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国家分裂的经济学：评估分离独立的经济影响AI 解读

Abstract This paper presents estimates of the economic effects of secession for a large panel of countries that gained independence between 1940 and 2016. It relies on a semi‐parametric identification strategy that controls for the confounding effects of past gross domestic product (GDP) dynamics, anticipation effects, unobserved heterogeneity, model uncertainty, and effect heterogeneity. Our findings indicate that secession lowered per capita GDP, on average by around 24%…

计量经济学中的因果关系：选择与随机AI 解读

This essay describes the evolution and recent convergence of two methodological approaches to causal inference. The first one, in statistics, started with the analysis and design of randomized experiments. The second, in econometrics, focused on settings with economic agents making optimal choices. I argue that the local average treatment effects framework facilitated the recent convergence by making key assumptions transparent and intelligible to scholars in many fields.…

经济学中的实证策略：照亮从原因到结果的路径AI 解读

The view that empirical strategies in economics should be transparent and credible now goes almost without saying. By revealing for whom particular instrumental variables (IV) estimates are valid, the local average treatment effects (LATE) framework helped make this so. This lecture uses empirical examples, mostly involving effects of charter and exam school attendance, to illustrate the value of the LATE framework for causal inference. LATE distinguishes independence…

跨国公司、买方垄断与地方发展：联合果品公司的证据AI 解读

This paper studies the role of private sector companies in the development of local amenities. We use evidence from one of the largest multinationals of the 20th century: the United Fruit Company (UFCo). The firm was given a large land concession in Costa Rica—one of the so‐called “Banana Republics”—from 1899 to 1984. Using administrative census data with census‐block geo‐references from 1973 to 2011, we implement a geographic regression discontinuity design that exploits a…

巴西失业与失业保险对犯罪的影响AI 解读

We investigate the impact of job loss on crime and the mitigating role of unemployment benefits, exploiting detailed individual‐level data linking employment careers, criminal records, and welfare registries for the universe of male workers in Brazil. The probability of committing crimes increases on average by 23% for workers displaced by mass layoffs, and by slightly less for their cohabiting sons. Using causal forests, we show that the effect is entirely driven by young…

局部稳健的半参数估计AI 解读

Many economic and causal parameters depend on nonparametric or high dimensional first steps. We give a general construction of locally robust/orthogonal moment functions for GMM, where first steps have no effect, locally, on average moment functions. Using these orthogonal moments reduces model selection and regularization bias, as is important in many applications, especially for machine learning first steps. Also, associated standard errors are robust to misspecification…

因果与结构效应的自动去偏机器学习AI 解读

Many causal and structural effects depend on regressions. Examples include policy effects, average derivatives, regression decompositions, average treatment effects, causal mediation, and parameters of economic structural models. The regressions may be high‐dimensional, making machine learning useful. Plugging machine learners into identifying equations can lead to poor inference due to bias from regularization and/or model selection. This paper gives automatic debiasing for…

随机网络结构差异检验AI 解读

How can one determine whether a treatment, such as the introduction of a social program or trade shock, alters agents' incentives to form links in a network? This paper proposes analogs of a two‐sample Kolmogorov–Smirnov test, widely used in the literature to test the null hypothesis of no treatment effects, for network data. It first specifies a testing problem in which the null hypothesis is that two networks are drawn from the same random graph model. It then describes…

动态分配处理的长期效应：新方法与培训收入效应评估AI 解读

We propose and implement a new method to estimate treatment effects in settings where individuals need to be in a certain state (e.g., unemployment) to be eligible for a treatment, treatments may commence at different points in time, and the outcome of interest is realized after the individual left the initial state. An example concerns the effect of training on earnings in subsequent employment. Any evaluation needs to take into account that some of those who are not…

近似邻域干扰下的因果推断AI 解读

This paper studies causal inference in randomized experiments under network interference. Commonly used models of interference posit that treatments assigned to alters beyond a certain network distance from the ego have no effect on the ego's response. However, this assumption is violated in common models of social interactions. We propose a substantially weaker model of “approximate neighborhood interference” (ANI) under which treatments assigned to alters further from the…

打破同序：回归不连续设计与市场设计的结合AI 解读

Many schools in large urban districts have more applicants than seats. Centralized school assignment algorithms ration seats at over‐subscribed schools using randomly assigned lottery numbers, non‐lottery tie‐breakers like test scores, or both. The New York City public high school match illustrates the latter, using test scores and other criteria to rank applicants at the city's screened schools, combined with lottery tie‐breaking at the rest. We show how to identify causal…

最小化对模型误设的敏感性AI 解读

We propose a framework for estimation and inference when the model may be misspecified. We rely on a local asymptotic approach where the degree of misspecification is indexed by the sample size. We construct estimators whose mean squared error is minimax in a neighborhood of the reference model, based on one‐step adjustments. In addition, we provide confidence intervals that contain the true parameter under local misspecification. As a tool to interpret the degree of…

