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半参数模型失去可识别性时的渐近最优检验AI 解读

We consider tests of hypotheses when the parameters are not identifiable under the null in semiparametric models, where regularity conditions for profile likelihood theory fail. Exponential average tests based on integrated profile likelihood are constructed and shown to be asymptotically optimal under a weighted average power criterion with respect to a prior on the nonidentifiable aspect of the model. These results extend existing results for parametric models, which…

函数系数协整模型AI 解读

This paper studies estimation and inference of functional coefficient cointegration models. The proposed model offers a more flexible structure of cointegration where the value of cointegrating coefficients may be affected by informative covariates and thus may vary over time. The model may be viewed as a stochastic cointegration model and includes the conventional cointegration model as a special case. The proposed new model provides a useful complement to the conventional…

分位数、期望分位数与样条AI 解读

A time-varying quantile can be fitted by formulating a time series model for the corresponding population quantile and iteratively applying a suitably modified state space signal extraction algorithm. It is shown that such quantiles satisfy the defining property of fixed quantiles in having the appropriate number of observations above and below. Like quantiles, time-varying expectiles can be estimated by a state space signal extraction algorithm and they satisfy properties…

异质依赖数据下核估计量的一致收敛速度AI 解读

The main uniform convergence results of Hansen (2008, Econometric Theory 24, 726–748) are generalized in two directions: Data are allowed to (a) be heterogeneously dependent and (b) depend on a (possibly unbounded) parameter. These results are useful in semiparametric estimation problems involving time-inhomogeneous models and/or sampling of continuous-time processes. The usefulness of these results is demonstrated by two applications: kernel regression estimation of a…

Student t 混合自回归模型及其在厚尾金融数据中的应用AI 解读

We introduce the class of Student t-mixture autoregressive models, which is promising for financial time series modelling. The model is able to capture serial correlations, time-varying means and volatilities, and the shape of the conditional distributions can be time varied from short-tailed to long-tailed, or from unimodal to multimodal. The use of t-distributed errors in each component of the model allows conditional leptokurtic distributions that account for the commonly…

加性风险回归模型的经验贝叶斯估计AI 解读

We develop a novel empirical Bayesian framework for the semiparametric additive hazards regression model. The integrated likelihood, obtained by integration over the unknown prior of the nonparametric baseline cumulative hazard, can be maximized using standard statistical software. Unlike the corresponding full Bayes method, our empirical Bayes estimators of regression parameters, survival curves and their corresponding standard errors have easily computed closed-form…

自适应变系数时空模型AI 解读

Summary We propose an adaptive varying-coefficient spatiotemporal model for data that are observed irregularly over space and regularly in time. The model is capable of catching possible non-linearity (both in space and in time) and non-stationarity (in space) by allowing the auto-regressive coefficients to vary with both spatial location and an unknown index variable. We suggest a two-step procedure to estimate both the coefficient functions and the index variable, which is…

广义多参数似然模型的统计估计AI 解读

Multiparameter likelihood models (MLMs) with multiple covariates have a wide range of applications; however, they encounter the “curse of dimensionality” problem when the dimension of the covariates is large. We develop a generalized multiparameter likelihood model that copes with multiple covariates and adapts to dynamic structural changes well. It includes some popular models, such as the partially linear and varying-coefficient models, as special cases. We present a…

自适应采样下的变点估计AI 解读

We consider the problem of locating a jump discontinuity (change-point) in a smooth parametric regression model with a bounded covariate. It is assumed that one can sample the covariate at different values and measure the corresponding responses. Budget constraints dictate that a total of n such measurements can be obtained. A multistage adaptive procedure is proposed, where at each stage an estimate of the change point is obtained and new points are sampled from its…

基于经验谱测度的局部平稳时间序列局部推断AI 解读

The time varying empirical spectral measure plays a major role in the treatment of inference problems for locally stationary processes. The properties of the empirical spectral measure and related statistics are studied -- both when its index function is fixed or when dependent on the sample size. In particular we prove a general central limit theorem. Several applications and examples are given including semiparametric Whittle estimation, local least squares estimation and…

新西兰经济周期AI 解读

Our paper is in the spirit of Rex Bergstrom's interests and research in cyclical growth models and his meticulous attention to underlying data series. We develop a new quarterly real GDP series for post–World War II New Zealand, derive a new “benchmark” set of classical business cycle turning points, and establish nonparametric classical cycle characteristics. Markov-switching models, estimated by Gibbs-sampling methods, are used to derive mean growth rate and volatility…

趋势断裂假说的简便、稳健且有力检验AI 解读

In this paper we develop a simple procedure that delivers tests for the presence of a broken trend in a univariate time series that do not require knowledge of the form of serial correlation in the data and are robust as to whether the shocks are generated by an I (0) or an I (1) process. Two trend break models are considered: the first holds the level fixed while allowing the trend to break, while the latter allows for a simultaneous break in level and trend. For the known…

