Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models
通过蒙特卡洛模拟比较参数驱动与观测驱动模型在预测时变参数时的表现，发现广义自回归得分模型预测精度与正确设定的参数驱动模型相当，且优于传统观测驱动模型。
Accepted for an article forthcoming in the <I>Review of Economics and Statics</I>. Volume 97, 2015.<P> We study whether and when parameter-driven time-varying parameter models lead to forecasting gains over observation-driven models. We consider dynamic count, intensity, duration, volatility and copula models, including new specifications that have not been studied earlier in the literature. In an extensive Monte Carlo study, we find that observation-driven generalised autoregressive score (GAS) models have similar predictive accuracy to correctly specified parameter-driven models. In most cases, differences in mean squared errors are smaller than 1% and model confidence sets have low power when comparing these two alternatives. We also find that GAS models outperform many familiar observation-driven models in terms of forecasting accuracy. The results point to a class of observation-driven models with comparable forecasting ability to parameter-driven models, but lower computational complexity.