oxford: Oxford University Press.Google Scholar
  4. Engelberg, J., Manski, C. F., & Williams, J. (2009). Comparing the point predictions and subjective probability distributions of professional forecasters. Journal of Business and Economic Statistics, 27(1), 30–41.CrossRefGoogle Scholar
  5. Giordani, P., & Söderlind, P. (2003). Inflation forecast uncertainty. European Economic Review, 47(6), 1037–1059.CrossRefGoogle Scholar
  6. Kendall, M. G., Stuart, A., & Ord, J. K. (1987). Advanced theory of statistics (5th ed., Vols. 1 and 2). London: Charles Griffin and Co.Google Scholar
  7. López-Pérez, V. (2015). Measures of macroeconomic uncertainty for the ECB’s Survey of Professional Forecasters. In M. Donduran, M. Uzunöz, E. Bulut, T. O. Cadirci, & T. Aksoy (Eds.), Proceedings of the First International Conference on Social Sciences: Istanbul: Yildiz Technical University.Google Scholar
  8. López-Pérez, V. (2016). Does Uncertainty Affect Participation in the European Central Bank’s Survey of Professional Forecasters?. Mimeo, Universidad Politécnica de Cartagena.Google Scholar
  9. Zarnowitz, V., & Lambros, L. A. (1987). Consensus and uncertainty in economic prediction. Journal of Political Economy, 95(3), 591–621.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2019

Authors and Affiliations

  • Michael P. Clements
    • 1
  1. 1.ICMA Centre, Henley Business SchoolUniversity of ReadingWheatleyUK

Personalised recommendations