时变连续时间系统的离散采样AI 解读

We consider a multivariate continuous-time process, generated by a system of linear stochastic differential equations, driven by white noise, and involving coefficients that possibly vary over time. The process is observable only at discrete, but not necessarily equally-spaced, time points (though equal spacing significantly simplifies matters). Such settings represent partial extensions of ones studied extensively by A.R. Bergstrom. A model for the observed time series is…

含单整或平稳噪声的趋势变化检验AI 解读

We consider testing for structural changes in the trend function of a time series without any prior knowledge of whether the noise component is stationary or integrated. Following Perron and Yabu (2009), we consider a quasi-feasible generalized least squares procedure that uses a super-efficient estimate of the sum of the autoregressive parameters αwhen α=1. This allows tests of basically the same size with stationary or integrated noise regardless of whether the break is…

时变条件异方差模型的自适应逐点估计AI 解读

This paper offers a new method for estimation and forecasting of the volatility of financial time series when the stationarity assumption is violated. Our general, local parametric approach particularly applies to general varying-coefficient parametric models, such as GARCH, whose coefficients may arbitrarily vary with time. Global parametric, smooth transition and change-point models are special cases. The method is based on an adaptive pointwise selection of the largest…

寻找金融时间序列中的偏度AI 解读

In this paper, we study marginal and conditional skewness in financial returns for nine time series of major international stock indices. For this purpose, we develop a new variant of the GARCH model with dynamic skewness and kurtosis. Our empirical results indicate that there is no evidence of marginal asymmetry in the nine time series under consideration. We do however find significant time-varying conditional skewness. The economic significance of conditional skewness is…

协变量调整的广义线性模型AI 解读

We propose covariate adjustment methodology for a situation where one wishes to study the dependence of a generalized response on predictors while both predictors and response are distorted by an observable covariate. The distorting covariate is thought of as a size measurement that affects predictors in a multiplicative fashion. The generalized response is modelled by means of a random threshold, where the subject-specific thresholds are affected by a multiplicative factor…

This paper offers a new approach to modeling and forecasting of nonstationary time series with applications to volatility modeling for financial data. The approach is based on the assumption of local homogeneity: for every time point, there exists a historical interval of homogeneity, in which the volatility parameter can be well approximated by a constant. The proposed procedure recovers this interval from the data using the local change point (LCP) analysis. Afterward, the…

具有不相关但相依误差的Copula多元GARCH模型AI 解读

Multivariate GARCH (MGARCH) models are usually estimated under multivariate normality. In this paper, for non-elliptically distributed financial returns, we propose copula-based multivariate GARCH (C-MGARCH) model with uncorrelated dependent errors, which are generated through a linear combination of dependent random variables. The dependence structure is controlled by a copula function. Our new C-MGARCH model nests a conventional MGARCH model as a special case. The aim of…

变系数模型的收缩估计AI 解读

The varying coefficient model is a useful extension of the linear regression model. Nevertheless, how to conduct variable selection for the varying coefficient model in a computationally efficient manner is poorly understood. To solve the problem, we propose here a novel method, which combines the ideas of the local polynomial smoothing and the Least Absolute Shrinkage and Selection Operator (LASSO). The new method can do nonparametric estimation and variable selection…

干预与因果关系：利用动态贝叶斯网络预测交通流AI 解读

Real-time traffic flow data across entire networks can be used in a traffic management system to monitor current traffic flows so that traffic can be directed and managed efficiently. Reliable short-term forecasting models of traffic flows are crucial for the success of any traffic management system. The model proposed in this article for forecasting traffic flows is a multivariate Bayesian dynamic model called the multiregression dynamic model (MDM). This model is an…

利用参数与半参数贝叶斯折点回归分析 SEER 癌症发生率AI 解读

Cancer is the second leading cause of death in the United States. Cancer incidence and mortality rates measure the progress against cancer; these rates are obtained from the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute (NCI). Lung cancer has the highest mortality rate among all cancers, whereas prostate cancer has the highest number of new cases among males. In this article, we analyze the incidence rates of these two cancers,…

将风险率建模为函数型数据：队列生命表分析与死亡率预测AI 解读

As world populations age, the analysis of demographic mortality data and demographic predictions of future mortality have met with increasing interest. The study of mortality patterns and the forecasting of future mortality with its associated impacts on social welfare, health care, and societal planning has become a more pressing issue. An ideal set of data to study patterns of change in long-term mortality is the well-known historical Swedish cohort mortality data, because…

年轻女性性态度与行为的联合模型：非线性时间效应、时变协变量与退出AI 解读

Understanding human sexual behaviors is essential for the effective prevention of sexually transmitted infections. Analysis of longitudinally measured sexual behavioral data, however, is often complicated by zero-inflation of event counts, nonlinear time trend, time-varying covariates, and informative dropouts. Ignoring these complicating factors could undermine the validity of the study findings. In this paper, we put forth a unified joint modeling structure that…