高维数据下的无条件分位数回归AI 解读

This paper considers estimation and inference for heterogeneous counterfactual effects with high‐dimensional data. We propose a novel robust score for debiased estimation of the unconditional quantile regression (Firpo, Fortin, and Lemieux (2009)) as a measure of heterogeneous counterfactual marginal effects. We propose a multiplier bootstrap inference and develop asymptotic theories to guarantee the size control in large sample. Simulation studies support our theories.…

经济不确定性与结构改革：来自股票市场波动的证据AI 解读

Does economic uncertainty promote the implementation of structural reforms? We answer this question using one of the most exhaustive cross‐country panel data sets on reforms in six major areas and measuring economic uncertainty with stock market volatility. To identify causality, we exploit exogenous differential variation in countries' exposure to foreign volatility shocks due to predetermined and time‐invariant bilateral characteristics. Across all specifications, we find…

半参数估计量的影响函数AI 解读

There are many economic parameters that depend on nonparametric first steps. Examples include games, dynamic discrete choice, average exact consumer surplus, and treatment effects. Often estimators of these parameters are asymptotically equivalent to a sample average of an object referred to as the influence function. The influence function is useful in local policy analysis, in evaluating local sensitivity of estimators, and constructing debiased machine learning…

识别假设的不确定性AI 解读

Uncertainty about the choice of identifying assumptions is common in causal studies, but is often ignored in empirical practice. This paper considers uncertainty over models that impose different identifying assumptions, which can lead to a mix of point‐ and set‐identified models. We propose performing inference in the presence of such uncertainty by generalizing Bayesian model averaging. The method considers multiple posteriors for the set‐identified models and combines…

高维乘法模型的信息论方法：随机贴现因子与处理效应AI 解读

This paper is concerned with estimation of functionals of a latent weight function that satisfies possibly high‐dimensional multiplicative moment conditions. Main examples are functionals of stochastic discount factors in asset pricing, missing data problems, and treatment effects. We propose to estimate the latent weight function by an information theoretic approach combined with the ℓ 1 ‐penalization technique to deal with high‐dimensional moment conditions under sparsity.…

模糊断点回归识别假设的检验AI 解读

We propose a new specification test for assessing the validity of fuzzy regression discontinuity designs (FRD‐validity). We derive a new set of testable implications, characterized by a set of inequality restrictions on the joint distribution of observed outcomes and treatment status at the cut‐off. We show that this new characterization exploits all of the information in the data that is useful for detecting violations of FRD‐validity. Our approach differs from and…

越早越好：意大利新冠疫情封锁挽救的生命AI 解读

Summary This paper estimates the effects of non-pharmaceutical interventions – mainly, the lockdown – on the COVID-19 mortality rate for the case of Italy, the first Western country to impose a national shelter-in-place order. We use a new estimator, the augmented synthetic control method (ASCM), that overcomes some limits of the standard synthetic control method (SCM). The results are twofold. From a methodological point of view, the ASCM outperforms the SCM in that the…

非线性半参数模型的正则化正交机器学习AI 解读

Summary This paper proposes a Lasso-type estimator for a high-dimensional sparse parameter identified by a single index conditional moment restriction (CMR). In addition to this parameter, the moment function can also depend on a nuisance function, such as the propensity score or the conditional choice probability, which we estimate by modern machine learning tools. We first adjust the moment function so that the gradient of the future loss function is insensitive (formally,…

含内生性的部分线性模型：基于条件矩的方法AI 解读

Summary In a partially linear conditional moment model we propose a new estimator for the slope parameter of the endogenous variable of interest, which combines a Robinson’s transformation to partial out the nonlinear part of the model, with a smooth minimum distance approach to exploit all the information of the conditional mean independence restriction. Our estimator only depends on one tuning parameter, is easy to compute, consistent and $\sqrt{n}$-asymptotically normal…

带凸包约束的合成控制法：贝叶斯最大后验方法AI 解读

Summary Synthetic control methods have gained popularity among causal studies with observational data, particularly when estimating the impacts of the interventions implemented to a small number of large units. The synthetic control methods face two major challenges: (a) estimating weights for each donor to create a synthetic control and (b) providing statistical inferences. To overcome these challenges, we propose a Bayesian framework that implements the synthetic control…

监禁如何影响再犯？剂量—反应函数的估计AI 解读

We study the causal effect of incarceration on reoffending using discontinuities in North Carolina’s sentencing guidelines. A regression discontinuity analysis shows that 1 year of incarceration causes a reduction in the likelihood of being reincarcerated within 3, 5, and 8 years from sentencing by 44%, 29%, and 21%, respectively. To parse the potentially heterogeneous dose response relationship underlying these effects, we develop an econometric model of prison sentences…

增广极小极大线性估计AI 解读

Many statistical estimands can expressed as continuous linear functionals of a conditional expectation function. This includes the average treatment effect under unconfoundedness and generalizations for continuous-valued and personalized treatments. In this paper, we discuss a general approach to estimating such quantities: we begin with a simple plug-in estimator based on an estimate of the conditional expectation function, and then correct the plug-in estimator by…