An empirical likelihood goodness-of-fit test for time series

Summary. Standard goodness-of-fit tests for a parametric regression model against a series of nonparametric alternatives are based on residuals arising from a fitted model. When a parametric regression model is compared with a nonparametric model, goodness-of-fit testing can be naturally approached by evaluating the likelihood of the parametric model within a nonparametric framework. We employ the empirical likelihood for an α -mixing process to formulate a test statistic that measures the goodness of fit of a parametric regression model. The technique is based on a comparison with kernel smoothing estimators. The empirical likelihood formulation of the test has two attractive features. One is its automatic consideration of the variation that is associated with the nonparametric fit due to empirical likelihood's ability to Studentize internally. The other is that the asymptotic distribution of the test statistic is free of unknown parameters, avoiding plug-in estimation. We apply the test to a discretized diffusion model which has recently been considered in financial market analysis.     

Modelling map positional error to infer true feature location

The authors consider the issue of map positional error, or the difference between location as represented in a spatial database (i.e., a map) and the corresponding unobservable true location. They propose a fully model‐based approach that incorporates aspects of the map registration process commonly performed by users of geographic informations systems, including rubber‐sheeting. They explain how estimates of positional error can be obtained, hence estimates of true location. They show that with multiple maps of varying accuracy along with ground truthing data, suitable model averaging offers a strategy for using all of the maps to learn about true location.     

Pseudo–Modelling of Outliers for Estimation of Regression And Time Series Models

This paper documents situations where the variance inflation model for outliers has undesirable properties. The model is commonly used to accommodate outliers in a Bayesian analysis of regression and time series models. The alternative approach provided here does not suffer from these undesirable properties but gives inferences similar to those of the variance inflation model when this is appropriate. It can be used with regression, time series, and regression with correlated errors in a unified way, and adheres to the scientific principle that inference should be based on the data after obvious outliers have been discarded. Only one parameter is required for outliers; it is interpretable as the a priori willingness to remove observations from the analysis.     

Exposure Reconstruction for the TCDD-Exposed NIOSH Cohort Using a Concentration- and Age-Dependent Model of Elimination

Recent studies demonstrating a concentration dependence of elimination of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) suggest that previous estimates of exposure for occupationally exposed cohorts may have underestimated actual exposure, resulting in a potential overestimate of the carcinogenic potency of TCDD in humans based on the mortality data for these cohorts. Using a database on U.S. chemical manufacturing workers potentially exposed to TCDD compiled by the National Institute for Occupational Safety and Health (NIOSH), we evaluated the impact of using a concentration- and age-dependent elimination model (CADM) (Aylward et al., 2005) on estimates of serum lipid area under the curve (AUC) for the NIOSH cohort. These data were used previously by Steenland et al. (2001) in combination with a first-order elimination model with an 8.7-year half-life to estimate cumulative serum lipid concentration (equivalent to AUC) for these workers for use in cancer dose-response assessment. Serum lipid TCDD measurements taken in 1988 for a subset of the cohort were combined with the NIOSH job exposure matrix and work histories to estimate dose rates per unit of exposure score. We evaluated the effect of choices in regression model (regression on untransformed vs. ln-transformed data and inclusion of a nonzero regression intercept) as well as the impact of choices of elimination models and parameters on estimated AUCs for the cohort. Central estimates for dose rate parameters derived from the serum-sampled subcohort were applied with the elimination models to time-specific exposure scores for the entire cohort to generate AUC estimates for all cohort members. Use of the CADM resulted in improved model fits to the serum sampling data compared to the first-order models. Dose rates varied by a factor of 50 among different combinations of elimination model, parameter sets, and regression models. Use of a CADM results in increases of up to five-fold in AUC estimates for the more highly exposed members of the cohort compared to estimates obtained using the first-order model with 8.7-year half-life. This degree of variation in the AUC estimates for this cohort would affect substantially the cancer potency estimates derived from the mortality data from this cohort. Such variability and uncertainty in the reconstructed serum lipid AUC estimates for this cohort, depending on elimination model, parameter set, and regression model, have not been described previously and are critical components in evaluating the dose-response data from the occupationally exposed populations.     

甘肃省县域经济发展水平空间差异分析

甘肃省包括86个县区,受地理位置、气候条件、资源状况、经济基础各种因素的影响,各县区之间的经济发展水平差异显著但又有很多相似之处。为了定量揭示甘肃省县域经济发展水平在空间上的分布现状、差异以及相似性,笔者拟运用多元统计方法对甘肃省县域经济发展水平的空间差异分析     

Semiparametric linear transformation models for current status data

Current status data arise when the death of every subject in a study cannot be determined precisely, but is known only to have occurred before or after a random monitoring time. The authors discuss the analysis of such data under semiparametric linear transformation models for which they propose a general inference procedure based on estimating functions. They determine the properties of the estimates they propose for the regression parameters of the model and illustrate their technique using tumorigenicity data.     

