缺失数据下的逆概率多重加权分位回归估计及其应用

数据缺失问题普遍存在于应用研究中。在随机缺失机制假定下,本文从模型推断角度出发,针对线性缺失分位回归模型,提出一种新的有效估计方法——逆概率多重加权（IPMW）估计。该方法是在逆概率加权（IPW）估计的基础上,结合倾向得分匹配及模型平均思想,经过多次估计,加权确定最终参数估计结果。该方法适用于响应变量是独立同分布或独立非同分布的情形,并适用于绝大多数缺失场景。经过理论推导及模拟研究发现,IPMW估计量在继承IPW估计量的优势上具有更稳健的性质。最后,将该方法应用于含有缺失数据的微观调查数据中,研究了经济较发达的准一线城市中等收入群体消费水平的影响因素,对比两种估计方法的估计结果及置信带,发现逆概率多重加权估计量的标准偏差更小,估计结果更稳健。     

岭回归在资本结构影响因素回归建模中的应用

自多重共线性是资本结构影响因素线性回归模型中迫切需要解决的问题,本文运用岭回归方法,以医药制造类上市公司的统计资料为基础,对影响资本结构的主要因素进行了实证分析,有效地解决了回归模型中的多重共线性问题.     

岭回归在经济增长影响因素分析中的应用

岭回归分析是一种改进的最小二乘法,在实际工作中有很大的应用价值.本文以河北省为例,借助标准SAS统计软件,运用岭回归技术,对改革开放以来河北省经济增长的影响因素作了详细而深入的实证研究,并提出了实现河北经济快速、稳定增长的建议和措施,对保证河北省经济的持续发展有重要的借鉴和参考意义.     

Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation

Inverse probability weighting (IPW) and multiple imputation are two widely adopted approaches dealing with missing data. The former models the selection probability, and the latter models data distribution. Consistent estimation requires correct specification of corresponding models. Although the augmented IPW method provides an extra layer of protection on consistency, it is usually not sufficient in practice as the true data‐generating process is unknown. This paper proposes a method combining the two approaches in the same spirit of calibration in sampling survey literature. Multiple models for both the selection probability and data distribution can be simultaneously accounted for, and the resulting estimator is consistent if any model is correctly specified. The proposed method is within the framework of estimating equations and is general enough to cover regression analysis with missing outcomes and/or missing covariates. Results on both theoretical and numerical investigation are provided.     

Efficient Augmented Inverse Probability Weighted Estimation in Missing Data Problems

When analyzing data with missing data, a commonly used method is the inverse probability weighting (IPW) method, which reweights estimating equations with propensity scores. The popularity of the IPW method is due to its simplicity. However, it is often being criticized for being inefficient because most of the information from the incomplete observations is not used. Alternatively, the regression method is known to be efficient but is nonrobust to the misspecification of the regression function. In this article, we propose a novel way of optimally combining the propensity score function and the regression model. The resulting estimating equation enjoys the properties of robustness against misspecification of either the propensity score or the regression function, as well as being locally semiparametric efficient. We demonstrate analytically situations where our method leads to a more efficient estimator than some of its competitors. In a simulation study, we show the new method compares favorably with its competitors in finite samples. Supplementary materials for this article are available online.     

中国农村居民代际收入流动性趋势及质量研究

采用中国家庭收入项目调查(CHIPS)数据,基于收入转换矩阵,测算了中国农村居民代际收入流动性大小和趋势,并对代际收入流动性质量进行了探讨。研究发现:农村居民代际收入流动性较高,主要是源于中间收入阶层在相邻阶层短距离位置变动,而两端收入阶层则相对封闭,代际收入流动性总体呈倒"U"型趋势特征;农村居民代际收入流动性不利于子代收入的改善,不利于中等收入阶层的形成与巩固,持续贫困率和持续富有率较高,两极分化趋势明显,农村居民代际收入流动性质量偏低。     

基于链式方程的收入变量 缺失值的多重插补

 在经济计量分析中收入变量的缺失值是一个普遍而又较难处理的问题。传统的处理方法往往导致分析结果具有系统偏差。本文提出利用基于链式方程的多重插补方法来处理收入变量的缺失值问题。文章将此方法应用到一个实际数据集,然后通过分析插补后的数据集讨论了此方法的性质,并和其他多重插补方法进行了比较。结果表明：基于链式方程的多重插补能在一定程度上纠正推断结果的系统偏差,并且给出恰当的标准差估计。     

M-Estimators Based on Inverse Probability Weighted Estimating Equations with Response Missing at Random

Asymptotic properties of M-estimators with complete data are investigated extensively. In the presence of missing data, however, the standard inference procedures for complete data cannot be applied directly. In this article, the inverse probability weighted method is applied to missing response problem to define M-estimators. The existence of M-estimators is established under very general regularity conditions. Consistency and asymptotic normality of the M-estimators are proved, respectively. An iterative algorithm is applied to calculating the M-estimators. It is shown that one step iteration suffices and the resulting one-step M-estimate has the same limit distribution as in the fully iterated M-estimators.     

税收对中国收入分配差距的影响研究

研究中国税收与居民收入差距之间的定量关系,研究表明:目前中国税种少,个人所得税结构复杂,1994年以前个人所得税起征点高,2000年以后个人所得税起征点低;中等收入阶层是个人所得税主要来源,企业整体税收较重,大型(垄断)企业税收较轻,小型企业税收负担重;主要税种增值税流失严重,消费税比例高。以上原因致使中国税收调节居民收入差距效应微弱。根据研究结论,提出了政策建议。     

An Empirical Comparison of Multiple Imputation Methods for Categorical Data

Multiple imputation is a common approach for dealing with missing values in statistical databases. The imputer fills in missing values with draws from predictive models estimated from the observed data, resulting in multiple, completed versions of the database. Researchers have developed a variety of default routines to implement multiple imputation; however, there has been limited research comparing the performance of these methods, particularly for categorical data. We use simulation studies to compare repeated sampling properties of three default multiple imputation methods for categorical data, including chained equations using generalized linear models, chained equations using classification and regression trees, and a fully Bayesian joint distribution based on Dirichlet process mixture models. We base the simulations on categorical data from the American Community Survey. In the circumstances of this study, the results suggest that default chained equations approaches based on generalized linear models are dominated by the default regression tree and Bayesian mixture model approaches. They also suggest competing advantages for the regression tree and Bayesian mixture model approaches, making both reasonable default engines for multiple imputation of categorical data. Supplementary material for this article is available online.     

Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT

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 test is based on the fact that under one-sided noncompliance LATT coincides with the average treatment effect for the treated (ATT). We conduct Monte Carlo simulations to demonstrate, among other things, that part of the theoretical efficiency gain afforded by unconfoundedness in estimating ATT survives pretesting. We illustrate the implementation of the test on data from training programs administered under the Job Training Partnership Act in the United States. This article has online supplementary material.     

基于数据缺失率和缺失模式的多重插补误差研究

文章通过多重插补方法对不同缺失率和缺失模式的多变量缺失样本进行插补,研究了多重插补误差与缺失率和缺失模式的依赖关系。结果表明,当缺失率为0~15%时,多重插补误差与缺失率呈线性关系;当缺失率大于15%时,两者呈偏离线性关系。多重插补误差与缺失模式的方差均值比呈正相关性,当方差均值比越大时,误差也越大。