A Note on the Role of the Propensity Score for Estimating Average Treatment Effects

Hahn [Hahn, J. (1998). On the role of the propensity score in efficient semiparametric estimation of average treatment effects. Econometrica 66:315-331] derived the semiparametric efficiency bounds for estimating the average treatment effect (ATE) and the average treatment effect on the treated (ATET). The variance of ATET depends on whether the propensity score is known or unknown. Hahn attributes this to “dimension reduction.” In this paper, an alternative explanation is given: Knowledge of the propensity score improves upon the estimation of the distribution of the confounding variables.     

On Rao score and PearsonX 2 statistics in generalized linear models

The identity of the Rao score and PearsonX ² statistics is well known in the areas where the latter was first introduced: goodness-of-fit in contingency tables and binary responses. We show in this paper that the same identity holds when the two statistics are used for testing goodness-of-fit of Generalized Linear Models. We also highlight the connections that exist between the two statistics when they are used for the comparison of nested models. Finally, we discuss some merits of these unifying results. Work financially supported by cofin. MIUR grants 2000 and 2002.     

Which Wald statistic? Choosing a parameterization of the Wald statistic to maximize power in k-sample generalized estimating equations

The Wald statistic is known to vary under reparameterization. This raises the question: which parameterization should be chosen, in order to optimize power of the Wald statistic? We specifically consider k-sample tests of generalized linear models (GLMs) and generalized estimating equations (GEEs) in which the alternative hypothesis contains only two parameters. An example is presented in which such an alternative hypothesis is of interest. Amongst a general class of parameterizations, we find the parameterization that maximizes power via analysis of the non-centrality parameter, and show how the effect on power of reparameterization depends on sampling design and the differences in variance across samples. There is no single parameterization with optimal power across all alternatives. The Wald statistic commonly used under the canonical parameterization is optimal in some instances but it performs very poorly in others. We demonstrate results by example and by simulation, and describe their implications for likelihood ratio statistics and score statistics. We conclude that due to poor power properties, the routine use of score statistics and Wald statistics under the canonical parameterization for GEEs is a questionable practice.     

Notes on Interval Estimation of Odds Ratio in Matched Pairs under Stratified Sampling

We develop four asymptotic interval estimators and one exact interval estimator for the odds ratio (OR) under stratified random sampling with matched pairs. We apply Monte Carlo simulation to evaluate the performance of these five interval estimators. We note that the conditional score test-based interval estimator with a monotonic transformation and the interval estimator based on the Mantel–Haenszel (MH) type point estimator with the logarithmic transformation are generally preferable to the others considered here. We also note that the conditional exact confidence interval can be of use when the total number of matched pairs with discordant responses is small.     

Mean shift and influence measures in linear measurement error models with stochastic linear restrictions

We present influence diagnostics for linear measurement error models with stochastic linear restrictions using the corrected likelihood of Nakamura in 1990. The case deletion and mean shift outlier models are developed to identify outlying and influential observations. We derive a corrected score test statistic for outlier detection based on mean shift outlier models. The analogs of Cook's distance and likelihood distance are proposed to determine influential observations based on case deletion models. A parametric bootstrap procedure is used to obtain empirical distributions of the test statistics and a simulation study has been used to evaluate the performance of the proposed estimators based on the mean squares error criterion and the score test statistic. Finally, a numerical example is given to illustrate the theoretical results.     

