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多元时间序列模型识别的统一方法AI 解读

This article proposes a Bayesian procedure for simultaneous identification of the Kronecker indices and model parameters of a multivariate linear system. The model parameters include the starting values and innovations of the system so that the series considered may be co-integrated or non-invertible. The procedure uses some recent developments in stochastic search variable selection in linear regression analysis and Markov chain Monte Carlo methods in statistical computing.…

周期性状态转换模型的贝叶斯推断AI 解读

We present a general class of nonlinear time-series Markov regime-switching models for seasonal data which may exhibit periodic features in the hidden Markov process as well as in the laws of motion in each of the regimes. This class of models allows for non-trivial dependencies between seasonal, cyclical and long-term patterns in the data. To overcome the computational burden we adopt a Bayesian approach to estimation and inference. This paper contains two empirical…

非线性自回归的可加性检验AI 解读

Additivity is commonly used in the statistical literature to simplify data analysis, especially in analysis of variance and in multivariate smoothing. In this paper, we propose three procedures for testing additivity in nonlinear time series analysis. The first procedure combines some smoothing techniques with analysis of variance, the second is a Lagrange multiplier test using nonparametric estimation, and the third is a permutation test which uses smoothing techniques to…

非线性自回归的可加性检验AI 解读

Additivity is commonly used in the statistical literature to simplify data analysis, especially in analysis of variance and in multivariate smoothing. In this paper, we propose three procedures for testing additivity in nonlinear time series analysis. The first procedure combines some smoothing techniques with analysis of variance, the second is a Lagrange multiplier test using nonparametric estimation, and the third is a permutation test which uses smoothing techniques to…

趋势平稳与差分平稳的贝叶斯推断AI 解读

This paper proposes a general Bayesian framework for distinguishing between trend- and difference-stationarity. Usually, in model selection, we assume that all of the data were generated by one of the models under consideration. In studying time series, however, we may be concerned that the process is changing over time, so that the preferred model changes over time as well. To handle this possibility, we compute the posterior probabilities of the competing models for each…

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自回归时间序列均值与方差变化的贝叶斯推断及预测AI 解读

This article is concerned with statistical inference and prediction of mean and variance changes in an autoregressive time series. We first extend the analysis of random mean-shift models to random variance-shift models. We then consider a method for predicting when a shift is about to occur. This involves appending to the autoregressive model a probit model for the probability that a shift occurs given a chosen set of explanatory variables. The basic computational tool we…

非线性加性 ARX 模型AI 解读

We consider in this article a class of nonlinear additive autoregressive models with exogenous variables for nonlinear time series analysis and propose two modeling procedures for building such models. The procedures proposed use two backfitting techniques (the ACE and BRUTO algorithms) to identify the nonlinear functions involved and use the methods of best subset regression and variable selection in regression analysis to determine the final model. Simulated and real…

评论：自适应预测AI 解读

I am not surprised by how far behind our profession is in calculating interval forecasts. After all, interval forecast is a concept not well formulated nor well understood by statisticians. On the other hand, I am amazed by Chatfield's expectation and his recommendation. Will he be satisfied when Formulas (4.1) and (4.2) are routinely used in practice? Will he be satisfied when the forecast intervals are sufficiently wide to cover all the data points in out-of-sample…

不可逆时间序列模型的检验及应用AI 解读

This paper is concerned with testing for noninvertible time-series models. For a stationary but noninvertible autoregressive moving average model, the author constructs a derived process that is nonstationary but invertible with a nonstationary factor identical to the noninvertible factor of the original time series. The author then proposes a test procedure for testing noninvertibility using various unit-root test statistics available i n the literature. The limiting…

不可逆模型的检验及应用AI 解读

This article is concerned with testing for noninvertibie time series models. For a stationary but noninvertibie autoregressive moving average model, I construct a derived process that is non-stationary but invertible with a nonstationary factor identical to the noninvertibie factor of the original time series. I then propose a test procedure for testing noninvertibility using various unit-root test statistics available in the literature. The limiting distributions of the…

函数系数自回归模型AI 解读

In this article we propose a new class of models for nonlinear time series analysis, investigate properties of the proposed model, and suggest a modeling procedure for building such a model. The proposed modeling procedure makes use of ideas from both parametric and nonparametric statistics. A consistency result is given to support the procedure. For illustration we apply the proposed model and procedure to several data sets and show that the resulting models substantially…

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…

向量自回归移动平均模型的简约参数化AI 解读

It is not uncommon to hear the complaint that there are many difficulties in building vector autoregressive moving average (ARMA) models, such as too many parameters in a model. In this article, I show that the complaint can be avoided if a proper method is used at the model-specification stage to obtain the detailed structure of the vector process under study. Two such methods, one from the engineering literature and the other from the statistical literature, are discussed.…

门限自回归过程的检验与建模AI 解读

The threshold autoregressive model is one of the nonlinear time series models available in the literature. It was first proposed by Tong (1978) and discussed in detail by Tong and Lim (1980) and Tong (1983). The major features of this class of models are limit cycles, amplitude dependent frequencies, and jump phenomena. Much of the original motivation of the model is concerned with limit cycles of a cyclical time series, and indeed the model is capable of producing…

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.…

条件异方差时间序列模型AI 解读

Under the traditional linear time series or regression setting, the conditional variance of one-step-ahead prediction is time invariant. Experience in conjunction with data analysis, however, suggests that the variability of a process might well depend on the available information. This reality has motivated extensive research to relax the constant variance assumption imposed by the traditional linear time series model, and several classes of generalized parametric models…

存在异常值时的时间序列模型设定AI 解读

Outliers are commonplace in data analysis. Time series analysis is no exception. Noting that the effect of outliers on model identification statistics could be serious, this article is concerned with the problem of time series model specification in the presence of outliers. An iterative procedure is proposed to identify the outliers, to remove their effects, and to specify a tentative model for the underlying process. The procedure is essentially based on the iterative…

时间序列的非线性检验AI 解读

This paper considers two nonlinearity tests for stationary time series. The idea of Tukey's one degree of freedom for nonadditivity test is generalized to the time series setting. The case of concurrent nonlinearity is discussed in detail. Simulation results show that the proposed tests are more powerful than that of Keenan (1985).

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…

动态回归（分布滞后）模型的识别AI 解读

Dynamic regression models (also known as distributed lag models) are widely used in engineering for quality control and in economics for forecasting. In this article I propose a procedure for specifying such models in practice. The proposed procedure requires no prewhitening and can directly handle the nonstationary series. Furthermore, the procedure cross-validates prior beliefs about causal relationships between variables with empirical findings to ensure the suitability…

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…

非平稳自回归模型的阶数选择AI 解读

In Hannan (1980), some limiting properties of the order selection criteria, AIC, BIC, and $\phi(p, q)$ for modeling stationary time series were derived. In this paper, we generalize these properties to the case in which the underlying process follows a nonstationary autoregressive model. We show that BIC and $\phi(p, 0)$ are weakly consistent. For the AIC, we prove that the asymptotic distribution given by Shibata (1976) for the stationary autoregressive models continues to…