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美国失业率预测AI 解读

This article presents a comparison of forecasting performance for a variety of linear and nonlinear time series models using the U.S. unemployment rate. Our main emphases are on measuring forecasting performance during economic expansions and contractions by exploiting the asymmetric cyclical behavior of unemployment numbers, on building vector models that incorporate initial jobless claims as a leading indicator, and on utilizing additional information provided by the…

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卫星臭氧数据的建模AI 解读

Starting in the early 1970s, the decline in column ozone over much of the earth has received much attention. Satellite ozone data, with the advantage of global coverage, now play an important role in assessing long-term trends in ozone distributions. We consider a class of space-time regression models for the analysis of satellite data on a fixed latitude, which take into account temporal and longitudinal dependence of the observations. The models can be used to test the…

利用平方累积和回顾检测方差变化AI 解读

This article studies the problem of multiple change points in the variance of a sequence of independent observations. We propose a procedure to detect variance changes based on an iterated cumulative sums of squares (ICSS) algorithm. We study the properties of the centered cumulative sum of squares function and give an intuitive basis for the ICSS algorithm. For series of moderate size (i.e., 200 observations and beyond), the ICSS algorithm offers results comparable to those…

多步预测中极大似然估计的稳健性：指数平滑情形AI 解读

We extend the argument initiated by Cox (1961) that the exponential smoothing formula can be made more robust for multi-step forecasts if the smoothing parameter is adjusted as a function of the forecast horizon l. The consistency property of the estimator which minimizes the sum of squares of the sample l-step ahead forecast errors makes the robustness result useful in practice. We also generalize the consistency result to some other parsimonious nonstationary models which…

多步预测中最大似然估计的稳健性：指数平滑情形AI 解读

We extend the argument initiated by Cox (1961) that the exponential smoothing formula can be made more robust for multi-step forecasts if the smoothing parameter is adjusted as a function of the forecast horizon l. The consistency property of the estimator which minimizes the sum of squares of the sample l-step ahead forecast errors makes the robustness result useful in practice. We also generalize the consistency result to some other parsimonious nonstationary models which…

多元非平稳过程的渐近性质及其自回归应用AI 解读

Asymptotic properties of multivariate time series with characteristic roots on the unit circle are considered. For a vector autoregressive moving average (ARMA) process, we derive the limiting distributions of certain statistics which are useful in understanding nonstationary processes. These distributions are derived in a unified manner for all types of characteristic roots and are expressed in terms of stochastic integrals of Brownian motions. For applications, we use the…

随机水平突变时间序列模型、ARIMA 近似与水平突变检测AI 解读

The main purpose of this article is to assess the performance of autoregressive integrated moving average (ARIMA) models when occasional level shifts occur in the time series under study. A random level-shift time series model that allows the level of the process to change occasionally is introduced. Between two consecutive changes, the process behaves like the usual autoregressive moving average (ARMA) process. In practice, a series generated from a random level-shift ARMA…

多元时间序列的模型设定AI 解读

SUMMARY We propose a method to specify an appropriate yet parsimonious vector autoregressive moving average (ARMA) model for a given multivariate time series. By considering contemporaneous linear transformations of the vector process, we introduce the concept of scalar component models within the vector ARMA framework (a) to reveal possibly hidden simplifying structures of the process, (b) to achieve parsimony in parameterization and (c) to identify the exchangeable models.…

典型分析在时间序列模型识别中的应用AI 解读

The second-order moment structure of time series models is used to derive a canonical analysis in time series modelling. Consistency properties of certain canonical correlations and the corresponding eigenvectors are shown. Based on these properties, a canonical correlation approach for tentative order determination in building autoregressive-moving average models is proposed. This approach can handle directly nonstationary as well as stationary processes and it also…

典型分析在时间序列模型识别中的应用AI 解读

The second-order moment structure of time series models is used to derive a canonical analysis in time series modelling. Consistency properties of certain canonical correlations and the corresponding eigenvectors are shown. Based on these properties, a canonical correlation approach for tentative order determination in building autoregressive-moving average models is proposed. This approach can handle directly nonstationary as well as stationary processes and it also…

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.

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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.

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统计分析中的贝叶斯推断AI 解读

Nature of Bayesian Inference Standard Normal Theory Inference Problems Bayesian Assessment of Assumptions: Effect of Non-Normality on Inferences About a Population Mean with Generalizations Bayesian Assessment of Assumptions: Comparison of Variances Random Effect Models Analysis of Cross Classification Designs Inference About Means with Information from More than One Source: One-Way Classification and Block Designs Some Aspects of Multivariate Analysis Estimation of Common…

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方差分析中随机效应模型的贝叶斯分析AI 解读

Bayesian methods are utilized to analyse a one-way random effect model yij = μ+ai+eij in which the errors eij are assumed to follow a first-order autoregressive series eij = ρei(j-1)+εij. For given value of ρ, the model can be reduced to that considered in our earlier paper (1965). It is shown that inferences about the variances σα2 = var(ai), and σ2 = var (εij) can be very sensitive to changes in the value of ρ assumed. In particular, in a set of data generated from a…

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