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最后一期房贷偿还后的消费平滑：检验幅度假说AI 解读

Abstract We examine whether the magnitude of an anticipated income change affects consumption smoothing (the magnitude hypothesis). Although this hypothesis has been discussed for fifty years, we are one of the first to provide formal statistical evidence to support it. We consider the natural experiment of an individual's final mortgage payment, an anticipated income change, and examine how it affects credit card expenditure. We can identify causality because the dates of…

竞争政策与生产率增长：一项实证评估AI 解读

Abstract This paper estimates the impact of competition policy on total factor productivity growth for 22 industries in twelve OECD countries over 1995 to 2005. We find a positive and significant effect of competition policy as measured by created indexes. We provide results based on instrumental variables estimators and heterogeneous effects to support the causal nature of the established link. The effect is particularly strong for specific aspects of competition policy…

使用无效工具变量时局部平均处理效应的部分识别AI 解读

We derive nonparametric bounds for local average treatment effects (LATE) without imposing the exclusion restriction assumption or requiring an outcome with bounded support. Instead, we employ assumptions requiring weak monotonicity of mean potential and counterfactual outcomes within or across subpopulations defined by the values of the potential treatment status under each value of the instrument. The key element in our derivation is a result relating LATE to a causal…

密度不连续性的估计与推断AI 解读

Continuity or discontinuity of probability density functions of data often plays a fundamental role in empirical economic analysis. For example, for identification and inference of causal effects in regression discontinuity designs it is typically assumed that the density function of a conditioning variable is continuous at a cutoff point that determines assignment of a treatment. Also, discontinuity in density functions can be a parameter of economic interest, such as in…

含测量误差协变量的逆概率加权AI 解读

Inverse probability-weighted estimators are widely used in applications where data are missing due to nonresponse or censoring and in the estimation of causal effects from observational studies. Current estimators rely on ignorability assumptions for response indicators or treatment assignment and outcomes being conditional on observed covariates which are assumed to be measured without error. However, measurement error is common for the variables collected in many…

序贯治疗决策中最优动态治疗方案的稳健估计AI 解读

A dynamic treatment regime is a list of sequential decision rules for assigning treatment based on a patient's history. Q- and A-learning are two main approaches for estimating the optimal regime, i.e., that yielding the most beneficial outcome in the patient population, using data from a clinical trial or observational study. Q-learning requires postulated regression models for the outcome, while A-learning involves models for that part of the outcome regression…

没有生存数据的生存分析：连接长度偏倚与病例对照数据AI 解读

We show that relative mean survival parameters of a semiparametric log-linear model can be estimated using covariate data from an incident sample and a prevalent sample, even when there is no prospective follow-up to collect any survival data. Estimation is based on an induced semiparametric density ratio model for covariates from the two samples, and it shares the same structure as for a logistic regression model for case-control data. Likelihood inference coincides with…

概率因果性与相互依赖模式集合的检测AI 解读

Summary Interdependent effects are usually distinguished from statistical interaction by using the sufficient causes framework. This almost always involves expressing probability distributions as deterministic logic functions, where certain conditions invariably produce or prevent an outcome. Using an idea from the philosophy literature, that a cause is defined as an event which increases the probability of an outcome, a probabilistic sufficient causes framework is developed…

分析师覆盖的负面影响：企业创新的证据AI 解读

We examine the effects of financial analysts on the real economy in the case of innovation. Our baseline results show that firms covered by a larger number of analysts generate fewer patents and patents with lower impact. To establish causality, we use a difference-in-differences approach that relies on the variation generated by multiple exogenous shocks to analyst coverage, as well as an instrumental variable approach. Our identification strategies suggest a negative…

利用次要结局改进存在不依从的随机实验推断AI 解读

We develop new methods for analyzing randomized experiments with noncompliance and, by extension, instrumental variable settings, when the often controversial, but key, exclusion restriction assumption is violated. We show how existing large-sample bounds on intention-to-treat effects for the subpopulations of compliers, never-takers, and always-takers can be tightened by exploiting the joint distribution of the outcome of interest and a secondary outcome, for which the…

随机临床试验中依方案生存终点疗效的敏感性分析AI 解读

Assessing per-protocol treatment effcacy on a time-to-event endpoint is a common objective of randomized clinical trials. The typical analysis uses the same method employed for the intention-to-treat analysis (e.g., standard survival analysis) applied to the subgroup meeting protocol adherence criteria. However, due to potential post-randomization selection bias, this analysis may mislead about treatment efficacy. Moreover, while there is extensive literature on methods for…

无排除限制的处理效应估计：学校早餐计划应用AI 解读

SUMMARY The increase in childhood obesity has garnered the attention of many in policymaking circles. Consequently, school nutrition programs such as the School Breakfast Program (SBP) have come under scrutiny. The identification of the causal effects of such programs, however, is difficult owing to non‐random selection into the program and the lack of exclusion restrictions. Here, we propose two new estimators aimed at addressing this situation. We compare our new…

