题 目：A Simple Automatic Portmanteau Test for Conditional Goodness-of-Fit in Dynamic Models

讲座人：杜在超副教授

西南财经大学

时 间：2011年12月1日（星期四）16：30—18：00

地 点：经济楼D110

主办单位：王亚南经济研究院

经济学院

摘要: In this paper, we propose a data-driven Portmanteu test for conditional goodness-of-fit in dynamic models. Our method is based on the fact that under the correct specification of the conditional distribution the generalized errors obtained after the conditional probability integral transformation are iid U[0,1]. The proposed test is a modified Box-Pierce statistic applied to the generalized errors, with a data-driven choice for the number of autocorrelations used. The test explicitly takes into account of the parameter estimation effect, and as a result it has a convenient standard chi-squared limit distribution. Hence, the main distinctive feature of our approach is its simplicity. Furthermore, unlike existing approaches, our approach is applicable to a wide class of models, including ARMA-GARCH models with time varying higher order moments, such as Hansens (1994) skewed t model. A simulation study shows that our test has a satisfactory size and power performance. Finally, an empirical application to the Nikkei Index data highlights the merits of the proposed test over competing alternatives.