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未观测竞争下的第一价格拍卖非参数识别：密度断点框架AI 解读

We consider nonparametric identification of independent private value first-price auction models, in which the analyst only observes winning bids. Our benchmark model assumes an exogenous number of bidders N. We show that, if the bidders observe N, the resulting discontinuities in the winning bid density can be used to identify the distribution of N. The private value distribution can be nonparametrically identified in a second step. This extends, under testable…

集约边际与广延边际上的处理效应评估AI 解读

This paper provides a solution to the evaluation of treatment effects in selective samples when neither instruments nor parametric assumptions are available. We provide sharp bounds for average treatment effects under a conditional monotonicity assumption for all principal strata, i.e. units characterizing the complete intensive and extensive margins. Most importantly, we allow for a large share of units whose selection is indifferent to treatment, e.g. due to…

交互固定效应面板数据模型与（相对）小 TAI 解读

This paper studies the estimation of a linear panel data model with interactive fixed effects. A transformation is introduced which, after having been applied, renders the least squares (LS) estimator of Bai (2009) consistent and asymptotically unbiased when n is large and T is fixed. This is termed the transformed least squares (TLS) estimator. Going further, these properties are shown to also carry over to the large n, large T setting, provided T /n → 0. This contrasts…

高维面板数据模型的稳健估计与推断AI 解读

This paper provides the relevant literature with a complete toolkit for conducting robust estimation and inference about the parameters of interest involved in a high-dimensional panel data framework. Specifically, (1) we allow for non-Gaussian, serially and cross-sectionally correlated and heteroskedastic error processes, (2) we develop an estimation method for the high-dimensional long-run covariance matrix using a thresholded estimator, (3) we also allow for the number of…

时间效应序列相关下双向聚类的刀切法方差估计AI 解读

Chiang, Hansen, and Sasaki (2024) and Chen and Vogelsang (2024) developed cluster-robust variance estimators (CRVEs) for linear regressions with two-way clustered data allowing for arbitrary serial dependence. However, conventional CRVEs can perform poorly in finite samples. We propose jackknife CRVEs and establish their theoretical validity. Our theoretical and simulation results demonstrate several advantages of the proposed procedures. First, the jackknife CRVE delivers…

网络中的测量误差与同伴效应AI 解读

In many practical applications, only noisy proxies for the true regressors are available, which is commonly believed to induce an attenuation bias. In the linear-in-means model, however, estimated peer effects might be inflated, potentially leading to false positives. This paper shows that the asymptotic bias depends on the interplay between individual characteristics and network links and demonstrates how the network structure can facilitate identification without the need…

含不可观测个体特定阈值效应的面板阈值回归AI 解读

This paper provides a new approach to estimation and inference in multiple regime panel threshold regression with unobserved individual-specific threshold effects. Such effects are practically relevant and have features that distinguish them from traditional linear panel data models. It is shown that within-regime demeaning in the static model or within-regime first-differencing in the dynamic model both fail to deliver consistent threshold estimators. Instead, correlated…

非加性固定效应模型的识别：教师质量回报是否同质？AI 解读

Panel or grouped data are often used to allow for unobserved individual heterogeneity in econometric models via fixed effects. In this paper, we discuss identification of a panel data model in which the unobserved heterogeneity both enters additively and interacts with treatment variables. We present identification and estimation methods for parameters of interest in this model under both strict and weak exogeneity assumptions. The key identification insight is that other…

具有动态异质溢出的矩阵值空间自回归AI 解读

We introduce a new time-varying parameter spatial matrix autoregressive model that integrates matrix-valued time series, heterogeneous spillover effects, outlier robustness, and time-varying parameters in one unified framework. The model allows for separate dynamic spatial spillover effects across both the row and column dimensions of the matrix-valued observations. Robustness can be introduced via a matrix Student’s t distribution. The model can still be estimated…

基于离散傅里叶变换检验交互固定效应面板模型的结构变化AI 解读

We propose a discrete Fourier transform (DFT)-based test for detecting structural changes in panel data models with interactive fixed effects (IFE). When structural changes are present but ignored, the conventional asymptotic distribution of the least squares (LS) estimator of Bai (2009) may no longer provide reliable inference, and the resulting residuals contain signals of parameter instability. Our test exploits this feature by comparing the DFT of weighted residuals to…

潜在多维聚类：推断与实践AI 解读

We show how clustering standard errors in one or more dimensions can be justified in M-estimation when there is sampling or assignment uncertainty. Since existing procedures for variance estimation are either conservative or invalid, we propose a variance estimator that refines a conservative procedure and remains valid. We then interpret environments where clustering is frequently employed in empirical work from our design-based perspective and provide insights on their…

迭代分布式多项回归AI 解读

This article introduces an iterative distributed computing estimator for the multinomial logistic regression model with large choice sets. Compared to the maximum likelihood estimator, the proposed iterative distributed estimator achieves significantly faster computation and, when initialized with a consistent estimator, attains asymptotic efficiency under a weak dominance condition. Additionally, we propose a parametric bootstrap inference procedure based on the iterative…

