论文标题
引导时间序列强大的混合Portmanteau测试
Bootstrapping a Powerful Mixed Portmanteau Test for Time Series
论文作者
论文摘要
提出了一个新的Portmanteau测试统计量，用于检测时间序列数据中的非线性。在本文中，我们详细介绍了toeplitz自相关矩阵，以与残差和平方残留块矩阵的自相关和互相关。我们使用MTH自相关和互相关块矩阵的确定因素的日志得出了新的Portmanteau测试统计量。提出的测试统计量的渐近分布得出是卡方分布的线性组合，可以通过伽马分布近似。该测试用于确定某些固定时间序列模型的线性和非线性依赖性。结果表明，在许多情况下，新测试与其渐近分布的收敛性具有比其他测试更高的功率合理。我们通过研究沃达丰卡塔尔和Nikkei-300每日回报中的线性和非线性效应来证明拟议测试的效率。
A new portmanteau test statistic is proposed for detecting nonlinearity in time series data. In this paper, we elaborate on the Toeplitz autocorrelation matrix to the autocorrelation and cross-correlation of residuals and squared residuals block matrix. We derive a new portmanteau test statistic using the log of the determinant of the mth autocorrelations and cross-correlations block matrix. The asymptotic distribution of the proposed test statistic is derived as a linear combination of chi-squared distributions and can be approximated by a gamma distribution. This test is applied to identify the linearity and nonlinearity dependency of some stationary time series models. It is shown that the convergence of the new test to its asymptotic distribution is reasonable with higher power than other tests in many situations. We demonstrate the efficiency of the proposed test by investigating linear and nonlinear effects in Vodafone Qatar and Nikkei-300 daily returns.
