Internal consistency of survey respondents’ forecasts: evidence based on the Survey of Professional Forecasters

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Clements, Michael P.;
  • Publisher: University of Warwick, Department of Economics
  • Subject: HB
    mesheuropmc: health care economics and organizations | social sciences | population characteristics

We ask whether the different types of forecasts made by individual survey respondents are mutually consistent, using the SPF survey data. We compare the point forecasts and central tendencies of probability distributions matched by individual respondent, and compare the... View more
  • References (15)
    15 references, page 1 of 2

    Andersen, T. G., Bollerslev, T., Diebold, F. X., and Labys, P. (2003). Modelling and forecasting realized volatility. Econometrica, 71, 579–625.

    Anderson, T. G., and Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accuate forecasts. International Economic Review, 39, 885–905.

    Baillie, R. T., and Bollerslev, T. (1992). Prediction in dynamic models with time-dependent conditional variances. Journal of Econometrics, 52, 91–113.

    Berkowitz, J. (2001). Testing density forecasts, with applications to risk management. Journal of Business and Economic Statistics, 19, 465–474.

    Bollerslev, T. (1986). Generalised autoregressive conditional heteroskedasticity. Journal of Econometrics, 51, 307–327.

    Bomberger, W. A. (1996). Disagreement as a measure of uncertainty. Journal of Money, Credit and Banking, 28, 381–392.

    Capistrán, C., and Timmermann, A. (2005). Disagreement and biases in in‡ation forecasts. Mimeo, Banco de México and UCSD.

    Christo¤ersen, P. F. (1998). Evaluating interval forecasts. International Economic Review, 39, 841–862.

    Christo¤ersen, P. F., and Diebold, F. X. (1997). Optimal prediction under asymmetric loss. Econometric Theory, 13, 808–817.

    Clements, M. P. (2006). Evaluating the Survey of Professional Forecasters probability distributions of expected in‡ation based on derived event probability forecasts. Empirical Economics, 31, No. 1, 49–64.

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