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具有时间序列误差的回归模型AI 解读

The time series regression models in which the errors of regression equations follow stationary or nonstationary autoregressive moving average models are considered. Convergence properties of the sample autocorrelation function of observed series and the least squares estimates of the linear regression parameters are shown. Based upon these results, a procedure for specifying the tentative order of the mixed ARMA errors is proposed. Two examples are given.

平稳与非平稳 ARMA 模型的自回归参数一致估计与扩展样本自相关函数AI 解读

A unified approach for the tentative specification of the order of mixed stationary and nonstationary ARMA models is proposed. For the ARMA models, an iterative regression procedure is given to produce consistent estimates of the autoregressive parameters. An extended sample autocorrelation function based on these consistent estimates is then defined and used for order determination. One of the advantages of this new approach is that it eliminates the need to determine,…

ARMA 模型中自回归参数最小二乘估计的一致性AI 解读

A unified treatment of the consistency properties of the ordinary least squares estimates in an autoregressive fitting of time series from nonstationary or stationary autoregressive moving average models is given. For a given model, the orders of autoregressions which produce consistent estimates are obtained and the limiting values, hence the biases, of the estimates of other autoregressions are investigated.

多元时间序列建模与扩展样本互相关AI 解读

This article provides an expository account of the multivariate autoregressive moving average models and proposes an extended sample cross-correlation approach for practical model identification. An iterative model building procedure for applying these models to real data is discussed and demonstrated by analyzing the 5-series U.S. Hog Data.