论文检索

多个工具变量下的向量单调性假设AI 解读

When a researcher combines multiple instrumental variables for a single binary treatment, the monotonicity assumption of the local average treatment effects (LATE) framework can become restrictive: it requires that all units share a common direction of response even when separate instruments are shifted in opposing directions. What I call vector monotonicity, by contrast, simply assumes treatment uptake to be monotonic in all instruments. I characterize the class of causal…

第一价格拍卖中卖方预期收入的估计与推断AI 解读

I propose an estimator for the seller’s expected revenue function in a first-price sealed-bid auction with independent private values and symmetric bidders, who can exhibit constant relative risk aversion and bid according to the Bayesian Nash equilibrium. I build the proposed estimator from pseudo-private values, which can be estimated from observed bids, and show that it is pointwise and uniformly consistent: the corresponding optimal nonparametric rates of convergence can…

少簇与弱识别工具变量回归的野生自助法推断AI 解读

We study the wild bootstrap inference for instrumental variable regressions under an alternative asymptotic framework that the number of independent clusters is fixed, the size of each cluster diverges to infinity, and the within cluster dependence is sufficiently weak. We first show that the wild bootstrap Wald test controls size asymptotically up to a small error as long as the parameters of endogenous variables are strongly identified in at least one of the clusters.…

利用方差信息进行面板群组结构的谱聚类估计AI 解读

Consider a panel data setting where repeated observations on individuals are available. Often it is reasonable to assume that there exist groups of individuals that share similar effects of observed characteristics, but the grouping is typically unknown in advance. We first conduct a local analysis which reveals that the variances of the individual coefficient estimates contain useful information for the estimation of group structure. We then propose a method to estimate…

没有显著性星号是好消息：基于协变量平衡检验 p 值的再随机化AI 解读

Randomized experiments balance all covariates on average and are considered the gold standard for estimating treatment effects. Chance imbalances are nonetheless common in realized treatment allocations. To inform readers of the comparability of treatment groups at baseline, contemporary scientific publications often report covariate balance tables with not only covariate means by treatment group but also the associated p-values from significance tests of their differences.…

金融市场对细节的有限关注：约化式与结构估计证据AI 解读

We show that firm valuations fell after a key expense became more visible in financial statements. FAS 123-R required firms to deduct option compensation costs from earnings, instead of disclosing them in footnotes. Firms that granted high option pay experienced earnings reductions, while fundamentals remained unchanged. These firms were more likely to miss earnings forecasts, and they experienced recommendation downgrades and valuation declines. Our findings suggest that…

动态面板模型的正则化估计AI 解读

In a dynamic panel data model, the number of moment conditions increases rapidly with the time dimension, resulting in a large dimensional covariance matrix of the instruments. As a consequence, the generalized method of moments (GMM) estimator exhibits a large bias in small samples, especially when the autoregressive parameter is close to unity. To address this issue, we propose a regularized version of the one-step GMM estimator using three regularization schemes based on…

高维正则化分位数回归全局与局部最优解的次梯度分析AI 解读

Regularized quantile regression (QR) is a useful technique for analyzing heterogeneous data under potentially heavy-tailed error contamination in high dimensions. This paper provides a new analysis of the estimation/prediction error bounds of the global solution of $L_1$ -regularized QR (QR-LASSO) and the local solutions of nonconvex regularized QR (QR-NCP) when the number of covariates is greater than the sample size. Our results build upon and significantly generalize the…

含交互固定效应的分位数面板模型两步估计AI 解读

This paper considers the estimation of panel data models with interactive fixed effects where the idiosyncratic errors are subject to conditional quantile restrictions. An easy-to-implement two-step estimator is proposed for the coefficients of the observed regressors. In the first step, the principal component analysis is applied to the cross-sectional averages of the regressors to estimate the latent factors. In the second step, the smoothed quantile regression is used to…

高维多项 Logit 模型AI 解读

Summary The number of parameters in a standard multinomial logit model increases linearly with the number of choice alternatives and number of explanatory variables. Because many modern applications involve large choice sets with categorical explanatory variables, which enter the model as large sets of binary dummies, the number of parameters in a multinomial logit model is often large. This paper proposes a new method for data‐driven two‐way parameter clustering over…

强制安全带法规与交通死亡：重新评估AI 解读

Abstract Using data from the Fatality Analysis Reporting System for the period 1983–1997, Cohen and Einav ( Review of Economics and Statistics 2003; 85[4]: 828–843) found that mandatory seatbelt laws were associated with a 4–6% reduction in traffic fatalities among motor vehicle occupants. After successfully replicating their two‐way fixed effects estimates, we (1) add 22 years of data (1998–2019) to capture additional seatbelt policy variation and observe a longer…

随机前沿模型中的内生二元处理：萨尔瓦多土壤保护应用AI 解读

Summary Numerous programs exist to promote productivity, alleviate poverty, and enhance food security in developing countries. Stochastic frontier analysis can be helpful to assess their effectiveness. However, challenges can arise when accounting for treatment endogeneity, often intrinsic to these interventions. We study maximum likelihood estimation of stochastic frontier models when both the frontier and inefficiency depend on a potentially endogenous binary treatment. We…

