Misclassification of binary outcomes in network settings may bias the estimates of causal effects, including spillover effects that arise from social interactions, and may generate spurious causal effects. To address this issue, we develop a parametric framework that jointly estimates misclassification probabilities and causal effect parameters within a binary choice model with neighborhood exposure mappings. Monte Carlo simulations show that ignoring outcome misclassification or network-related variables leads to substantial bias, whereas the proposed method achieves a smaller bias and RMSE. By applying the method to microfinance and social network data from Karnataka, we find that under binary exposure, ignoring outcome misclassification yields statistically significant spillover and overall effects, whereas these effects become statistically insignificant once outcome misclassification is corrected for. Furthermore, omitting network-related variables overstates the direct effect. These results underscore the importance of jointly correcting for outcome misclassification and accounting for network-related variables to obtain credible causal inference.

In this paper, we propose a new procedure to test conditional independence assumption in studying casual inference for time series data. The conditional independence assumption is transformed to a nonparametric conditional moment test with the help of auxiliary variables which are allowed to affect policy choice but the dependence can be fully captured by potential outcomes and observable controls. When the policy choice is binary, a nonparametric statistic test is developed further for testing the conditional independence assumption conditional on policy propensity score. Under some regular conditions, we show that the proposed test statistics are asymptotically normal under the null hypotheses for time series data. In addition, the performances of the proposed methods are illustrated through Monte Carlo simulations and a real example considered in Angrist and Kuersteiner (2011).

This paper proposes a nonparametric test to assess whether there exist heterogeneous quantile treatment effects (QTEs) of an intervention on the outcome of interest across different sub-populations defined by covariates of interest. Specifically, a consistent test statistic based on the Cramér–von Mises type criterion is developed to test if the treatment has a constant quantile effect for all sub-populations defined by covariates of interest. Under some regularity conditions, the asymptotic behaviors of the proposed test statistic are investigated under both the null and alternative hypotheses. Furthermore, a nonparametric Bootstrap procedure is suggested to approximate the finite-sample null distribution of the proposed test; then, the asymptotic validity of the proposed Bootstrap test is theoretically justified. Through Monte Carlo simulations, we demonstrate the power properties of the test in finite samples. Finally, the proposed testing approach is applied to investigate whether there exists heterogeneity for the QTE of maternal smoking during pregnancy on infant birth weight across different age groups of mothers.

Sequential Monte Carlo (SMC) methods are a class of Monte Carlo methods that are used to obtain random samples of a high dimensional random variable in a sequential fashion. Many problems encountered in applications often involve different types of constraints. These constraints can make the problem much more challenging. In this paper, we formulate a general framework of using SMC for constrained sampling problems based on forward and backward pilot resampling strategies. We review some existing methods under the framework and develop several new algorithms. It is noted that all information observed or imposed on the underlying system can be viewed as constraints. Hence the approach outlined in this paper can be useful in many applications.

危机中孕育新机,变局中开创新局,加快建设全国统一大市场是新时代我国构建新发展格局的基础支撑和内在要求.首先,从抵御外部不稳定因素和实现自主创新两个视角展开,阐明加快建设全国统一大市场是我国积极应对百年未有之大变局的划时代举措.其次,从地方保护主义制度根源和加快建设全国统一大市场的政策演进两个方面对我国相关的制度渊源进行系统梳理.再次,从经济影响视角,分析指出加快建设全国统一大市场是与我国经济社会发展相适应的动态演进过程,对于实现我国经济高质量发展和提升企业竞争力具有深远影响.最后,聚焦新能源汽车产业,通过案例研究的方法具体分析地方保护主义对新能源汽车产业健康发展的危害,进而展望全国统一大市场建设对于释放新能源汽车产业的市场潜力以及实现可持续发展的重大机遇.

The era of big data brings opportunities and challenges to developing new statistical methods and models to evaluate social programs or economic policies or interventions. This paper provides a comprehensive review on some recent advances in statistical methodologies and models to evaluate programs with high-dimensional data. In particular, four kinds of methods for making valid statistical inferences for treatment effects in high dimensions are addressed. The first one is the so-called doubly robust type estimation, which models the outcome regression and propensity score functions simultaneously. The second one is the covariate balance method to construct the treatment effect estimators. The third one is the sufficient dimension reduction approach for causal inferences. The last one is the machine learning procedure directly or indirectly to make statistical inferences to treatment effect. In such a way, some of these methods and models are closely related to the de-biased Lasso type methods for the regression model with high dimensions in the statistical literature. Finally, some future research topics are also discussed.

