论文标题
时间序列模型的新拟合测试
New Goodness-of-Fit Tests for Time Series Models
论文作者
论文摘要
本文提出了综合Portmanteau测试，以对比时间序列模型的充分性。测试统计数据基于结合条件残差的自相关函数，条件平方残差的自相关功能以及这些残差与其正方形之间的互相关函数。最大似然估计器用于在一般时间序列模型（包括ARMA，GARCH和其他非线性结构）下得出所提出的测试统计数据的渐近分布。一项广泛的蒙特卡洛模拟研究表明，在许多情况下，提出的测试成功控制了I型错误概率，并且比其他竞争对手测试具有更多的功率。从标准普尔500标准普尔500的92家公司的每周股票收益中的两项申请表明了拟议的测试的实际使用。
This article proposes omnibus portmanteau tests for contrasting adequacy of time series models. The test statistics are based on combining the autocorrelation function of the conditional residuals, the autocorrelation function of the conditional squared residuals, and the cross-correlation function between these residuals and their squares. The maximum likelihood estimator is used to derive the asymptotic distribution of the proposed test statistics under a general class of time series models, including ARMA, GARCH, and other nonlinear structures. An extensive Monte Carlo simulation study shows that the proposed tests successfully control the type I error probability and tend to have more power than other competitor tests in many scenarios. Two applications to a set of weekly stock returns for 92 companies from the S&P 500 demonstrate the practical use of the proposed tests.
