论文检索

多元时间序列因果性的稳健最优检验AI 解读

Here, we derive optimal rank-based tests for noncausality in the sense of Granger between two multivariate time series. Assuming that the global process admits a joint stationary vector autoregressive (VAR) representation with an elliptically symmetric innovation density, both no feedback and one direction causality hypotheses are tested. Using the characterization of noncausality in the VAR context, the local asymptotic normality (LAN) theory described in Le Cam (1986,…

误差不相关的 ARMA 模型诊断检验AI 解读

We consider tests for lack of fit in ARMA models with nonindependent innovations. In this framework, the standard Box–Pierce and Ljung–Box portmanteau tests can perform poorly. Specifically, the usual text book formulas for asymptotic distributions are based on strong assumptions and should not be applied without careful consideration. In this article we derive the asymptotic covariance matrix of a vector of autocorrelations for residuals of ARMA models under weak…

多元ARMA模型中两个变量向量之间的因果性检验AI 解读

In the analysis of economic time series, a question often raised is whether a vector of variables causes another one in the sense of Granger. Most of the literature on this topic is concerned with bivariate relationships or uses finite-order autoregressive specifications. The purpose of this article is to develop a causality analysis in the sense of Granger for general vector autoregressive moving average (ARMA) models. We give a definition of Granger noncausality between…

多元时间序列序列相关系数的渐近协方差结构AI 解读

A simple proof of the asymptotic joint normality of any finite set of serial correlations of a general multivariate second-order stationary time series is presented and an explicit formula for the asymptotic covariance between two serial correlations is derived. The latter extends Bartlett's formula for the asymptotic covariance between two sample cross-correlations.

多变量时间序列中序列相关的渐近协方差结构AI 解读

A simple proof of the asymptotic joint normality of any finite set of serial correlations of a general multivariate second-order stationary time series is presented and an explicit formula for the asymptotic covariance between two serial correlations is derived. The latter extends Bartlett's formula for the asymptotic covariance between two sample cross-correlations.

一阶积分移动平均过程样本自协方差函数的渐近行为AI 解读

In this paper, we derive the first two asymptotic moments of the autooovariance function of a time series generated from an integrated moving average process of order (1,1). In particular, it is found that the expected value of the sample autocovariance at lag k is approximately proportional to the length of the series. This result suggests a method of checking whether a nonstationary time series can be described by an integrated moving average process of order (1,1) and its…