Does Statistical Significance Help to Evaluate Predictive Performance of Competing Models?

European Journal of Economic and Political Studies 8, 1-13

13 Pages Posted: 25 Mar 2019

See all articles by Levent Bulut

Levent Bulut

Valdosta State University - Department of Economics and Finance

Date Written: 2105

Abstract

In Monte Carlo experiment with simulated data, we show that as a point forecast criterion, the Clark and West's (2006) unconditional test of mean squared prediction errors does not reflect the relative performance of a superior model over a relatively weaker one. The simulation results show that even though the mean squared prediction errors of a constructed superior model is far below a weaker alternative, the Clark- West test does not reflect this in their test statistics. Therefore, studies that use this statistic in testing the predictive accuracy of alternative exchange rate models, stock return predictability, inflation forecasting, and unemployment forecasting should not weight too much on the magnitude of the statistically significant Clark-West tests statistics.

Keywords: Model Comparison, Predictive Accuracy, Point-Forecast Criterion, the Clark and West Test, Monte-Carlo Methods, Forecast Comparison

JEL Classification: F37, F47, G17, C52

Suggested Citation

Bulut, Levent, Does Statistical Significance Help to Evaluate Predictive Performance of Competing Models? (2105). European Journal of Economic and Political Studies 8, 1-13, Available at SSRN: https://ssrn.com/abstract=3345353

Levent Bulut (Contact Author)

Valdosta State University - Department of Economics and Finance ( email )

Valdosta State University Harley Langdale, Jr. Col
Valdosta, GA 31698
United States

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