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多元已实现波动率的广义条件自回归 Wishart 模型AI 解读

It is well known that in finance variances and covariances of asset returns move together over time. Recently, much interest has been aroused by an approach involving the use of the realized covariance (RCOV) matrix constructed from high-frequency returns as the ex-post realization of the covariance matrix of low-frequency returns. For the analysis of dynamics of RCOV matrices, we propose the generalized conditional autoregressive Wishart (GCAW) model. Both the noncentrality…

线性时间序列模型相对于门限扩展的检验AI 解读

This paper derives the asymptotic null distribution of a quasilikelihood ratio test statistic for an autoregressive moving average model against its threshold extension. The null hypothesis is that of no threshold, and the error term could be dependent. The asymptotic distribution is rather complicated, and all existing methods for approximating a distribution in the related literature fail to work. Hence, a novel bootstrap approximation based on stochastic permutation is…

条件异方差分数单整 ARMA 模型的最小绝对偏差估计AI 解读

We consider a unified least absolute deviation estimator for stationary and nonstationary fractionally integrated autoregressive moving average models with conditional heteroscedasticity. Its asymptotic normality is established when the second moments of errors and innovations are finite. Several other alternative estimators are also discussed and are shown to be less efficient and less robust than the proposed approach. A diagnostic tool, consisting of two portmanteau…

降维空间的自适应估计AI 解读

Summary Searching for an effective dimension reduction space is an important problem in regression, especially for high dimensional data. We propose an adaptive approach based on semiparametric models, which we call the (conditional) minimum average variance estimation (MAVE) method, within quite a general setting. The MAVE method has the following advantages. Most existing methods must undersmooth the nonparametric link function estimator to achieve a faster rate of…

条件异方差下部分非平稳多元自回归模型的估计AI 解读

This paper investigates a partially nonstationary multivariate autoregressive model, which allows its innovations to be generated by a multivariate ARCH, autoregressive conditional heteroscedastic, process. Three estimators, including the least squares estimator, a full‐rank maximum likelihood estimator and a reduced‐rank maximum likelihood estimator, are considered and their asymptotic distributions are derived. When the multivariate ARCH process reduces to the innovation…

时间序列瞬时变换的估计AI 解读

Summary We modify Ramsay's algorithm for estimating monotonic transformations in regression and extend it to autoregression, where strict monotonicity is an essential requirement. Compared with other methods, our method can capture some characteristics that are pertinent to the time series and is much easier to implement. An order selection method is introduced and developed. Some real data sets are analysed.

一种混合自回归模型AI 解读

Summary We generalize the Gaussian mixture transition distribution (GMTD) model introduced by Le and co-workers to the mixture autoregressive (MAR) model for the modelling of non-linear time series. The models consist of a mixture of K stationary or non-stationary AR components. The advantages of the MAR model over the GMTD model include a more full range of shape changing predictive distributions and the ability to handle cycles and conditional heteroscedasticity in the…

单指标系数回归模型AI 解读

In this article we investigate a class of single-index coefficient regression models under dependence. This includes many existing models, such as the smooth transition threshold autoregressive (STAR) model of Chan and Tong, the functional-coefficient autoregressive (FAR) model of Chen and Tsay, and the single-index model of Ichimura. Compared to the varying-coefficient model of Hastie and Tibshirani, our model can avoid the curse of dimensionality in multivariate…

具有马尔可夫状态切换的随机波动模型AI 解读

This article presents a new way of modeling time-varying volatility. We generalize the usual stochastic volatility models to encompass regime-switching properties. The unobserved state variables are governed by a first-order Markov process. Bayesian estimators are constructed by Gibbs sampling. High-, medium- and low-volatility states are identified for the Standard and Poor's 500 weekly return data. Persistence in volatility is explained by the persistence in the low- and…

含 GARCH 误差的不稳定 ARMA 时间序列最大似然估计量的极限分布AI 解读

This paper investigates the maximum likelihood estimator (MLE) for unstable autoregressive moving-average (ARMA) time series with the noise sequence satisfying a general autoregressive heteroscedastic (GARCH) process. Under some mild conditions, it is shown that the MLE satisfying the likelihood equation exists and is consistent. The limiting distribution of the MLE is derived in a unified manner for all types of characteristic roots on or outside the unit circle and is…

带条件异方差的分数积分 ARMA 时间序列模型AI 解读

This article considers fractionally integrated autoregressive moving-average time series models with conditional heteroscedasticity, which combines the popular generalized autoregressive conditional heteroscedastic (GARCH) and the fractional (ARMA) models. The fractional differencing parameter d can be greater than 1/2, thus incorporating the important unit root case. Some sufficient conditions for stationarity, ergodicity, and existence of higher-order moments are derived.…

