On the inefficiency of propensity score matching

Propensity score matching is now widely used in empirical applications for estimating treatment effects. Propensity score matching (PSM) is preferred to matching on X because of the lower dimension of the estimation problem. In this note, however, it is shown that PSM is inefficient compared to matching on X. Hence, matching on X should be considered as a serious alternative.     

Performance evaluation of propensity score methods for estimating average treatment effects with multi-level treatments*

The propensity score (PS) method is widely used to estimate the average treatment effect (TE) in observational studies. However, it is generally confined to the binary treatment assignment. In an extension to the settings of a multi-level treatment, Imbens proposed a generalized propensity score which is the conditional probability of receiving a particular level of the treatment given pre-treatment variables. The average TE can then be estimated by conditioning solely on the generalized PS under the assumption of weak unconfoundedness. In the present work, we adopted this approach and conducted extensive simulations to evaluate the performance of several methods using the generalized PS, including subclassification, matching, inverse probability of treatment weighting (IPTW), and covariate adjustment. Compared with other methods, IPTW had the preferred overall performance. We then applied these methods to a retrospective cohort study of 228,876 pregnant women. The impact of the exposure to different types of the antidepressant medications (no exposure, selective serotonin reuptake inhibitor (SSRI) only, non-SSRI only, and both) during pregnancy on several important infant outcomes (birth weight, gestation age, preterm labor, and respiratory distress) were assessed.     

Inflation uncertainty and economic growth: evidence from the LAD ARCH model

In this paper, we combined the panel data and least absolute deviation autoregressive conditional heteroscedastic (ARCH) (L ₁-ARCH) model to infer on the relationship between inflation uncertainty and economic growth in five emerging market economies. Two interesting findings emerged from the analysis; first, we confirmed that the inflation uncertainty has a significant and negative effect on economic growth. Second, inflation is also an important variable and it is detrimental to economic prospects in the fast-growing Association of Southeast Asian Nations (ASEAN) economies. All in all, the empirical findings suggest that greater stability in the economy may be desirable in order to stimulate economic growth in the region.     

Survival analysis using inverse probability of treatment weighted methods based on the generalized propensity score

In survival analysis, treatment effects are commonly evaluated based on survival curves and hazard ratios as causal treatment effects. In observational studies, these estimates may be biased due to confounding factors. The inverse probability of treatment weighted (IPTW) method based on the propensity score is one of the approaches utilized to adjust for confounding factors between binary treatment groups. As a generalization of this methodology, we developed an exact formula for an IPTW log‐rank test based on the generalized propensity score for survival data. This makes it possible to compare the group differences of IPTW Kaplan–Meier estimators of survival curves using an IPTW log‐rank test for multi‐valued treatments. As causal treatment effects, the hazard ratio can be estimated using the IPTW approach. If the treatments correspond to ordered levels of a treatment, the proposed method can be easily extended to the analysis of treatment effect patterns with contrast statistics. In this paper, the proposed method is illustrated with data from the Kyushu Lipid Intervention Study (KLIS), which investigated the primary preventive effects of pravastatin on coronary heart disease (CHD). The results of the proposed method suggested that pravastatin treatment reduces the risk of CHD and that compliance to pravastatin treatment is important for the prevention of CHD. Copyright © 2009 John Wiley & Sons, Ltd.     

Controlling for confounding via propensity score methods can result in biased estimation of the conditional AUC: A simulation study

In the medical literature, there has been an increased interest in evaluating association between exposure and outcomes using nonrandomized observational studies. However, because assignments to exposure are not random in observational studies, comparisons of outcomes between exposed and nonexposed subjects must account for the effect of confounders. Propensity score methods have been widely used to control for confounding, when estimating exposure effect. Previous studies have shown that conditioning on the propensity score results in biased estimation of conditional odds ratio and hazard ratio. However, research is lacking on the performance of propensity score methods for covariate adjustment when estimating the area under the ROC curve (AUC). In this paper, AUC is proposed as measure of effect when outcomes are continuous. The AUC is interpreted as the probability that a randomly selected nonexposed subject has a better response than a randomly selected exposed subject. A series of simulations has been conducted to examine the performance of propensity score methods when association between exposure and outcomes is quantified by AUC; this includes determining the optimal choice of variables for the propensity score models. Additionally, the propensity score approach is compared with that of the conventional regression approach to adjust for covariates with the AUC. The choice of the best estimator depends on bias, relative bias, and root mean squared error. Finally, an example looking at the relationship of depression/anxiety and pain intensity in people with sickle cell disease is used to illustrate the estimation of the adjusted AUC using the proposed approaches.     

Comparison of clustering algorithms on generalized propensity score in observational studies: a simulation study

In observational studies, unbalanced observed covariates between treatment groups often cause biased inferences on the estimation of treatment effects. Recently, generalized propensity score (GPS) has been proposed to overcome this problem; however, a practical technique to apply the GPS is lacking. This study demonstrates how clustering algorithms can be used to group similar subjects based on transformed GPS. We compare four popular clustering algorithms: k-means clustering (KMC), model-based clustering, fuzzy c-means clustering and partitioning around medoids based on the following three criteria: average dissimilarity between subjects within clusters, average Dunn index and average silhouette width under four various covariate scenarios. Simulation studies show that the KMC algorithm has overall better performance compared with the other three clustering algorithms. Therefore, we recommend using the KMC algorithm to group similar subjects based on the transformed GPS.     