We propose a novel identification strategy to estimate the long-term imprinting effects of being born in the Great Recession on innovative startups. After purging ubiquitous selection biases and sorting effects, we find that recession startups experience substantially better long-term outcomes in terms of survival and growth in employment and sales, despite being born when funding is scarce and demand is declining. In contrast to prior work, we find that the recession does…

内生性检验：一种基于矩的贝叶斯方法AI 解读

A standard assumption in the Bayesian estimation of linear regression models is that the regressors are exogenous in the sense that they are uncorrelated with the model error term. In practice, however, this assumption can be invalid. In this article, using the exponentially tilted empirical likelihood (ETEL) framework, we develop a Bayes factor test for endogeneity that compares a base model that is correctly specified under exogeneity but misspecified under endogeneity…

We estimate wage cyclicality using the user cost of labor (UCL), a measure of marginal labor cost. We propose a novel methodology to correct composition bias from cyclical jobto-job upgrading by exploiting school-to-employment flows. We implement the methodology using rich Japanese wage data. The estimated UCL is strongly procyclical, with a semi-elasticity of 2.52 with respect to the unemployment rate, which is 1.6 and 2.3 times as large as that of the new-hire and average…

平均处理效应局部化：合成控制中的投影方法AI 解读

Many real-world policies and business interventions require assessing short-term effects to inform timely decisions, even though most causal inference methods focus on long-term average treatment effects. In this article, we introduce average treatment effect localization (ATEL), which captures localized, short-term policy impacts in panel data settings with a single treated unit and provides early indicators of policy impact. To accommodate both time-varying and nonlinear…

协变量自适应随机实验中混合效应模型的统计推断AI 解读

Recent clinical studies increasingly involve a long factor with many levels, e.g., investigation sites, resulting in a large number of strata that must be accounted for either through design or subsequent analysis. This complication has prompted concerns from the U.S. Food and Drug Administration (FDA, 2023) regarding the adequacy of standard statistical methods, whose performance may deteriorate, and their properties become unclear when the number of strata is relatively…

集体家庭消费中的规模经济AI 解读

ABSTRACT Browning et al. (2013) model collective‐household economies of scale in goods consumption by having each good be partly shared, instead of each good being public or private. We modify their model to achieve simple point identification and estimation of the economies of scale of consumption of each good, and we provide a new index of household level economies of scale. Our model has a linear form, permits households of varying composition, including children, and…

非线性结构模型中边际效应分布与分位数的面板估计：垃圾食品需求应用AI 解读

In this paper, we propose a framework to estimate the distribution of marginal effects in a general class of structural models that allow for very general nonlinearities, high dimensional heterogeneity, and unrestricted correlation between the persistent components of this heterogeneity and all covariates. The main idea is to form a derivative dependent variable using two periods of the panel and use differences in outcome variables of nearby subpopulations to obtain the…

异质性部分干扰下观察性研究处理效应的半参数估计AI 解读

In many observational studies in social science and medicine, subjects or units are connected, and one unit’s treatment and attributes may affect another’s treatment and outcome, violating the stable unit treatment value assumption (SUTVA) and resulting in interference. To enable feasible estimation and inference, many previous works assume exchangeability of interfering units (neighbors). However, in many applications with distinctive units, interference is heterogeneous…

弱识别与少量聚类下工具变量分位数回归的梯度野生自助法AI 解读

We study the gradient wild bootstrap-based inference for instrumental variable quantile regressions in the framework of a small number of large clusters in which the number of clusters is viewed as fixed, and the number of observations for each cluster diverges to infinity. For the Wald inference, we show that our wild bootstrap Wald test, with or without studentization using the cluster-robust covariance estimator (CRVE), controls size asymptotically up to a small error as…

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异质性面板中的个体平均估计AI 解读

In this work, we introduce a unit averaging procedure to efficiently recover unit-specific parameters in a heterogeneous panel model. The procedure consists in estimating the parameter of a given unit using a weighted average of all the unit-specific parameter estimators in the panel. The weights of the average are determined by minimizing an MSE criterion we derive. We analyze the properties of the resulting minimum MSE unit averaging estimator in a local heterogeneity…

排序数据的静态与动态贝叶斯加性回归树模型AI 解读

Ranking lists are often provided at regular time intervals in a range of applications, including economics, sports, marketing, and politics. Most popular methods for rank-order data postulate a linear specification for the latent scores, which determine the observed ranks, and ignore the temporal dependence of the ranking lists. To address these issues, novel nonparametric static (ROBART) and autoregressive (ARROBART) models are developed, with latent scores defined as…

利用辅助数据界定遗漏变量偏差：邻里效应估计应用AI 解读

This paper develops a method for estimating a superset that contains the linear projection coefficients of an outcome variable y onto a full set of covariates (x,z,w), when the available data are split across two independent sources. One dataset identifies the joint distribution of y and a subset of covariates (z,w), while the other identifies the joint distribution of (x,z). This framework is motivated by applications where administrative data lacks information on…