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Testable Implications of Forecast Optimality

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  • Andrew J. Patton
  • Allan Timmermann

Abstract

Evaluation of forecast optimality in economics and finance has almost exclusively been conducted on the assumption of mean squared error loss under which forecasts should be unbiased and forecast errors serially uncorrelated at the single period horizon with increasing variance as the forecast horizon grows. This paper considers properties of optimal forecasts under general loss functions and establishes new testable implications of forecast optimality. These hold when the forecaster's loss function is unknown but testable restrictions can be imposed on the data generating process, trading off conditions on the data generating process against conditions on the loss function. Finally, we propose flexible parametric estimation of the forecaster's loss function, and obtain a test of forecast optimality via a test of over-identifying restrictions.

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Bibliographic Info

Paper provided by Suntory and Toyota International Centres for Economics and Related Disciplines, LSE in its series STICERD - Econometrics Paper Series with number /2005/485.

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Date of creation: Jan 2005
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Handle: RePEc:cep:stiecm:/2005/485

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Web page: http://sticerd.lse.ac.uk/_new/publications/default.asp

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Keywords: forecast evaluation; loss function; rationality tests;

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Cited by:
  1. Raffella Giacomini & Barbara Rossi, 2005. "Detecting and Predicting Forecast Breakdowns," UCLA Economics Working Papers 845, UCLA Department of Economics.

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