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局部条件尾部平均处理效应的估计AI 解读

The conditional tail average treatment effect (CTATE) is defined as a difference between the conditional tail expectations of potential outcomes, which can capture heterogeneity and deliver aggregated local information on treatment effects over different quantile levels and is closely related to the notion of second-order stochastic dominance and the Lorenz curve. These properties render it a valuable tool for policy evaluation. In this article, we study estimation of the…

不可观测选择集下离散选择模型的识别与估计AI 解读

We propose a framework for nonparametric identification and estimation of discrete choice models with unobserved choice sets. We recover the joint distribution of choice sets and preferences from a cross-section of repeated choices. We assume that either the latent choice sets are sparse or that the number of repeated choices is sufficiently large. Sparsity requires the number of possible choice sets to be relatively small. It is satisfied, for instance, when the choice sets…

具有趋势性时变系数的空间面板数据模型AI 解读

This article investigates the estimation and inference of spatial panel data models in which the regression coefficient vector is a trending function. We use time differences to eliminate the individual effects and employ various GMM estimations for regression coefficients with both linear and quadratic moments. Time trend estimator based on these GMM estimations is also proposed. Monte Carlo experiments show that the finite sample performance of the estimators is…

利用位移插值检测微弱分布变化AI 解读

Detecting weak, systematic distribution shifts and quantitatively modeling individual, heterogeneous responses to policies or incentives have found increasing empirical applications in social and economic sciences. Given two probability distributions P (null) and Q (alternative), we study the problem of detecting weak distribution shift deviating from the null P toward the alternative Q, where the level of deviation vanishes as a function of n, the sample size. We propose a…

Abadie 的 Kappa 与局部平均处理效应加权估计量AI 解读

Recent research has demonstrated the importance of flexibly controlling for covariates in instrumental variables estimation. In this paper we study the finite sample and asymptotic properties of various weighting estimators of the local average treatment effect (LATE), motivated by Abadie’s (2003) kappa theorem and offering the requisite flexibility relative to standard practice. We argue that two of the estimators under consideration, which are weight normalized, are…

分位数政策效应：美国宏观审慎政策应用AI 解读

To assess the dynamic distributional impacts of macroeconomic policy, we propose quantile policy effects to quantify disparities between the quantiles of potential outcomes under different policies. We first identify quantile policy effects under the unconfoundedness assumption and propose an inverse probability weighting estimator. We then examine the asymptotic behavior of the proposed estimator in a time series framework and suggest a blockwise bootstrap method for…

含内生主导单元的非线性空间动态面板模型：份额数据应用AI 解读

This article develops a nonlinear spatial dynamic panel data model with one particularly interesting application to a structural interaction model for share data. To account for effects from dominant (popular) units, the spatial weights matrix in our model can allow for unbounded column sums. To account for heterogeneity, our model includes two-way fixed effects and heteroscedastic errors. We further consider the potential endogeneity of the spatial weight matrix constructed…

降秩时空模型AI 解读

To simultaneously model the cross-sectional dependency and dynamic time dependency among n units, most research in spatial econometrics parameterizes the coefficient matrices among the n units as functions of known weights matrices. This modeling framework is over-simplified and faces the risk of misspecification when constructing the weights matrices. In this article, we propose a novel reduced-rank spatio-temporal model by assuming the coefficient matrices follow a…

异质误差方差下线性面板模型的分组异质性AI 解读

We develop a procedure to identify latent group structures in linear panel data models that exploits a grouping in the error variances of cross-sectional units. To accommodate such grouping, we introduce an objective function that avoids a singularity that arises in a pseudolikelihood approach. We provide theoretical and numerical evidence showing when allowing for variance groups improves classification. The developed procedure provides new evidence on the relation between…

基于 Hyvärinen 评分的全数据驱动归一化幂化核密度估计AI 解读

Recently, Jewson and Rossell proposed a new approach for kernel density estimation using an exponentiated form of kernel density estimators. The density estimator contained two hyperparameters that flexibly controls the smoothness of the resulting density. We tune them in a data-driven manner by minimizing an objective function based on the Hyvärinen score to avoid the optimization involving the intractable normalizing constant caused by the exponentiation. We show the…

竞争市场中的信息价值：来自中小企业的证据AI 解读

We empirically investigate how the performance of small and medium-sized enterprises (SMEs) changes when gaining access to market information. To do so, we evaluate the impact of an information program diffused by a bank among its SME customers. Adopting firms gained access to reports with rich information about their own clientele and that of nearby establishments. While we find that adoption is associated with a 4.5% revenue increase, our instrumental variable results…

随机系数 Logit 需求模型估计中的模拟误差与数值不稳定AI 解读

The nonlinear GMM-IV estimator of Berry, Levinsohn and Pakes (1995) can suffer from numerical instability resulting in a wide range of parameter estimates and economic implications. This has been reported to depend on technical details such as the choice of the optimization algorithm, starting values, and convergence criteria. We show that numerical approximation errors in the estimator’s moment function are the main driver of this instability. With accurate approximation,…

