Nonparametric comparison of predicitve accuracy - San Diego, Clyde Anne R. (University of the Philippines, 2010)
Abstract (A bootstrap-based method of comparing predictive accuracy of two forecasts is proposed. The approach use simple standardization for cross-sectional data and Normalizing and Variance Stabilizing (NoVas) transformation for time series data. Transformation is necessary to standardize differential loss measures in order for conditional heteroskedasticity not to mask difference or exaggerate random differences between two forecast errors. Simulation study indicated robustness of the proposed test in the presence of heteroskedastic errors, poor model fit and multicollinearity in cross-sectional data. For time series data, the proposed test illustrated robustness when the times series is short and volatility is present).