异质与非线性效应下IV与OLS差距的实证分解AI 解读

Abstract This study proposes an econometric framework to interpret and empirically decompose the difference between instrumental variables (IV) and ordinary least squares (OLS) estimates given by a linear regression model when the true causal effects of the treatment are nonlinear in treatment levels and heterogeneous across covariates. I show that the IV-OLS coefficient gap consists of three estimable components: the difference in weights on the covariates, the difference…

部分线性模型中条件分位数过程的推断及失业福利影响应用AI 解读

Abstract We propose methods to estimate and make inferences on conditional quantile processes for models with both nonparametric and (locally or globally) linear components. We derive their asymptotic properties, optimal bandwidths, and uniform confidence bands over quantiles allowing for robust bias correction. Our framework covers the sharp regression discontinuity design, which is used to study the effects of unemployment insurance benefits extensions, focusing on…

配对随机实验中分位数处理效应的自助推断AI 解读

Abstract This paper examines methods of inference concerning quantile treatment effects (QTEs) in randomized experiments with matched-pairs designs (MPDs). Standard multiplier bootstrap inference fails to capture the negative dependence of observations within each pair and is therefore conservative. Analytical inference involves estimating multiple functional quantities that require several tuning parameters. Instead, this paper proposes two bootstrap methods that can…

学校多样性：移民与美国本土出生学生的教育表现AI 解读

Abstract We study the effect of exposure to immigrants on the educational outcomes of U.S.-born students, using a unique dataset combining population-level birth and school records from Florida. This research question is complicated by the substantial school selection of U.S.-born students, especially among White and comparatively affluent students, in response to the presence of immigrant students in the school. We propose a new identification strategy, comparing sibling…

社会互动、机制与均衡：学习时间和学业表现模型的证据AI 解读

We develop and estimate a model of student study time choices on a social network. The model is designed to exploit unique data in the Berea Panel Study. Study time data allow us to quantify an intuitive mechanism for academic social interactions: own study time may depend on friend study time in a heterogeneous manner. Social network data allow us to embed study time and resulting academic achievement in an estimable equilibrium framework. We develop a specification test…

非线性持久性下消费对收入冲击反应的异质性AI 解读

In this paper we use the enhanced consumption data in the Panel Survey of Income Dynamics (PSID) from 2005–2017 to explore the transmission of income shocks to consumption. We build on the nonlinear quantile framework introduced in Arellano et al. (2017). Our focus is on the estimation of consumption responses to persistent nonlinear income shocks in the presence of unobserved heterogeneity. To reliably estimate heterogeneous responses in our unbalanced panel, we develop…

存在第一阶段异质性的工具变量估计AI 解读

We propose a simple data-driven procedure that exploits heterogeneity in the first-stage correlation between an instrument and an endogenous variable to improve the asymptotic mean squared error (MSE) of instrumental variable estimators. We show that the resulting gains in asymptotic MSE can be quite large in settings where there is substantial heterogeneity in the first-stage parameters. We also show that a naive procedure used in some applied work, which consists of…

一个工具变量：恰好识别 IV 的偏差与覆盖率AI 解读

We revisit the finite-sample behavior of single-variable just-identified instrumental variables (just-ID IV) estimators, arguing that in most microeconometric applications, the usual inference strategies are likely reliable. Three widely-cited applications are used to explain why this is so. We then consider pretesting strategies of the form t1>c, where t1 is the first-stage t-statistic, and the first-stage sign is given. Although pervasive in empirical practice, pretesting…

无向二元关系数据的核密度估计AI 解读

We study nonparametric estimation of density functions for undirected dyadic random variables (i.e., random variables defined for all n≡defN2 unordered pairs of agents/nodes in a weighted network of order N). These random variables satisfy a local dependence property: any random variables in the network that share one or two indices may be dependent, while those sharing no indices in common are independent. In this setting, we show that density functions may be estimated by…

带删失选择规则的非可分样本选择模型AI 解读

We consider identification and estimation of nonseparable sample selection models with censored selection rules. We employ a control function approach and discuss different objects of interest based on (1) local effects conditional on the control function, and (2) global effects obtained from integration over ranges of values of the control function. We derive conditions for identification of these different objects and suggest strategies for estimation. Moreover, we provide…

线性模型的欠识别检验及其动态面板与资产定价应用AI 解读

This paper develops the links between overidentification tests, underidentification tests, score tests and the Cragg and Donald (1993, 1997) and Kleibergen and Paap (2006) rank tests in linear instrumental variable (IV) models. For the structural linear model y=Xβ+u, with the endogenous explanatory variables partitioned as X=x1X2, this general framework shows that standard underidentification tests are tests for overidentification in an auxiliary linear model, x1=X2δ+ɛ,…

利用 Wasserstein 生成对抗网络设计蒙特卡洛模拟AI 解读

When researchers develop new econometric methods it is common practice to compare the performance of the new methods to those of existing methods in Monte Carlo studies. The credibility of such Monte Carlo studies is often limited because of the discretion the researcher has in choosing the Monte Carlo designs reported. To improve the credibility we propose using a class of generative models that has recently been developed in the machine learning literature, termed…