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均值泛函估计的改进分治方法及其平均处理效应应用AI 解读

Mean estimation is an important issue in statistical inference and machine learning. We are concerned with estimating mean functional that is a function of several nonparametric functions when there is a large amount of observations. Directly estimating such mean functional through nonparametric smoothing has the complexity of at least a quadratic order of the sample size, which is computationally prohibitive for massive data. The divide-and-conquer approach are thus readily…

一般缺失模式下高维因子模型的推断AI 解读

This paper establishes the inferential theory for the least squares estimation of large factor models with missing data. We propose a unified framework for asymptotic analysis of factor models that covers a wide range of missing patterns, including heterogenous random missing, selection on covariates/factors/loadings, block/staggered missing, mixed frequency and ragged edge. We establish the average convergence rates of the estimated factor space and loading space, the limit…

交互固定效应与短面板下的动态处理效应估计AI 解读

We study the estimation and inference of dynamic average treatment effect parameters when parallel trends holds conditional on interactive fixed effects and where units enter into treatment at different time periods. Our proposed generalized method of moments estimator consists of two parts: first, we estimate the unobserved time effects by applying the fixed-T consistent quasi-long-differencing estimator of Ahn et al., (2013) to the never-treated group. Second, we estimate…

分组固定效应模型的简便估计方法AI 解读

This paper introduces a new fixed effects estimator for linear panel data models with clustered time patterns of unobserved heterogeneity. The method avoids non-convex and combinatorial optimization by combining a preliminary consistent estimator of the slope coefficient, an agglomerative pairwise-differencing clustering of cross-sectional units, and a pooled ordinary least squares regression. Asymptotic guarantees are established in a framework where T can grow at any power…

利用指数可逆性加速动态离散选择模型估计AI 解读

Many estimators of dynamic discrete choice models with persistent unobserved heterogeneity have desirable statistical properties but are computationally intensive. In this paper we propose a method to quicken estimation for a broad class of dynamic discrete choice problems by exploiting semiparametric index restrictions. Specifically, we propose an estimator for models whose reduced form parameters are invertible functions of one or more linear indices (Ahn et al., 2018) , a…

成对有效的工具变量AI 解读

Finding valid instruments is difficult. We propose Validity Set Instrumental Variable (VSIV) estimation, a method for estimating local average treatment effects (LATEs) in heterogeneous causal effect models when the instruments are partially invalid. We consider settings with pairwise valid instruments, that is, instruments that are valid for a subset of instrument value pairs. VSIV estimation exploits testable implications of instrument validity to remove invalid pairs and…

信任作为进入壁垒：金融科技采用的证据AI 解读

This paper studies the role of trust in incumbent lenders (banks) as an entry barrier to emerging FinTech lenders in credit markets. The empirical setting exploits the outbreak of the Wells Fargo scandal as a negative shock to borrowers’ trust in banks. Using a difference-in-differences framework, I find that increased exposure to the Wells Fargo scandal leads to an increase in the probability of borrowers using FinTech as mortgage originators. Utilizing political…

2024年 Denis Sargan 计量经济学奖AI 解读

The editors1 of The Econometrics Journal decided to award the 2024 Denis Sargan Econometrics Prize to Rahul Singh (Harvard) and Liyang Sun (CEMFI and UCL) for their article “Double robustness for complier parameters and a semi-parametric test for complier characteristics” in the January 2024 issue of The Econometrics Journal.2 Suppose that we are interested in the effects of a binary treatment on some outcome and allow these effects to vary arbitrarily in the studied…

一步平滑样条工具变量回归AI 解读

Summary We extend nonparametric regression smoothing splines to a context where there is endogeneity and instrumental variables are available. Unlike popular existing estimators, the resulting estimator is one step and relies on a unique regularisation parameter. We derive rates of the convergence for the estimator and its first derivative, which are uniform in the support of the endogenous variable. We also address the issue of imposing monotonicity in estimation and extend…

非线性面板数据模型的共同相关效应估计AI 解读

Summary This paper focuses on estimating the coefficients and average partial effects of observed regressors in nonlinear panel data models with interactive fixed effects, using the common correlated effects framework. The proposed two-step estimation method involves applying principal component analysis to estimate the latent factors based on cross-sectional averages of the regressors in the first step, and jointly estimating the coefficients of the regressors and the…

伪面板最小距离估计中的部分匹配样本修正AI 解读

Summary Certain repeated cross-sectional data sets, such as the Current Population Survey (CPS), use special sampling designs by which samples from different times periods are partially matched. This paper proposes a correction to the optimal weighting matrix in minimum distance (MD) estimation of pseudo-panel models to account for such partially matched samples. This partially matched sample correction may be needed if the sample matching rate is nontrivial and, at the same…

