Finite Sample Weighting of Recursive Forecast Errors

37 Pages Posted: 24 Dec 2013 Last revised: 11 Jul 2014

See all articles by Chris Brooks

Chris Brooks

University of Reading - ICMA Centre

Simon Burke

University of Reading

Silvia Stanescu

University of Kent - Kent Business School

Date Written: June 2014

Abstract

This paper proposes and tests a new framework for weighting recursive out-of-sample prediction errors in accordance with their corresponding in-sample estimation uncertainty. In essence, we show how as much information from the sample as possible can be used in the evaluation of prediction accuracy by commencing the forecasts at the earliest opportunity and weighting the prediction errors. We demonstrate through a Monte Carlo study that when only a small sample is available the proposed framework can select the correct model from a set of candidate models considerably more often than the existing standard approach. We also show that the proposed weighting approaches result in tests of equal predictive accuracy which have much better size than the standard approach. An application to a set of exchange rate data highlights relevant differences in the results of tests of predictive accuracy based on the standard approach versus the framework proposed in this paper.

Keywords: forecast evaluation; forecast comparison; recursive model estimation; mean squared error; forecast weighting scheme

JEL Classification: C52, C53

Suggested Citation

Brooks, Chris and Burke, Simon and Stanescu, Silvia, Finite Sample Weighting of Recursive Forecast Errors (June 2014). Available at SSRN: https://ssrn.com/abstract=2371361 or http://dx.doi.org/10.2139/ssrn.2371361

Chris Brooks (Contact Author)

University of Reading - ICMA Centre ( email )

Whiteknights Park
P.O. Box 242
Reading RG6 6BA
United Kingdom
+44 118 931 82 39 (Phone)
+44 118 931 47 41 (Fax)

Simon Burke

University of Reading ( email )

Whiteknights
Reading, Berkshire RG6 6AH
United Kingdom

Silvia Stanescu

University of Kent - Kent Business School ( email )

Canterbury, Kent CT2 7PE
United Kingdom

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
61
Abstract Views
668
rank
388,454
PlumX Metrics