Testing Parametric Conditional Distributions of Dynamic Models
提出一种非参数检验方法，用于检验动态模型（如GARCH、ARMA）的参数条件分布，适用于金融收益数据的正态性或t分布检验。
This paper proposes a nonparametric test for parametric conditional distributions of dynamic models. The test is of the Kolmogorov type coupled with Khmaladze's martingale transformation. It is asymptotically distribution-free and has nontrivial power against root-n local alternatives. The method is applicable for various dynamic models, including autoregressive and moving average models, generalized autoregressive conditional heteroskedasticity (GARCH), integrated GARCH, and general nonlinear time series regressions. The method is also applicable for cross-sectional models. Finally, we apply the procedure to testing conditional normality and the conditional t-distribution in a GARCH model for the NYSE equal-weighted returns. © 2003 President and Fellows of Harvard College and the Massachusetts Institute of Technology.