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Out-of-Sample Forecast Tests Robust to Window Size Choice

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Author Info

  • Barbara Rossi
  • Atsushi Inoue

Abstract

This paper proposes new methodologies for evaluating out-of-sample forecasting performance that are robust to the choice of the estimation window size. The methodologies involve evaluating the predictive ability of forecasting models over a wide range of window sizes. We show that the tests proposed in the literature may lack power to detect predictive ability, and might be subject to data snooping across different window sizes if used repeatedly. An empirical application shows the usefulness of the methodologies for evaluating exchange rate models' ’forecasting ability.

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Bibliographic Info

Paper provided by Duke University, Department of Economics in its series Working Papers with number 11-04.

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Length: 48
Date of creation: 2011
Date of revision:
Handle: RePEc:duk:dukeec:11-04

Contact details of provider:
Postal: Department of Economics Duke University 213 Social Sciences Building Box 90097 Durham, NC 27708-0097
Phone: (919) 660-1800
Fax: (919) 684-8974
Web page: http://econ.duke.edu/

Related research

Keywords: Predictive Ability Testing; Forecast Evaluation; Estimation Window;

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Cited by:
  1. Todd Clark & Michael W. McCracken, 2011. "Advances in forecast evaluation," Working Paper 1120, Federal Reserve Bank of Cleveland.
  2. Peter Reinhard Hansen & Allan Timmermann, 2012. "Choice of Sample Split in Out-of-Sample Forecast Evaluation," CREATES Research Papers 2012-43, School of Economics and Management, University of Aarhus.
  3. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.

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