连续内生变量与离散工具变量下非参数回归的迭代估计AI 解读

We consider a nonparametric regression model with continuous endogenous independent variables when only discrete instruments are available that are independent of the error term. Although this framework is very relevant for applied research, its implementation is challenging, as the regression function becomes the solution to a nonlinear integral equation. We propose a simple iterative procedure to estimate such models and showcase some of its asymptotic properties. In a…

个体福利分析：随机拟线性效用、独立性与置信界AI 解读

We introduce a novel framework for individual-level welfare analysis. It builds on a parametric model for continuous demand with a quasilinear utility function, allowing for heterogeneous coefficients and unobserved individual-good-level preference shocks. We obtain bounds on the individual-level consumer welfare loss at any confidence level due to a hypothetical price increase, solving a scalable optimization problem constrained by a novel confidence set under an…

允许网络随时间变化的面板空间与社会网络依赖检验AI 解读

The paper introduces robust generalized Moran I tests for network-generated cross-sectional dependence in a panel data setting where unit-specific effects can be random or fixed. Network dependence may originate from endogenous variables, exogenous variables, and/or disturbances, and the network dependence is allowed to vary over time. The formulation of the test statistics also aims at accommodating situations where the researcher is unsure about the exact nature of the…

信贷供给与房价：来自抵押贷款市场分割的证据AI 解读

This paper develops a difference-in-differences estimator that uses annual changes in the conforming loan limit and the 80% loan-to-value (LTV) threshold to isolate the impact of easier access to credit on house prices. Houses that become eligible for financing with an 80% LTV conforming loan increase in value by about $1.17 per square foot, controlling for a rich set of characteristics. Our estimates imply a local elasticity of house prices to interest rates below 6, which…

从 MIDAS 视角看不确定性、偏度与商业周期AI 解读

ABSTRACT We employ a mixed‐frequency quantile regression approach to model the time‐varying conditional distribution of the US real GDP growth rate. We show that monthly information on financial conditions improves the predictive power of an otherwise quarterly‐only model. We combine selected quantiles of the estimated conditional distribution to produce novel measures of uncertainty and skewness. Embedding these measures in a VAR framework, we show that unexpected changes…

交互效应面板模型中的多重结构突变AI 解读

ABSTRACT This paper develops new econometric methods for multiple structural break detection in panel data models with interactive fixed effects. The new methods include tests for the presence of structural breaks, estimators for the number of breaks and their location, and a method for constructing asymptotically valid break date confidence intervals. The new methodology is applied to a large panel of US banks for a period characterized by massive quantitative easing…

基于分位数的处理效应异质性检验AI 解读

ABSTRACT We introduce a permutation test for heterogeneous treatment effects based on the quantile process. However, tests based on the quantile process often suffer from estimated nuisance parameters that jeopardize their validity, even in large samples. To overcome this problem, we use Khmaladze's martingale transformation. We show that the permutation test based on the transformed statistic controls size asymptotically. Numerical evidence asserts the good size and power…

帮派、劳动流动与发展AI 解读

We study how criminal organizations affect economic development. We exploit a natural experiment in El Salvador, where these criminal organizations emerged due to an exogenous shift in American immigration policy that led to the deportation of gang leaders from the United States to El Salvador. Using a spatial regression discontinuity design that focuses on the gang‐created system of borders, we find that individuals in gang‐controlled neighborhoods have less material…

社会税：再分配压力与劳动供给AI 解读

In low‐income communities in both rich and poor countries, redistributive transfers within kin and social networks are frequent. Such arrangements may distort labor supply—acting as a “social tax” that dampens the incentive to work. We document that across countries, from the United States to Côte d'Ivoire, low‐income groups report strong pressure to share earned income with others; in addition, social groups that undertake more interpersonal transfers work fewer hours.…

高阶风险偏好的结构估计AI 解读

Structural measures of higher order risk attitudes have well‐developed foundations in Expected Utility Theory (EUT), but little is known about their empirical magnitudes. We introduce a novel experimental design and a companion econometric model that allows us to structurally estimate indices of risk aversion, prudence, and temperance under EUT without imposing restrictions on their interdependence. We find that indices of absolute risk aversion, prudence, and temperance…

信息性先验的贝叶斯影响评估：哥伦比亚管理与出口改善项目AI 解读

Policymakers often test expensive new programs on relatively small samples. Formally incorporating informative Bayesian priors into impact evaluation offers the promise to learn more from these experiments. We evaluate a Colombian program for 200 firms which aimed to increase exporting. Priors were elicited from academics, policymakers, and firms. Contrary to these priors, frequentist estimation cannot reject null effects in 2019, and finds some negative impacts in 2020. For…

农村养老金、劳动力再配置与总收入：中国的实证和量化分析AI 解读

We exploit the implementation of a rural pension policy in China to estimate the average rural‐to‐urban migration cost for workers affected by the policy and the average underlying sectoral productivity difference. Our estimates, based on a large panel data set, reveal significant migration costs and substantial sectoral productivity differences, with sorting playing a minor role in accounting for sectoral labor income gaps. We construct and structurally estimate a general…