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Econometrics 2015, 3(3), 590-609; doi:10.3390/econometrics3030590

A Kolmogorov-Smirnov Based Test for Comparing the Predictive Accuracy of Two Sets of Forecasts

1
Statistical Research Centre, The Business School, Bournemouth University, Bournemouth, BH8 8EB, UK
2
Institute for International Energy Studies (IIES), Tehran 1967743711, I.R., Iran
*
Author to whom correspondence should be addressed.
Academic Editor: Kerry Patterson
Received: 17 February 2015 / Revised: 24 July 2015 / Accepted: 28 July 2015 / Published: 4 August 2015
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Abstract

This paper introduces a complement statistical test for distinguishing between the predictive accuracy of two sets of forecasts. We propose a non-parametric test founded upon the principles of the Kolmogorov-Smirnov (KS) test, referred to as the KS Predictive Accuracy (KSPA) test. The KSPA test is able to serve two distinct purposes. Initially, the test seeks to determine whether there exists a statistically significant difference between the distribution of forecast errors, and secondly it exploits the principles of stochastic dominance to determine whether the forecasts with the lower error also reports a stochastically smaller error than forecasts from a competing model, and thereby enables distinguishing between the predictive accuracy of forecasts. We perform a simulation study for the size and power of the proposed test and report the results for different noise distributions, sample sizes and forecasting horizons. The simulation results indicate that the KSPA test is correctly sized, and robust in the face of varying forecasting horizons and sample sizes along with significant accuracy gains reported especially in the case of small sample sizes. Real world applications are also considered to illustrate the applicability of the proposed KSPA test in practice. View Full-Text
Keywords: forecast accuracy; Kolmogorov-Smirnov; stochastic dominance; non-parametric; size and power; predictive accuracy; KSPA; Diebold-Mariano; DM forecast accuracy; Kolmogorov-Smirnov; stochastic dominance; non-parametric; size and power; predictive accuracy; KSPA; Diebold-Mariano; DM
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Hassani, H.; Silva, E.S. A Kolmogorov-Smirnov Based Test for Comparing the Predictive Accuracy of Two Sets of Forecasts. Econometrics 2015, 3, 590-609.

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