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强自相关时间序列的 HAC 修正AI 解读

Applied work routinely relies on heteroscedasticity and autocorrelation consistent (HAC) standard errors when conducting inference in a time series setting. As is well known, however, these corrections perform poorly in small samples under pronounced autocorrelations. In this article, I first provide a review of popular methods to clarify the reasons for this failure. I then derive inference that remains valid under a specific form of strong dependence. In particular, I…

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Conducting valid inference in a time series setting often requires the use of a heteroscedasticity and autocorrelation (HAC) robust variance estimator. Under mild dependence structures, the theory ...

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Inference in time series settings is complicated by the possibility of strong autocorrelation in the data. In general, some aspect of a time series model is assumed to satisfy stationarity and weak...

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Müller applies the theory of optimal statistical testing to heteroscedasticity and autocorrelation robust (HAR) inference in the presence of strong autocorrelation. As a starting point, Müller uses...

使用组桥估计量选择近似因子模型的正确因子数：大面板情形AI 解读

This article proposes a group bridge estimator to select the correct number of factors in approximate factor models. It contributes to the literature on shrinkage estimation and factor models by extending the conventional bridge estimator from a single equation to a large panel context. The proposed estimator can consistently estimate the factor loadings of relevant factors and shrink the loadings of irrelevant factors to zero with a probability approaching one. Hence, it…

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Müller looks at the problems of interval estimation and hypothesis testing in autocorrelated models from a frequency domain point of view. This leads to good insights as well as proposals for new m...

预测回归模型的非参数检验AI 解读

This article considers testing the significance of a regressor with a near unit root in a predictive regression model. The procedures discussed in this article are nonparametric, so one can test the significance of a regressor without specifying a functional form. The results are used to test the null hypothesis that the entire function takes the value of zero. We show that the standardized test has a normal distribution regardless of whether there is a near unit root in the…

基于惩罚样条与狄利克雷过程混合的贝叶斯非参数工具变量回归AI 解读

We propose a Bayesian nonparametric instrumental variable approach under additive separability that allows us to correct for endogeneity bias in regression models where the covariate effects enter with unknown functional form. Bias correction relies on a simultaneous equations specification with flexible modeling of the joint error distribution implemented via a Dirichlet process mixture prior. Both the structural and instrumental variable equation are specified in terms of…

含跳跃与微观结构噪声的积分波动率估计AI 解读

In this article, we propose a nonparametric procedure to estimate the integrated volatility of an Itô semimartingale in the presence of jumps and microstructure noise. The estimator is based on a combination of the preaveraging method and threshold technique, which serves to remove microstructure noise and jumps, respectively. The estimator is shown to work for both finite and infinite activity jumps. Furthermore, asymptotic properties of the proposed estimator, such as…

时变朴素贝叶斯模型及其在中文文本分类中的应用AI 解读

Document classification is an area of great importance for which many classification methods have been developed. However, most of these methods cannot generate time-dependent classification rules. Thus, they are not the best choices for problems with time-varying structures. To address this problem, we propose a varying naïve Bayes model, which is a natural extension of the naïve Bayes model that allows for time-dependent classification rule. The method of kernel smoothing…

基于（局部）处理组平均处理效应逆概率加权估计的无混杂检验AI 解读

We propose inverse probability weighted estimators for the local average treatment effect (LATE) and the local average treatment effect for the treated (LATT) under instrumental variable assumptions with covariates. We show that these estimators are asymptotically normal and efficient. When the (binary) instrument satisfies one-sided noncompliance, we propose a Durbin–Wu–Hausman-type test of whether treatment assignment is unconfounded conditional on some observables. The…

含平稳与非平稳协变量的 GARCH-X 模型：拟极大似然估计的渐近理论AI 解读

This article investigates the asymptotic properties of the Gaussian quasi-maximum-likelihood estimators (QMLE’s) of the GARCH model augmented by including an additional explanatory variable—the so-called GARCH-X model. The additional covariate is allowed to exhibit any degree of persistence as captured by its long-memory parameter dx; in particular, we allow for both stationary and nonstationary covariates. We show that the QMLE’s of the parameters entering the volatility…

线性约束下缺失值或错误值的多重插补AI 解读

Many statistical agencies, survey organizations, and research centers collect data that suffer from item nonresponse and erroneous or inconsistent values. These data may be required to satisfy linear constraints, for example, bounds on individual variables and inequalities for ratios or sums of variables. Often these constraints are designed to identify faulty values, which then are blanked and imputed. The data also may exhibit complex distributional features, including…

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Financial data, in particular in the context of managing portfolios and quantifying their risk, come in the form of high-dimensional time series. The evolution of the coordinate processes, represen...

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The authors are to be congratulated for a timely and important contribution. The article proposes a novel principal volatility component (PVC) technique based on a generalized kurtosis matrix in a ...

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It is a pleasure to provide some comments on the excellent and topical article by Hu and Tsay (2014).The article extends principal component analysis (PCA) to principal volatility component analysi...

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We would like to thank Beth Andrews, Christian Francq, Jean-Michel Zakoïan, Gabriele Fiorentini, Enrique Sentana, Qiwei Yao, Shiqing Ling, and Ke Zhu for their insightful and stimulating discussion...

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I congratulate the authors for tackling a challenging statistical problem with an important financial application, that is, estimating heavy-tailed GARCH models. The significance of the proposed th...

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The authors are to be congratulated for tackling a challenging statistical problem with important financial applications, that is, modeling multivariate volatility processes via dimension-reduction...

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In this interesting article, the authors developed and studied a non-Gaussian quasi-maximum likelihood (NGQML) method for estimating GARCH(p, q) models. The standard Gaussian QML estimator for gene...

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The Gaussian pseudo-maximum likelihood (PML) estimators advocated by Bollerslev and Wooldridge (1992) among many others remain root-T consistent for the conditional variance parameters of univariat...

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Congratulation to authors because of their interesting estimation approach. In this discussion, we compare the finite performance of the non-Gaussian quasi maximum likelihood estimator (NGQMLE) wit...

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The article by Hu and Tsay (2013) relates to an important area of research on analyzing multivariate time series with dynamic heteroscedasticity and addresses the dimensionality problem of traditio...

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We express our sincere thanks to all the discussants for their constructive discussions and encouragement. In particular, we appreciate their proposals of alternative approaches to finding common v...