基于Simpson公式改进的FAGM(1,1)模型及其应用研究

本文分析了传统FAGM（1,1）模型建模过程中存在的误差,提出了一种基于Simpson公式改进的FAGM（1,1）模型。首先,基于分数阶累加生成算子和分数阶累减生成算子建立分数阶FAGM（1,1）模型。其次,利用Simpson积分公式对FAGM（1,1）模型的背景值进行改进,建立SFAGM（1,1）模型。进一步,应用遗传算法确定SFAGM（1,1）模型的最优阶数以提高模型的预测精度。最后,以中国人均GDP为例,对比分析GM（1,1）模型、Simpson改进的GM（1,1）模型（SGM（1,1））、FAGM（1,1）模型、SFAGM（1,1）模型的模拟结果,并对"十三五"时期的人均GDP进行预测,其结果表明SFAGM（1,1）模型比GM（1,1）、SGM（1,1）、FAGM（1,1）在人均GDP的预测方面有更高的精度,"十三五"期间人均GDP年平均增长率为10.64%,到2020年达到83146.97元,是2010年人均GDP的2.69倍,以2010年的人均GDP为基准,到2020年将能够实现翻一番的目标。     

环境分权、地方政府竞争对绿色技术创新影响的“本地—邻地”效应

随着绿色发展成为五大发展理念之一,各地政府推进绿色技术创新成为降低环境污染和提高增长质量的主要途径。为了考察地方政府环境分权和竞争对绿色技术创新影响的“本地—邻地”效应,本文利用非期望产出EBM模型测度绿色技术创新程度,并将绿色技术创新指数引入动态空间杜宾模型。最后,结合2003-2017年中国省级面板数据进行实证分析。研究发现：(1)绿色技术创新具有显著的路径依赖、空间集聚以及邻近地区的策略竞争效应;(2)环境分权对本地和邻地绿色技术创新的影响呈“U型”变动特征,且现阶段并未跨越分权驱动创新的临界点;(3)环境分权与地方政府竞争的交互效应整体为负,环境行政分权、环境监察分权和环境监测分权等不同类型环境分权采取的策略并不相同;(4)区域环境分权产生的绿色技术创新激励效应存在异质性,中西部地区环境分权对绿色技术创新的激励效果更强。     

数字经济与城市群协同发展：基于夜间灯光数据的研究

准确认识数字经济与城市群协同发展的关系,有利于拓展数字经济影响的评估视角,也为发挥数字经济和区域一体化的融合倍增效应提供有益思路。研究发现,数字经济时代城市群辐射带动区域协同发展的步伐正在加快,数据要素跨域流通打破了城市群内部和城市群之间的市场分割,助推城市群边界的有效扩大。数字经济促进城市群协同发展主要通过两个作用机制,一是推动创新主体跨城市跨区域进行合作创新,促进城市协同创新网络发育壮大;二是提升城市空间集中度,增强邻近空间范围内的城市要素集聚能力。数字经济的网络性、跳跃性及外部性特征放大了中心城市对外围城市的辐射带动作用和空间溢出效应,因此构建与数字经济相匹配的区域一体化协调机制和要素统筹配置机制正当其时。     

收费公路运营效率测度及时空分异研究

收费公路行业内部债务治理与外部减税降费均要求收费公路运营降本增效。为了探析收费公路运营效率水平及其空间分布格局,使用SBM模型测度了2014—2020年29个省份收费公路运营效率,进而采用泰尔指数和Moran's I指数分析了其时空分异特征。研究认为:收费公路运营存在着效率较低且在时间维度上没有明显增长的趋势,在空间上存在发展不平衡问题,收费公路运营效率的区域差异主要来源于区域内部差异,区域内差异的贡献率平均达到81.578%; 收费公路运营效率具有空间正相关性,呈现出显著的“高-高”集聚和“低-低”集聚的空间特征,运营效率较高的省份对相邻省份具有正向溢出效应。研究表明,各省针对相邻省份或最近省份的运营效率现状,结合自身的比较优势探索产业升级或者物流发展模式,充分发挥相邻省份的经济发展和产业集聚对本省收费公路运营的辐射带动作用,推动建立起收费公路跨区域合作运营机制。