2020/06/05 by Yinchu Zhu, Allan Timmermann, Zhu, Yinchu +1 · 1 citation
Decision Sciences · Economics, Econometrics and Finance · #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Forecasting Techniques and Applications #Methodology (stat.ME) #Monetary Policy and Economic Impact
paper · pdf · doi:10.48550/arxiv.2006.03238
openalex publication_date 2020/06/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The approach for testing equal predictive accuracy for pairs of forecasting models proposed by Giacomini and White (2006) assumes that the parameters of the underlying forecasting models are estimated using a rolling window of fixed width and incorporates the effect of parameter estimation in the null hypothesis. We show that a necessary and sufficient condition for the conditionally expected loss differential of two forecasting models to be a martingale difference sequence is that the outcome is a simple average of the two forecasts. When the forecasts contain parameter estimation errors, this means that the conditional mean of the outcome has to be a function of past estimation errors--a condition that fails in many situations. We also show that the null can fail even in the absence of parameter estimation for many types of stochastic processes in common use.