论文检索

摘要暂缺

预测组合的基准化方法AI 解读

This article is concerned with the development of a statistical model-based approach to optimally combine forecasts derived from an extrapolative model, such as an autoregressive integrated moving average (ARIMA) time series model, with forecasts of a particular characteristic of the same series obtained from independent sources. The methods derived combine the strengths of all forecasting approaches considered in the combinational scheme. The implications of the general…

经济时间序列的基准校准AI 解读

This article develops a statistical model-based approach to the benchmarking problem. Benchmarking is done when data from a monthly sample survey are combined with data from an annual census for the purpose of improving the survey estimates. Previous authors have used numerical analysis techniques to derive methods to perform benchmarking. This article formulates the benchmarking problem in a statistical framework and uses modern times series methods to derive a solution.…

交易日效应时间序列模型中的多重共线性问题AI 解读

This article discusses the multicollinearity problems associated with the estimation of time series models influenced by trading-day variation. An analysis of the design matrix is performed, and measures of the degree of multicollinearity are computed. The characteristics of the design matrix of a popular parameterization are also analyzed, and it is shown that in some cases use of this reparameterization significantly alleviates the multicollinearity problem.

含交易日变化的时间序列建模中的多重共线性问题AI 解读

Teresita S. Salinas, Steven C. Hillmer, Multicollinearity Problems in Modeling Time Series with Trading-Day Variation, Journal of Business & Economic Statistics, Vol. 5, No. 3 (Jul., 1987), pp. 431-436

基于模型的季节调整程序的变异性度量AI 解读

An algorithm is derived that develops measures of variability for the estimates of the nonseasonal component computed from a model-based seasonal adjustment procedure. The measures of variability are developed from signal extraction theory. Properties of components of the variance are developed, and the behavior of the variance is investigated for one popular time series model. The results are illustrated by using real data.

摘要暂缺

摘要暂缺

经济时间序列季节调整中的问题AI 解读

In the first part of this article, we briefly review the history of seasonal adjustment and statistical time series analysis in order to understand why seasonal adjustment methods have evolved into their present form. This review provides insight into some of the problems that must be addressed by seasonal adjustment procedures and points out that advances in modern time series analysis raise the question of whether seasonal adjustment should be performed at all. This in…

摘要暂缺

平稳多元自回归移动平均模型的似然函数AI 解读

Procedures to estimate parameters in multivariate autoregressive moving average (ARMA) models are developed. Gaussian errors are assumed. Exact maximum likelihood estimation procedures are developed for pure moving average models. Approximate procedures are obtained to estimate stationary mixed ARMA models. Properties of the estimates and an example are given.