双阈值自回归条件异方差时间序列模型AI 解读

Tong's threshold models have been found useful in modelling nonlinearities in the conditional mean of a time series. The threshold model is extended to the so-called double-threshold ARCH(DTARCH) model, which can handle the situation where both the conditional mean and the conditional variance specifications are piecewise linear given previous information. Potential applications of such models include financial data with different (asymmetric) behaviour in a visiting versus…

非线性时间序列建模中残差自相关的渐近标准误AI 解读

The asymptotic distribution of residual autocorrelations for some very general nonlinear time series models is derived. This includes the important class of threshold models. Consequently more accurate standard errors for residual autocorrelations can be obtained facilitating model diagnostic checkings in many situations. A small simulation result applying the methodology to threshold models is reported.

非线性时间序列建模中残差自相关的渐近标准误AI 解读

The asymptotic distribution of residual autocorrelations for some very general nonlinear time series models is derived. This includes the important class of threshold models. Consequently more accurate standard errors for residual autocorrelations can be obtained facilitating model diagnostic checkings in many situations. A small simulation result applying the methodology to threshold models is reported.

季节差分的若干拉格朗日乘子检验AI 解读

Determining whether a time series has a unit root is an important problem in many time series analyses. For seasonal time series the problem is more complicated as one has to decide whether both regular and seasonal differencing or just one of them would suffice to transform a series into stationarity. This important problem is addressed via the Lagrange multiplier test approach. The large sample representations of the test statistics in terms of integrals of Wiener…

若干时间序列 Markov 回归模型的适切性检验AI 解读

The asymptotic distribution of residual autocorrelations and a score statistic are derived for checking model adequacy for some Markov regression models for time series. These models are natural extensions of the popular generalized linear models to time series data. The score statistic appears to be somewhat easier to use in practice. Some empirical results are also obtained for the score statistic in some simple cases. A real example is given.

季节差分的若干拉格朗日乘子检验AI 解读

Determining whether a time series has a unit root is an important problem in many time series analyses. For seasonal time series the problem is more complicated as one has to decide whether both regular and seasonal differencing or just one of them would suffice to transform a series into stationarity. This important problem is addressed via the Lagrange multiplier test approach. The large sample representations of the test statistics in terms of integrals of Wiener…

若干时间序列 Markov 回归模型的适合性检验AI 解读

The asymptotic distribution of residual autocorrelations and a score statistic are derived for checking model adequacy for some Markov regression models for time series. These models are natural extensions of the popular generalized linear models to time series data. The score statistic appears to be somewhat easier to use in practice. Some empirical results are also obtained for the score statistic in some simple cases. A real example is given.

多元时间序列的稳健建模AI 解读

A robust estimation procedure for multiple time series is proposed based on robustifying the residual autocovariances in the estimating equation. The asymptotic distribution of these estimators is derived. A robustified multivariate portmanteau statistic is also obtained which can be useful in model diagnostic checking. An illustrative example based on the mink-muskrat data is presented.

多元时间序列的稳健建模AI 解读

A robust estimation procedure for multiple time series is proposed based on robustifying the residual autocovariances in the estimating equation. The asymptotic distribution of these estimators is derived. A robustified multivariate portmanteau statistic is also obtained which can be useful in model diagnostic checking. An illustrative example based on the mink-muskrat data is presented.

The problem of testing the adequacy of a time series model in the presence of outliers is considered. The classical portmanteau statistic is generalized to an important class of robust estimators. Some Monte Carlo results suggest that the proposed generalization possesses good robustness properties over the classical statistic. A robustified version of a result of Newbold (1980) is also obtained.

稳健时间序列建模中的拟合优度检验AI 解读

The problem of testing the adequacy of a time series model in the presence of outliers is considered. The classical portmanteau statistic is generalized to an important class of robust estimators. Some Monte Carlo results suggest that the proposed generalization possesses good robustness properties over the classical statistic. A robustified version of a result of Newbold (1980) is also obtained.

误分类下子群均值估计的新方法AI 解读

This paper considers the estimation of the subgroup means of a response variable when the individuals in a random sample are allocated to the subgroups or classes by a fallible device. A double sampling scheme under which we determine also the true classes of the individuals in a subsample is utilized. A simple distributional-free estimator is proposed and the associated large-sample properties are also derived. The proposed estimator is also demonstrated by simulation…

分数差分时间序列建模AI 解读

Aspects of model building using fractionally differenced autoregressive-moving average processes are discussed. An algorithm for approximate maximum likelihood estimation is outlined and the large-sample distribution of the maximum likelihood estimates is derived. The large-sample distribution of the residual autocorrelations is also derived and a modified portmanteau test statistic is obtained for checking model adequacy.