Local Linear Estimation for Time-Dependent Coefficients in Cox's Regression Models

This article develops a local partial likelihood technique to estimate the time-dependent coefficients in Cox's regression model. The basic idea is a simple extension of the local linear fitting technique used in the scatterplot smoothing. The coefficients are estimated locally based on the partial likelihood in a window around each time point. Multiple time-dependent covariates are incorporated in the local partial likelihood procedure. The procedure is useful as a diagnostic tool and can be used in uncovering time-dependencies or departure from the proportional hazards model. The programming involved in the local partial likelihood estimation is relatively simple and it can be modified with few efforts from the existing programs for the proportional hazards model. The asymptotic properties of the resulting estimator are established and compared with those from the local constant fitting. A consistent estimator of the asymptotic variance is also proposed. The approach is illustrated by a real data set from the study of gastric cancer patients and a simulation study is also presented.     

A Semiparametric Model for Recapture Experiments

Abstract. A two-step procedure based on the conditional likelihood is proposed to estimate the population size of a closed population using a semiparametric model for recapture studies. An asymptotic variance estimate and numerical results are presented. The method is applied to a bird banding dataset in Hong Kong.     

A Test of the Relationship between Monetary Growth Uncertainty and Economic Growth in China： 1980-2008

我国货币增长不确定性的根源可以划分为货币政策冲击和宏观经济冲击两个层面,通过检验我国货币增长不确定性与经济增长之间的关系,结果显示：货币增长不确定性主要由宏观经济冲击所引致;1998年以前的货币增长不确定性比较剧烈,1998之后的货币增长不确定性明显减弱;由货币政策冲击导致的货币增长不确定性能够有效地促进经济增长,这意味着货币政策调控的有效性;但2003年以来,由宏观经济冲击导致的货币增长不确定性对经济增长起到了抑制作用,这说明以国际金融危机为代表的经济冲击对我国经济稳定增长产生了显著的消极影响,需对此进行积极的国家经济风险预警和防范。     

基于时变Copula的La-VaR测度研究

目前关于流动性调整的市场风险测度研究,主要是静态模型。针对此,文章提出经流动性风险调整的市场风险动态测度的时变Copula方法。该方法使用连接函数构建流动性风险和市场风险的联合分布,能够兼顾这两种风险的非正态特征和它们之间的动态相关结构。基于该方法度量了中国股市经流动性调整的市场风险La-VaR,Kupiec检验表明,基于时变Copula模型预测La-VaR的效果优于基于常相关Copula模型的预测效果,并且时变T-Copula模型优于时变N-Copula模型。     

Bayesian analysis of covariance under inverse Gaussian model

This paper considers the problem of analysis of covariance (ANCOVA) under the assumption of inverse Gaussian distribution for response variable from the Bayesian point of view. We develop a fully Bayesian model for ANCOVA based on the conjugate prior distributions for parameters contained in the model. The Bayes estimator of parameters, ANCOVA model and adjusted effects for both treatments and covariates along with predictive distribution of future observations are developed. We also provide the essentials for comparing adjusted treatments effects and adjusted factor effects. A simulation study and a real world application are also performed to illustrate and evaluate the proposed Bayesian model.     

Gompertz model with time-dependent covariate in the presence of interval-, right- and left-censored data

In this paper, the Gompertz model is extended to incorporate time-dependent covariates in the presence of interval-, right-, left-censored and uncensored data. Then, its performance at different sample sizes, study periods and attendance probabilities are studied. Following that, the model is compared to a fixed covariate model. Finally, two confidence interval estimation methods, Wald and likelihood ratio (LR), are explored and conclusions are drawn based on the results of the coverage probability study. The results indicate that bias, standard error and root mean square error values of the parameter estimates decrease with the increase in study period, attendance probability and sample size. Also, LR was found to work slightly better than the Wald for parameters of the model.     

基于时变Copula的房地产业与银行业尾部动态相关性研究

文献多数从线性相关和静态相关的视角研究房地产业与银行业(金融业)的相关性,本文基于大智慧“板块指数”中的“房地产”(代表房地产业)和“银行类”(代表银行业)数据,采用GARCH、EVT和时变Copula模型研究房地产业和银行业的动态尾部相关性.实证结果表明,时变Copula模型刻画尾部相关性的效果优于静态模型,时变SJC-Copula的建模效果相对最好;样本考察期内,上、下尾相关系数均为正,分别落在区间[0.2287,0.6146]和[0.4666,0.7143]内,且下尾相关系数均大于上尾相关系数,说明市场低迷时,房地产业和银行业易产生共生风险,并且低迷时的相关性强于活跃时的相依性;上、下尾相关系数均具有自相关性、ARCH效应.分析月均下尾相关系数发现,房地产业和银行业的下尾相关性具有“政策效应”:下尾相关系数相对较低的月份往往伴有严厉的房地产调控政策出台或之前的月份出台了调控政策,意味着房地产调控政策将弱化房地产业和银行业的下尾相关性.未来应继续执行或出台更严厉的调控政策,降低银行业与房地产业的共生风险,促进房地产业的健康发展.     

基于时变参数状态空间模型的我国能源效率变化研究

基于时变参数状态空间模型,以人均 GDP 和工业增加值占 GDP 的比重作为衡量工业化阶段的主要指标,以单位能耗 GDP 作为能源效率的衡量指标,实证分析工业化不同阶段对能源效率的影响。研究结果表明：在工业化发展的初期,工业成为经济发展的主导产业,工业对能源的巨大需求导致经济发展对能源的依赖性增强,人均 GDP 和工业增加值占比对能源效率的影响均较为明显;在工业化发展的中期,人均 GDP 对能源效率的影响趋于稳定,工业增加值占比对能源效率的影响逐渐趋于零。产业结构调整对改善能源效率的作用很小,加强区域间和国家间的节能技术合作可有效改善能源利用效率。     

我国工业增加值季节波动非线性研究——基于SEATV-STAR模型

多数宏观经济变量时间序列有季节波动,如果季节波动是非线性的,采用经季节调整过的数据或传统季节模型等线性处理季节波动的方法可能就不再适用。本文基于季节时变平滑转换自回归(SEATV-STAR)模型,运用"特殊到一般"的非线性检验策略对我国工业增加值季度增长率季节波动进行研究。结果表明:(1)工业增加值的季节波动兼有结构时变和非线性改变,工业增加值的周期波动是线性的。(2)技术进步、体制变迁等因素使得工业增加值季节波动发生连续的结构时变,它们是季节波动变化的主要影响因素。(3)工业增加值周期波动对其季节波动有非对称影响;在工业增加值的波峰阶段,其季节波幅会减小,且1、2季度工业增长率有明显提高。