不可观测项对称性下进入博弈的识别与估计AI 解读

Summary This paper provides a new point identification and estimation method for two-player entry games with complete information, based on symmetry of unobservables. Neither equilibrium selection nor parametric distributional assumptions are required. In addition, a weaker support condition is used relative to the existing literature. Unlike other semiparametric estimators, the estimator proposed here is $\sqrt{n}$-consistent. A test of the required symmetry condition is…

二元潜变量模型的简便闭式估计AI 解读

Summary This paper develops a closed-form non-parametric estimator of the conditional distribution function for a binary outcome variable given an unobserved latent variable. This type of function is commonly used in models that involve measurement error and dynamic models with agent-specific unobserved heterogeneity. This paper presents a consistent extremum sieve estimator with the following advantages: (i) it has a closed-form expression for all the sieve coefficients,…

条件分位数处理效应异质性的非参数检验AI 解读

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…

不可观测因素是否可分离？AI 解读

It is common to assume in empirical research that observables and unobservables are additively separable, especially when the former are endogenous. This is because it is widely recognized that identification and estimation challenges arise when interactions between the two are allowed for. Starting from a nonseparable IV model, where the instrumental variable is independent of unobservables, we develop a novel nonparametric test of separability of unobservables. The…

交互固定效应下部分识别面板模型的推断AI 解读

In this paper, we develop methods for statistical inferences in a partially identified nonparametric panel data model with endogeneity and interactive fixed effects. Under some normalization rules, we can concentrate out the large-dimensional parameter vector of factor loadings and specify a set of conditional moment restrictions that are involved with only the finite-dimensional factor parameters along with the infinite-dimensional nonparametric component. For a conjectured…

美国最低工资与青少年生育AI 解读

ABSTRACT The minimum wage is increasingly viewed as an important, but often neglected, tool for improving public health outcomes. Using data from the period 2003–2019 and a stacked difference‐in‐differences regression model that accounts for dynamic and heterogeneous treatment effects, we explore the relationship between minimum wages and teenage childbearing in the United States. We find no evidence of a systematic, negative relationship between minimum wages and…

处理交互作用与内生参与AI 解读

ABSTRACT We study causal inference in randomized experiments (or quasi‐experiments) following a factorial design. There are two treatments, denoted and , and units are randomly assigned to one of four categories: treatment alone, treatment alone, joint treatment, or none. Allowing for endogenous non‐compliance with the two binary instruments representing the intended assignment, as well as unrestricted interference across the two treatments, we derive the causal…

处理状态误分类下的双重差分AI 解读

ABSTRACT This paper studies identification and estimation of the average treatment effect of a latent treated subpopulation in difference‐in‐difference designs when the observed treatment is differentially (or endogenously) mismeasured for the truth. Common examples include misreporting and mistargeting. We propose a two‐step estimator that corrects for the empirically common phenomenon of one‐sided misclassification in the treatment status. The solution uses a single…

簇刀切工具变量估计AI 解读

Abstract Researchers commonly use jackknife-based instrumental variables estimators to eliminate the many-instruments bias of two-stage least squares. Where inference must be clustered, however, the jackknife fails to eliminate the bias. We propose a cluster-jackknife approach in which first-stage predicted values for each observation are constructed from a regression that leaves out the observation’s entire cluster. The cluster-jackknife instrumental variables estimator…

面板数据模型中逐系数结构突变的估计AI 解读

When estimating structural breaks, existing econometric methods adopt an approach in which either all parameters change simultaneously, or they remain the same. In this paper, we consider the estimation of panel data models when an unknown subset of coefficients is subject to breaks. The challenge lies in estimating the breaks for each coefficient. To tackle this, we propose a new estimator for panel data, the “Coefficient-by-Coefficient Lasso” break estimator. This…

二元指标设定下的剥夺状况分布推断AI 解读

The paper addresses the issue of comparing deprivation distributions when the severity of deprivation is measured by a sum of (weighted) binary variables. To accomplish this task, it provides a graphical tool, the Three I’s of Deprivation (TID) curve, which summarises the incidence, intensity and inequality aspects of deprivation in a society and is the natural counterpart to the TIP curve widely used in income poverty analysis. Uncertainty around the estimated deprivation…

阈值政策设计中的遗憾分析AI 解读

Threshold policies are decision rules that assign treatments based on whether an observable characteristic exceeds a certain threshold. They are widespread across multiple domains, including welfare programs, taxation, and clinical medicine. This paper examines the problem of designing threshold policies using experimental data, when the goal is to maximize the population welfare. First, I characterize the regret – a measure of policy optimality – of the Empirical Welfare…

协变量自适应随机化下多回归量的调整AI 解读

Our paper discovers a new trade-off of using regression adjustments (RAs) in causal inference under covariate-adaptive randomizations (CARs). On one hand, RAs can improve the efficiency of causal estimators by incorporating information from covariates that are not used in the randomization. On the other hand, RAs can degrade estimation efficiency due to their estimation errors, which are not asymptotically negligible when the number of regressors is of the same order as the…