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ON THE RECOVERABILITY OF FORECASTERS’ PREFERENCES

  • Robert P. Lieli (a1) and Maxwell B. Stinchcombe (a2)

Abstract

We study the problem of identifying a forecaster’s loss function from observations on forecasts, realizations, and the forecaster’s information set. Essentially different loss functions can lead to the same forecasts in all situations, though within the class of all continuous loss functions, this is strongly nongeneric. With the small set of exceptional cases ruled out, generic nonparametric preference recovery is theoretically possible, but identification depends critically on the amount of variation in the conditional distributions of the process being forecast. There exist processes with sufficient variability to guarantee identification, and much of this variation is also necessary for a process to have universal identifying power. We also briefly address the case in which the econometrician does not fully observe the conditional distributions used by the forecaster, and in this context we provide a practically useful set identification result for loss functions used in forecasting binary variables.

Copyright

Corresponding author

*Address correspondence to Robert P. Lieli, Department of Economics, Central European University, Nádor u. 9, 1051 Budapest, Hungary; e-mail: lielir@ceu.hu.

References

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Anderson, R.M. & Zame, W.R. (2001) Genericity with infinitely many parameters. Advances in Theoretical Economics 1, 162.
Baire, R. (1899) Sur les Fonctions de Variables Réelles (thèse). Annali di Matematica Pura ed Applicata (ser. 3) 3, 1123.
Capistran, C. & Timmermann, A. (2009) Disagreement and biases in inflation expectations. Journal of Money, Credit and Banking 41, 365396.
Corbae, D., Stinchcombe, M.B., & Zeman, J. (2009) An Introduction to Mathematical Analysis for Economic Theory and Econometrics. Princeton University Press.
Elliott, G., Komunjer, I., & Timmermann, A. (2003) Estimating Loss Function Parameters. Working Paper, Department of Economics, University of California, San Diego.
Elliott, G., Komunjer, I., & Timmermann, A. (2005) Estimation and testing of forecast rationality under flexible loss. Review of Economic Studies 72, 11071125.
Elliott, G., Komunjer, I., & Timmermann, A. (2008) Biases in macroeconomic forecasts: Irrationality or asymmetric loss. Journal of the European Economic Association 6, 122157.
Granger, C.W.J. (1969) Prediction with a generalized cost of error function. Operational Research Quarterly 20, 199207.
Granger, C.W.J. & Machina, M. (2006) Forecasting and decision theory. In Elliott, G., Granger, C.W.J., & Timmermann, A. (eds.), Handbook of Economic Forecasting. Elsevier.
Komunjer, I. & Vuong, Q. (2010) Semiparametric efficiency bound in time series models for conditional quantiles. Econometric Theory 26, 383405.
Mincer, J. & Zarnowitz, V. (1969) The evaluation of economic forecasts. In Mincer, J. (ed.), Economic Forecasts and Expectations. Columbia University Press.
Morris, S. & Ui, T. (2004) Best response equivalence. Games and Economic Behavior 49, 260287.
Patton, A.J. & Timmermann, A. (2005) Testable Implications of Forecast Optimality. Working paper, Department of Economics, London School of Economics and Political Science.
Patton, A.J. & Timmermann, A. (2007) Testing forecast optimality under unknown loss. Journal of the American Statistical Association 102, 11721184.

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