稀疏有向无环图马尔可夫等价类上的可逆MCMCAI 解读

Graphical models are popular statistical tools which are used to represent dependent or causal complex systems. Statistically equivalent causal or directed graphical models are said to belong to a Markov equivalent class. It is of great interest to describe and understand the space of such classes. However, with currently known algorithms, sampling over such classes is only feasible for graphs with fewer than approximately 20 vertices. In this paper, we design reversible…

个人税收影响资本结构吗？来自2003年减税的证据AI 解读

Because the personal tax treatments of interest and dividend income likely affect the relative cost of debt and equity financing, a sharp change in tax treatment could affect firms' optimal leverage. This paper examines the effect of the 2003 equity income tax cut on firms' debt usage. Because this tax cut affected only individual investors, we can use a difference-in-differences method to identify the effect of personal tax on firms' leverage. Previous research has found…

金融冲击的实体效应：来自分析师覆盖外生变化的证据AI 解读

ABSTRACT We study the causal effects of analyst coverage on corporate investment and financing policies. We hypothesize that a decrease in analyst coverage increases information asymmetry and thus increases the cost of capital; as a result, firms decrease their investment and financing. We use broker closures and broker mergers to identify changes in analyst coverage that are exogenous to corporate policies. Using a difference‐in‐differences approach, we find that firms that…

基于倾向得分的估计量表现AI 解读

We investigate the finite sample properties of a large number of estimators for the average treatment effect on the treated that are suitable when adjustment for observed covariates is required, like inverse probability weighting, kernel and other variants of matching, as well as different parametric models. The simulation design used is based on real data usually employed for the evaluation of labour market programmes in Germany. We vary several dimensions of the design…

内生性下的无条件分位数处理效应AI 解读

This article develops estimators for unconditional quantile treatment effects when the treatment selection is endogenous. We use an instrumental variable (IV) to solve for the endogeneity of the binary treatment variable. Identification is based on a monotonicity assumption in the treatment choice equation and is achieved without any functional form restriction. We propose a weighting estimator that is extremely simple to implement. This estimator is root n consistent,…

社会网络与同伴效应的识别AI 解读

There is a large and growing literature on peer effects in economics. In the current article, we focus on a Manski-type linear-in-means model that has proved to be popular in empirical work. We critically examine some aspects of the statistical model that may be restrictive in empirical analyses. Specifically, we focus on three aspects. First, we examine the endogeneity of the network or peer groups. Second, we investigate simultaneously alternative definitions of links and…

治疗转换下过度离散复发事件的时变效应估计AI 解读

In the analysis of multivariate event times, frailty models assuming time-independent regression coefficients are often considered, mainly due to their mathematical convenience. In practice, regression coefficients are often time dependent and the temporal effects are of clinical interest. Motivated by a phase III clinical trial in multiple sclerosis, we develop a semiparametric frailty modelling approach to estimate time-varying effects for overdispersed recurrent events…

缺失数据估计：超越双重稳健性AI 解读

We propose an estimator that is more robust than doubly robust estimators, based on weighting complete cases using weights other than inverse probability when estimating the population mean of a response variable subject to ignorable missingness. We allow multiple models for both the propensity score and the outcome regression. Our estimator is consistent if any of the multiple models is correctly specified. Such multiple robustness against model misspecification is a…

元分析中异质性方差的估计AI 解读

Summary Several new estimators of the between-study variability in a heterogeneous random effects meta-analysis model are derived. One is the unbiased statistic which is locally optimal for small values of the parameter. Others are Bayes procedures within a class of quadratic statistics for a diffuse prior with different choices of the prior mean. These estimators are compared with the DerSimonian–Laird procedure and the Hedges statistic in particular via the quadratic risk…

协变量含 Berkson 型误差的回归：非参数方法AI 解读

This paper establishes that so-called instrumental variables enable the identification and the estimation of a fully nonparametric regression model with Berkson-type measurement error in the regressors. An estimator is proposed and proven to be consistent. Its practical performance and feasibility are investigated via Monte Carlo simulations as well as through an epidemiological application investigating the effect of particulate air pollution on respiratory health. These…

为未来比较研究有效选择目标人群AI 解读

When comparing a new treatment with a control in a randomized clinical study, the treatment effect is generally assessed by evaluating a summary measure over a specific study population. The success of the trial heavily depends on the choice of such a population. In this paper, we show a systematic, effective way to identify a promising population, for which the new treatment is expected to have a desired benefit, utilizing the data from a current study involving similar…

基于指数平方损失的稳健变量选择AI 解读

Robust variable selection procedures through penalized regression have been gaining increased attention in the literature. They can be used to perform variable selection and are expected to yield robust estimates. However, to the best of our knowledge, the robustness of those penalized regression procedures has not been well characterized. In this paper, we propose a class of penalized robust regression estimators based on exponential squared loss. The motivation for this…