本文考虑了在带有协变量的非线性倍差法模型框架以及新的识别条件下,个体处理效应的估计问题.为了正确识别个体处理效应,假设在没有政策干预的情况下,控制组和处理组潜在结果变量的条件分布函数在两期内具有相同的变化,并且个体在接受处理或不接受处理的情况下,所对应的潜在结果变量的条件秩不变.基于所提出的模型和假设,发展了一种个体处理效应的估计方法.数值模拟结果表明所提出的估计量具有良好的有限样本性质.最后,所提出的模型和方法被应用于分析美国最低工资法案的实施对各县失业率的影响.

Different covariate balance weighting methods have been proposed by researchers from different perspectives to estimate the treatment effects. This paper gives a brief review of the covariate balancing propensity score method by Imai and Ratkovic (2014), the stable balance weighting procedure by Zubizarreta (2015), the calibration balance weighting approach by Chan, et al. (2016), and the integrated propensity score technique by Sant'Anna, et al. (2020). Simulations are conducted to illustrate the finite sample performance of both the average treatment effect and quantile treatment effect estimators based on different weighting methods. Simulation results show that in general, the covariate balance weighting methods can outperform the conventional maximum likelihood estimation method while the performance of the four covariate balance weighting methods varies with the data generating processes. Finally, the four covariate balance weighting methods are applied to estimate the treatment effects of the college graduate on personal annual income.

In this paper, we highlight some recent developments of a new route to evaluate macroeconomic policy effects, which are investigated under the framework with potential outcomes. First, this paper begins with a brief introduction of the basic model setup in modern econometric analysis of program evaluation. Secondly, primary attention goes to the focus on causal effect estimation of macroeconomic policy with single time series data together with some extensions to multiple time series data. Furthermore, we examine the connection of this new approach to traditional macroeconomic models for policy analysis and evaluation. Finally, we conclude by addressing some possible future research directions in statistics and econometrics.

The main goal of macro prudential policies is to maintain financial stability.This paper proposes adopting the macro-econometric policy evaluation method under the Rubin causal effect framework to evaluate the impact of China’s macro prudential policies on financial stability during the sample period 2007-2020.First,the paper constructs a macro prudential policy index to quantitatively measure the intensity of China’s macro prudential policies.Second,the paper uses the systemic financial risk index,termed as SRISK to measure China’s systemic financial risk.Finally,the paper evaluates the macro prudential policies’ effects on the systemic financial risk,crosssectoral contagion of systemic financial risk and important intermediate variables in the credit channel.Our empirical findings indicate that loose macro prudential policies can increase the risks of intermediate variables in the credit channel,and the risks lead to a significant rise in SRISK of house sector,but for the SRISK of financial and manufacturing sectors,the cumulative effects in 24 periods are not significant.However,in addition to a significant rise in commercial banks’ capital adequacy ratio growth,tight macro prudential policies have no significant effects on the other intermediate variables in the credit channel,and further have no obvious effects on SRISK of financial,house and manufacturing sectors.Based on the conclusions,we suggest that systemic risk indicators should be further researched to provide more comprehensive and systematic targets for macro prudential authorities.Moreover,the transmission channel of macro prudential policies on financial stability should be improved to enhance the efficiency of regulation.Finally,more attentions should be paid to the cross-sectoral contagion of systemic financial risk so as to prevent systemic financial risk from a systemic perspective.

本文基于Imai和Ratkovic (2014)协变量平衡倾向得分的估计方法,提出了带协变量平衡的GMM-LASSO估计方法.该方法既利用了协变量平衡的性质,同时又解决了如何基于数据来选取协变量的问题.理论上,文章证明了该估计方法是相合的.模拟显示,在满足一定的稀疏性的条件下,该方法可以显著地降低平均处理效应估计的绝对误差的中位数.最后,该方法被应用于研究2000年代初期意大利托斯卡纳地区的劳务派遣机制是否有助于工人寻找一份稳定的工作.

This paper proposes a new quantile regression model to characterize the heterogeneity for distributional effects of maternal smoking during pregnancy on infant birth weight across different the mother's age. By imposing a parametric restriction on the quantile functions of the potential outcome distributions conditional on the mother's age, we estimate the quantile treatment effects of maternal smoking during pregnancy on her baby's birth weight across different age groups of mothers. The results show strongly that the quantile effects of maternal smoking on low infant birth weight are negative and substantially heterogenous across different ages.