Estimation of average treatment effects based on parametric propensity score model

In this paper, the estimation of average treatment effects is examined given that the propensity score is of a parametric form with some unknown parameters. Under the assumption that the treatment is ignorable given some observed characteristics, the MLEs for those unknown parameters in the probability assignment model have been achieved firstly and then three estimators have been defined by the inverse probability weighted, regression and imputation methods, respectively. All the estimators are shown asymptotically normal and more importantly, the substantial efficiency gains of the first two estimates have been obtained theoretically compared with the existing estimators in Hahn (1998) and Hirano et al. (2003), i.e., the inverse weighted probability estimator and the regression estimator have smaller asymptotic variances. Our simulation analysis verifies the theoretical results in terms of biases, SEs and MSEs.     

The determinacy of the regression factor score predictor based on continuous parameter estimates from categorical variables

The estimation of population parameters of the continuous common factor model from categorical observed variables is meanwhile regularly performed. It is shown that the formula for the calculation of the determinacy of the regression factor score predictor from the estimated model parameters has to be adapted under these conditions. A method for the calculation of this determinacy from the model parameters of the continuous population factor model based on categorical variables is proposed and evaluated by means of simulated population data. It turns out that using the uncorrected formula can lead to serious overestimation of determinacy for categorical variables.     

人民币内向均衡实际汇率与错位测算：1997-2007

 本文基于行为均衡汇率(BEER)理论,运用边限协整检验方法,测算了1997年1月—2007年12月期间人民币内向实际汇率均衡水平和错位程度。实证结果表明：人民币内向实际汇率在大部分时间里错位,不过总体而言错位并不严重。     

人民币汇率失衡与调整问题研究

 摘　　要:目前，学术界在人民币汇率问题的研究上存在着分歧。尤其是对均衡汇率的测算，在方法上存在不足。多数模型的设计实质上是把名义汇率或实际有效汇率的平滑值当作了均衡汇率，而非真正的经济学意义上的均衡汇率。本文设计了一种“多目标两阶段估值”的方法来测算均衡汇率：在第一阶段，依据实现均衡汇率的各个目标，分别估算不同目标条件下的均衡汇率；第二阶段，以第一阶段估算的均衡汇率和实现均衡汇率的各目标建立计量模型，得到均衡汇率精算值。在深入分析汇率失衡给宏观经挤带来的后果的基础上，依据均衡汇率的计算结果，本文认为，提升汇率符合中国的长期利益和根本利益，而且在觋阶段政府还不应该放弃对汇率调整的干预     

Generalized exchange and propensity for military service: The moderating effect of prior military exposure

The propensity for military service (PMS) of young Americans is an important issue for our Armed Forces. Since the 1990s, the PMS of young Americans has steadily declined. Overtime, a declining PMS may cause military mission degradation, lowering of military recruitment standards, base closures, and reinstatement of the unpopular military draft system. This paper investigates the moderator effect of prior military service on the Generalized Exchange-PMS relationship. Generalized exchange is when indirect benefits such as preserving freedom and the American way of life accrue to the larger society because of an individual's military service. This paper uses a structural equation modelling approach to analyse the moderating effect of prior military exposure on prospective recruits regarding their PMS. Findings indicate that the group of prospective recruits with prior military exposure had higher levels of PMS than the group without such exposure, that is, the young people with prior military exposure are more likely to enlist in the military than the young Americans with no prior military exposure.     

Another look into the effect of premarital cohabitation on duration of marriage: an approach based on matching

Summary.  The paper proposes an alternative approach to studying the effect of premarital cohabitation on subsequent duration of marriage on the basis of a strong ignorability assumption . The approach is called propensity score matching and consists of computing survival functions conditional on a function of observed variables (the propensity score), thus eliminating any selection that is derived from these variables. In this way, it is possible to identify a time varying effect of cohabitation without making any assumption either regarding its shape or the functional form of covariate effects. The output of the matching method is the difference between the survival functions of treated and untreated individuals at each time point. Results show that the cohabitation effect on duration of marriage is indeed time varying, being close to zero for the first 2–3 years and rising considerably in the following years.     

