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Estimating Loss Function Parameters

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Author Info
Elliott, Graham
Komunjer, Ivana
Timmermann, Allan G

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Abstract

In situations where a sequence of forecasts is observed, a common strategy is to examine ‘rationality’ conditional on a given loss function. We examine this from a different perspective - supposing that we have a family of loss functions indexed by unknown shape parameters, then given the forecasts can we back out the loss function parameters consistent with the forecasts being rational even when we do not observe the underlying forecasting model? We establish identification of the parameters of a general class of loss functions that nest popular loss functions as special cases and provide estimation methods and asymptotic distributional results for these parameters. The methods are applied in an empirical analysis of IMF and OECD forecasts of budget deficits for the G7 countries. We find that allowing for asymmetric loss can significantly change the outcome of empirical tests of forecast rationality.

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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 3821.

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Date of creation: Mar 2003
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Handle: RePEc:cpr:ceprdp:3821

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Related research
Keywords: asymmetric loss; imf; macroeconomic forecasting; oecd;

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Find related papers by JEL classification:
C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - General
E00 - Macroeconomics and Monetary Economics - - General - - - General

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Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Stan Hurn & Ralf Becker, 2006. "Testing for nonlinearity in mean in the presence of heteroskedasticity," Stan Hurn Discussion Papers 2006-02, School of Economics and Finance, Queensland University of Technology. [Downloadable!]
    Other versions:
  2. Allan Timmermann & Andrew J. Patton, 2004. "Properties of Optimal Forecasts," Econometric Society 2004 North American Winter Meetings 234, Econometric Society. [Downloadable!]
    Other versions:
  3. Adrian Pagan & Hashem Pesaran, 2007. "Econometric Analysis of Structural Systems with Permanent and Transitory Shocks. Working paper #7," NCER Working Paper Series 7, National Centre for Econometric Research. [Downloadable!]
  4. Andrew J. Patton & Allan Timmermann, 2005. "Testable Implications of Forecast Optimality," STICERD - Econometrics Paper Series /2005/485, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE. [Downloadable!]
  5. Graham Elliott & Ivana Komunjer & Allan Timmermann, 2005. "Biases In Macroeconomic Forecasts: Irrationality Or Asymmetric Loss?," CAMA Working Papers 2005-14, Australian National University, Centre for Applied Macroeconomic Analysis. [Downloadable!]
    Other versions:
  6. Boriss Siliverstovs & Tom Engsted & Niels Haldrup, 2003. "Long-Run Forecasting in Multicointegrated Systems," Discussion Papers of DIW Berlin 381, DIW Berlin, German Institute for Economic Research. [Downloadable!]
    Other versions:
  7. Yang Yang & Tae-Hwy Lee, 2004. "Bagging Binary Predictors for Time Series," Econometric Society 2004 Far Eastern Meetings 512, Econometric Society. [Downloadable!]
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