: We conduct a series of simulations to compare the finite sample performance of the average treatment e ff ect estimators based on four recently proposed methodologies — the covariate balancing propensity score method, the stable balance weighting approach, the calibration balance weighting procedure, and the integrated propensity score method. Simulation results show that the performance of the four covariate balance weighting methods are generally better than that for the conventional method, maximum likelihood estimation method without covariate balance, and among the four covariate balance weighting methods, it is di ffi cult to tell which covariate balance weighting method can dominate the others.

This paper proposes an alternative test procedure for testing the conditional unconfoundedness assumption which is an important identification condition commonly imposed in the literature of program analysis and policy evaluation. We transform the conditional unconfoundedness test to a nonparametric conditional moment test using an auxiliary variable which is independent of the treatment assignment variable conditional on potential outcomes and observable covariates. The proposed test statistic is shown to have a limiting normal distribution under the null hypothesis of conditional independence. Monte Carlo simulations are conducted to examine the finite sample performances of the proposed test statistics. Finally, the proposed test method is applied to test the conditional unconfoundedness in the real example of the return to college education.

In this article, we propose a new class of semiparametric instrumental variable models with partially varying coefficients, in which the structural function has a partially linear form and the impact of endogenous structural variables can vary over different levels of some exogenous variables. We propose a three-step estimation procedure to estimate both functional and constant coefficients. The consistency and asymptotic normality of these proposed estimators are established. Moreover, a generalized F-test is developed to test whether the functional coefficients are of particular parametric forms with some underlying economic intuitions, and furthermore, the limiting distribution of the proposed generalized F-test statistic under the null hypothesis is established. Finally, we illustrate the finite sample performance of our approach with simulations and two real data examples in economics.

本文提出一种针对网络型数据的聚类动态面板引力模型,用于国际贸易流量网络的研究.该模型假设各贸易国分属于不同的潜在类别,各国间贸易流量对应的模型系数由出口国和进口国所属的类别决定.提出使用马尔可夫链蒙特卡罗方法对模型参数以及各贸易国所属的潜在类别进行贝叶斯估计.对2001-2015年60个国家间的贸易流量数据进行了实证分析.结果表明,所提出的模型能够对贸易国进行聚类,有效地提高贸易流量预测的精度.所提出的聚类动态面板引力模型可以被广泛的应用于其他动态网络型数据的研究.

《资源描述与检索》(Resource Description and Access,RDA)的问世,是国际编目理念和实践的深刻变革,也为国内中外文编目规则实现统一提供了最大机遇.介绍了RDA创立的“作品规范检索点”的概念及其作用,指出了国内中文编目规则在实现目录职能方面存在的明显不足,并通过几个实例论述了将“作品规范检索点”应用于国内中文编目的必要性和可能性,以便为实现国内中外文编目规则的统一创造重要的前提条件.

The literature of time series models with threshold effects makes the assumption of a constant threshold value over different periods. However, this time-homogeneity assumption tends to be too restrictive owing to the fact that the threshold value that triggers regime switching could possibly be time-varying. This study herein proposes a threshold model in which the threshold value is assumed to be a latent variable following an autoregressive (AR) process. The newly proposed model was estimated using a Markov Chain Monte Carlo (MCMC) algorithm under a Bayesian framework. The Monte Carlo simulations are presented to assess the effectiveness of the Bayesian approaches. An illustration of the model was made through an application to a regime-sensitive Taylor rule employing U.S. data.

Traditional Particle Filter (PF) algorithm based on Particle Swarm Optimization (PSOPF),which moves the moving particles to the high likelihood region,destroys the prediction distribution.When the likelihood function has many peaks,it has a large computation amount while filtering performance does not improved significantly.To solve this problem,a new PSOPF based on the Adjustment of the Likelihood (LA-PSOPF) was proposed.Under the premise of preserving the prediction distribution,the Particle Swarm Optimization (PSO) algorithm was used to adjust the likelihood distribution to increase the number of effective particles and improve the filtering performance.Meanwhile,a strategy of local optimization was introduced to scale down the swarm of PSO,reduce the amount of calculation and achieve the balance of accuracy and speed of estimation.The simulation results show that the proposed algorithm is better than PF and PSOPF when the measurement error is small and the likelihood function has many peaks,and the computing time is less than that of PSOPF.