Estimating and using propensity score in presence of missing background data: an application to assess the impact of childbearing on wellbeing

Propensity score methods are an increasingly popular technique for causal inference. To estimate propensity scores, we must model the distribution of the treatment indicator given a vector of covariates. Much work has been done in the case where the covariates are fully observed. Unfortunately, many large scale and complex surveys, such as longitudinal surveys, suffer from missing covariate values. In this paper, we compare three different approaches and their underlying assumptions of handling missing background data in the estimation and use of propensity scores: a complete-case analysis, a pattern-mixture model based approach developed by Rosenbaum and Rubin (J Am Stat Assoc79:516–524, 1984), and a multiple imputation approach. We apply these methods to assess the impact of childbearing events on individuals’ wellbeing in Indonesia, using a sample of women from the Indonesia Family Life Survey. I am grateful to all the participants at the project “Poverty Dynamics and Fertility in Developing Countries” for their support and encouragement. Special thanks are due to Fabrizia Mealli for her insightful suggestions and discussions. I also thank Jungho Kim, who is the main author of the STATA code to produce Indonesia consumption expenditure. Finally, I thank Arnstein Aassve, and Letizia Mencarini for help working with the data and their very useful discussions, and Alexia Fuernkranz-Prskawetz, and Henriette Engelhardt for detailed comments and suggestions which have improved the paper. Financial support from CNR-EFS and COFIN 2005 is gratefully acknowledged.     

人民币汇率、出口贸易与经济增长——基于中美、中日双边贸易的比较分析

以中美、中日1994—2007年的双边贸易数据为基础,建立人民币汇率、出口贸易和经济增长的联立方程模型,研究三者之间的作用机制。研究结果表明：人民币汇率贬值通过扩大出口促进经济增长,而经济增长和出口导致人民币实际汇率升值,符合巴拉萨效应;出口与经济增长之间的ELG假说成立,而GLE假说不成立;中美贸易的参数检验效果明显优于中日贸易。为此,应采取稳定出口,促进消费、投资和出口协调发展;优化贸易结构,提升贸易竞争力;合理控制人民币波动幅度,加快人民币国际化进程等政策措施。     

现收现付制、降低缴费率与经济增长——基于中国省际面板数据的经验研究

利用中国2001-2011年的省际面板数据资料,通过联立方程模型研究中国现行养老保险制度降低缴费率的经济增长效应,研究发现,中国实际上仍为现收现付模式的城镇社会养老保险制度,缴费率变化对个人储蓄率产生显著为负的影响,对居民对子女的教育投资比重的影响为正,但不显著,而个人储蓄率和居民对子女的教育投资比重对经济增长的影响都显著为正。当前养老保险制度通过降低缴费率对经济增长的净效应为负,降低缴费率有利于经济增长。     

The effects of individually varying times of observations on growth parameter estimations in piecewise growth model

When using latent growth modeling (LGM), researchers often restrict the factor loadings, while the multilevel modeling (MLM) treats time as a metric variable. However, when individually varying times of observations are concerned in the longitudinal studies, the use of specified loadings would lead to inaccurate estimation. Based on piecewise growth modeling (PGM), this simulation study showed that (i) individually varying times of observations with larger boundaries got worse estimates and model fits when LGM was used; (ii) estimating the PGM across all the simulation situations was robust within MLM, whereas LGM got identically equal estimation with MLM only in the case of time boundaries of ±1 month or shorter; (iii) larger change of slope in piecewise modeling indicated better estimation.     

相关与无关多样化对地区经济发展的影响研究——基于省际面板数据的实证检验

从经济增长和经济稳定两个维度研究了相关多样化、无关多样化与经济发展之间的关系。利用2003-2010年30个省级单位的面板数据,运用了多样化熵指标测度方法,将产业多样化分解为相关与无关两部分。研究表明：相关多样化、无关多样化与经济增长之间均存在非线性关系,与经济稳定只存在线性关系。结合现有的样本取值来看,相关多样化对经济增长有正向的促进作用,但不利于地区经济稳定;无关多样化有利于地区经济稳定,却不利于地区经济增长。     

The effects of income imputation on microanalyses: evidence from the European Community Household Panel

Summary.  Social surveys are usually affected by item and unit non-response. Since it is unlikely that a sample of respondents is a random sample, social scientists should take the missing data problem into account in their empirical analyses. Typically, survey methodologists try to simplify the work of data users by 'completing' the data, filling the missing variables through imputation. The aim of the paper is to give data users some guidelines on how to assess the effects of imputation on their microlevel analyses. We focus attention on the potential bias that is caused by imputation in the analysis of income variables, using the European Community Household Panel as an illustration.     

2005年汇改后人民币汇率多次结构突变检验分析——基于循序检验方法

采用最新的多次结构突变循序检验方法,对2005年7月21日汇改后人民币汇率时间序列趋势项是否具有多次结构突变进行研究,并在多次结构突变检验结果的基础上对消除趋势后的人民币汇率数据进行分析,结果发现：人民币汇率时间序列是围绕着4个结构断点的分段趋势平稳的;人民币汇率服从分段趋势平稳的结论对汇率政策有效性、汇率与其他经济总量关系研究及汇率预测具有重要意义。     

人民币汇率对中国长期贸易顺差的影响性分析——基于TVP-VAR模型的实证检验

全球金融危机条件下,中国汇率操纵论甚嚣尘上,然而针对人民币汇率和中国贸易顺差关系的研究观点却存在很大分歧。从技术角度看,基于线性回归模块的标准VAR/SVAR模型并不适合分析中国汇率和贸易顺差之间的非线性关系。文章在国内率先采用时变参数VAR模型(TVP-VAR),并基于2001年6月-2011年3月的数据进行分析后发现人民币汇率对于中国长期贸易顺差的影响微小,即汇率非重要。