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多元线性时间序列的非参数与半参数检验AI 解读

We formulate nonparametric and semiparametric hypothesis testing of multivariate stationary linear time series in a unified fashion and propose new test statistics based on estimators of the spectral density matrix. The limiting distributions of these test statistics under null hypotheses are always normal distributions, and they can be implemented easily for practical use. If null hypotheses are false, as the sample size goes to infinity, they diverge to infinity and…

分数阶系统中的协整秩确定AI 解读

This paper develops methods of investigating the existence and extent of cointegration in fractionally integrated systems. We focus on stationary series, with some discussion of extension to nonstationarity. The setting is semiparametric, so that modelling is effectively confined to a neighbourhood of frequency zero. We first discuss the definition of fractional cointegration. The initial step of cointegration analysis entails partitioning the vector series into subsets with…

长记忆平稳误差回归模型中最小二乘估计量的渐近性质AI 解读

We consider asymptotic properties of the least squares estimator (LSE) in a regression model with long-memory stationary errors. First we derive a necessary and sufficient condition that the LSE be asymptotically efficient relative to the best linear unbiased estimator (BLUE). Then we derive the asymptotic distribution of the LSE under a condition on the higher-order cumulants of the white-noise process of the errors.

具有长记忆平稳误差的回归模型估计AI 解读

We consider estimation of a regression model with long-memory stationary errors. First, we estimate the regression parameters by the least-squares estimator (LSE) and, next, those describing the correlation structure of the error terms by using the residuals obtained from the LSE. Certain regularity conditions introduced to develop the asymptotic theory no longer hold in this model. In this situation we derive asymptotic properties of the preceding estimation procedure.