要素匹配与中国投资效率空间差异性——基于双层嵌套型函数的分解研究

本文基于四要素双层嵌套的生产函数,利用1990-2016年全国层面及省际层面数据测算投资效率,并分析其空间结构特征及演变轨迹,检验投资效率匹配效应影响因素。研究发现：①1990-2016年我国整体投资效率呈下降趋势,资本-技能劳动匹配效应与整体投资效率走势趋同,快速累积的资本存量和不断升级的资本质量与短缺的技能劳动之间矛盾突出。②东中西部地区投资效率增长非一致性明显。东部地区资本相对投资效率明显高于中西部,但下降幅度大于中西部,中西部与东部地区之间差距缩小,其中匹配效应是区域间投资效率非平衡的主要成因。③若产业结构升级的技能劳动需求不能满足,将引发要素错配,不利于有效投资。房价与工资提升分别通过影响技能劳动供给和需求抑制要素匹配效率;教育和公路设施会对地区要素匹配产生积极影响,改善地区间投资效率非均衡性;地区投资效率存在显著正向空间相关性,毗邻地区的要素流动和经济发展相近地区的技术溢出均对投资效率产生积极的影响。     

城市化与产业发展的匹配机制研究--基于比较优势的理论视角

城市化与产业发展是相互促进、互为因果螺旋上升的关系,城市是产业发展的载体,城市化的推进引致巨大的消费需求和投资需求,而产业是城市化的动力和支撑。城市化进程可以看成是资源、基础设施、制度和环境等一系列要素禀赋的动态变化,并在此基础上形成比较优势,吸引特定产业的投资和促进产业演进,在一个静态时点上,城市化与产业发展处于一般均衡状态,二者之间存在一种相互匹配关系。因此,在我国新型城镇化建设中要树立大、中、小不同规模城市协同发展的理念,在分析城市要素动态变化基础上,实施科学有效的产业政策。     

Use of log−log survival function in modeling time-covariate interactions in Cox regression

Plotting of log−log survival functions against time for different categories or combinations of categories of covariates is perhaps the easiest and most commonly used graphical tool for checking proportional hazards (PH) assumption. One problem in the utilization of the technique is that the covariates need to be categorical or made categorical through appropriate grouping of the continuous covariates. Subjectivity in the decision making on the basis of eye-judgment of the plots and frequent inconclusiveness arising in situations where the number of categories and/or covariates gets larger are among other limitations of this technique. This paper proposes a non-graphical (numerical) test of the PH assumption that makes use of log−log survival function. The test enables checking proportionality for categorical as well as continuous covariates and overcomes the other limitations of the graphical method. Observed power and size of the test are compared to some other tests of its kind through simulation experiments. Simulations demonstrate that the proposed test is more powerful than some of the most sensitive tests in the literature in a wide range of survival situations. An example of the test is given using the widely used gastric cancer data.     

信息化条件下流通商主导的供应链动态联盟

供应链动态联盟是信息技术发达、市场需求主导的条件下实现敏捷生产和流通的重要组织形式。是一种基于非正式契约的不完全市场交易方式。在消费者占据主导权、生产企业逐渐归核化（refkusing）的条件下,流通商在联盟中居于重要地位。文章论述了信息网络技术高度发达的条件下流通商主导的供应链动态联盟的运作机制,以期能够为建立和谐高效的产销关系提供理论支持。     

A propensity score matching analysis of the relationship between victim sex and capital juror decision-making in North Carolina

A small body of prior research has examined the impact of victim sex on jury death penalty decision-making and the majority of this research has demonstrated some evidence of a “female victim effect” such that cases involving a female victim are more likely to receive the death penalty than similarly situated cases with a male victim. However, within this line of research studies have suggested that victim sex may work in conjunction with other case characteristics. In order to further explore this phenomenon, the current study examines a near-population of death penalty cases from North Carolina (n = 1069) from 1977–2009 using propensity score matching. Results demonstrate that once cases are matched on more than 50 legal and extralegal case characteristics, there is no statistically significant or substantive link between victim sex and death penalty decision-making. Findings suggest that it is concrete differences in the legal and extralegal factors observed in cases with female victims compared to male victims that shape jury death sentence decisions rather than a direct effect of victim sex (before matching: OR = 1.53; 95% CI = 1.20–1.95; p < .001/after matching: OR = 0.90; 95% CI = 0.66–1.24; p = .52). Study limitations and implications are also discussed.