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构建具有聚类结构的动态因子模型的稳健程序AI 解读

Dynamic factor models provide a useful way to model large sets of time series. These data often have heterogeneity and cluster structure and the formulation and estimation of dynamic factor models should be adapted to these features. This article presents a procedure to fit Dynamic Factor Models with Cluster Structure (DFMCS), where some of the factors are global and others group-specific, to heterogeneous data that may include multivariate additive outliers and level…

使用单侧动态主成分预测多条时间序列AI 解读

We define one-sided dynamic principal components (ODPC) for time series as linear combinations of the present and past values of the series that minimize the reconstruction mean squared error. Usually dynamic principal components have been defined as functions of past and future values of the series and therefore they are not appropriate for forecasting purposes. On the contrary, it is shown that the ODPC introduced in this article can be successfully used for forecasting…

广义动态主成分AI 解读

Brillinger defined dynamic principal components (DPC) for time series based on a reconstruction criterion. He gave a very elegant theoretical solution and proposed an estimator which is consistent under stationarity. Here, we propose a new enterally empirical approach to DPC. The main differences with the existing methods—mainly Brillinger procedure—are (1) the DPC we propose need not be a linear combination of the observations and (2) it can be based on a variety of loss…

ARMA 模型的稳健估计AI 解读

This paper introduces a new class of robust estimates for ARMA models. They are M-estimates, but the residuals are computed so the effect of one outlier is limited to the period where it occurs. These estimates are closely related to those based on a robust filter, but they have two important advantages: they are consistent and the asymptotic theory is tractable. We perform a Monte Carlo where we show that these estimates compare favorably with respect to standard…

利用投影寻踪检测多元时间序列异常值AI 解读

In this article we use projection pursuit methods to develop a procedure for detecting outliers in a multivariate time series. We show that testing for outliers in some projection directions can be more powerful than testing the multivariate series directly. The optimal directions for detecting outliers are found by numerical optimization of the kurtosis coefficient of the projected series. We propose an iterative procedure to detect and handle multiple outliers based on a…

ARMAX 时间序列模型的多折预测验证AI 解读

This article presents a new procedure for multifold predictive validation in time series. The procedure is based on the so-called “filtered residuals,” in-sample prediction errors evaluated in such a way that they are similar to out-of-sample ones. The filtered residuals are obtained from parameters estimated by eliminating from the estimation process the estimated innovations at the points to be predicted. Thus, instead of using the deletion of observations to validate the…

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用于时间序列拟合不足诊断的综合检验AI 解读

A new portmanteau test for time series, more powerful than the tests of Ljung and Box and Monti, is proposed. The test is based on the mth root of the determinant of the mth autocorrelation matrix. It is shown that the proposed statistic is a function of all of the squared multiple correlation coefficients of the regressions of the residuals on their lags when the number of lags goes from 1 to m. It can also be written as a function of the first m partial autocorrelation…

大型回归问题中的快速离群诊断程序AI 解读

We propose a procedure for computing a fast approximation to regression estimates based on the minimization of a robust scale. The procedure can be applied with a large number of independent variables where the usual algorithms require an unfeasible or extremely costly computer time. Also, it can be incorporated in any high-breakdown estimation method and may improve it with just little additional computer time. The procedure minimizes the robust scale over a set of…

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线性约束下多元时间序列干预效应的测量：银行业实例AI 解读

The authors consider the problem of estimating the effects of an intervention on a time-series vector subjected to a linear constraint. Minimum variance linear and unbiased estimators are provided for two different formulations of the problem--(1) when a multivariate intervention analysis is carried out and an adjustment is needed to fulfill the restriction and (2) when a univariate intervention analysis was performed on the aggregate series obtained from the linear…

家庭批量购买情形下的食品支出估计AI 解读

The aim of this article is the estimation of annual food expenditures with limited information about bulk purchases with data from a Spanish household-budget survey for 1990-91. Three alternatives are compared. The first, currently used for official purposes, does not use all the information. The second uses all the available information in a rough way. The third assumes a formal model for the unknown frequency of purchases. The three alternatives are compared by a…

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利用影响矩阵识别线性回归中的高影响观测子集AI 解读

SUMMARY This paper presents a new method to identify influential subsets in linear regression problems. The procedure uses the eigenstructure of an influence matrix which is defined as the matrix of uncentred covariances of the effect on the whole data set of deleting each observation, normalized to include the univariate Cook statistics on the diagonal. It is shown that the eigenstructure of the influence matrix is useful to identify influential subsets and a procedure for…

线性模型异常值检测概率方法的比较AI 解读

This paper compares the use of two posterior probability methods to deal with outliers in linear models. We show that putting together diagnostics that come from the mean-shift and variance-shift models yields a procedure that seems to be more effective than the use of probabilities computed from the posterior distributions of actual realized residuals. The relation of the suggested procedure to the use of a certain predictive distribution for diagnostics is derived.

This article studies how to identify influential observations in univariate autoregressive integrated moving average time series models and how to measure their effects on the estimated parameters of the model. The sensitivity of the parameters to the presence of either additive or innovational outliers is analyzed, and influence statistics based on the Mahalanobis distance are presented. The statistic linked to additive outliers is shown to be very useful for indicating the…

识别时间序列中的简化结构AI 解读

This article studies how to identify hidden factors in multivariate time series process. This problem is important because, when the series are driven by a set of common factors, (a) a large number of parameters may be needed to obtain an adequate representation of the system and (b) the estimated parameters will be highly correlated. Therefore, a complex and badly defined relationship can appear when, in fact, a simpler and parsimonious model in terms of a few common…

构建回归模型的稳健方法：住房部门应用AI 解读

This article studies robustification strategies for the linear model in the presence of outliers.The advantages of an internal analysis of the robustness of least squares for a given sample are pointed out.The application of this methodology is illustrated by building an explicit model of the determinants of rental housing values in the Madrid Metropolitan Area.

稳健回归建模方法：住房部门的应用AI 解读

This article studies robustification strategies for the linear model in the presence of outliers. The advantages of an internal analysis of the robustness of least squares for a given sample are pointed out. The application of this methodology is illustrated by building an explicit model of the determinants of rental housing values in the Madrid Metropolitan Area.