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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…

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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半参数广义帕累托模型中的尾部期望分位数估计AI 解读

Expectiles have received increasing attention as a market risk measure that is both coherent and elicitable. They are defined as a least squares analog to quantiles. Their estimation from heavy-tailed loss data in an extreme value framework is an important and difficult problem, especially when the target tail expectile is beyond the range of the data. This problem has been studied only fairly recently, using solely the Weissman extrapolation method. The competing…

异质性主体下的极值理论AI 解读

Extreme value processes feature in any economic model in which agents receive a number of draws from some distribution and we examine the behavior of the maximum in the limit as the number of draws becomes large. This paper asks: Do the average outcomes of such processes change when different agents receive a different number of draws? To answer this, we allow the number of draws an agent receives from the underlying distribution (e.g. of productivities, ideas, or utility…

最富者为何如此富有？新的纵向经验证据及其对财富不平等理论的启示AI 解读

We study the lifecycle dynamics of wealth inequality using 1993-2019 Norwegian administrative panel data on wealth and income. Employing a novel budget-constraint approach, we decompose the excess wealth of the top 0.1% households relative to the median between ages 45 and 64 into higher saving rates (36%), inheritances (31%), returns (28%), and labor income (5%). One-quarter of the wealthiest—the “New Money”—start with negative wealth on average but accumulate rapidly…

农业政策干预的规模化效应AI 解读

Policies to raise agricultural productivity have been central in the fight against global poverty. Their impacts are often measured in experiments that provide strong causal identification but may be too small-scale to capture general equilibrium effects that arise when policies are scaled up. We develop a quantitative model of agricultural trade, featuring a granular economic geography, that combines parameters estimated from small-scale experiments with rich administrative…

分解加成率的上升AI 解读

A recent literature argues that the rise in aggregate markups is due to economic activity reallocating toward high-markup firms rather than an increase in firmlevel markups. I show that standard decomposition methods used to analyze this phenomenon can estimate a non-zero reallocation effect even when the joint distribution of markups and market shares is time-invariant. This is due to churning in the firm-level distribution of markups and market shares. Empirically, I find…

利用成交价格进行有限样本拍卖推断AI 解读

We first provide inference on the quantile function of the bid distribution in firstprice or descending auctions, and the quantile function of the value distribution in second-price or ascending auctions, with a known number of bidders with symmetric independent private values. Specifically, we provide finite-sample, nonparametric, tuning parameter-free, uniform confidence bands for these quantile functions when observing only the transaction price. We also show how these…

条件影响函数AI 解读

There are many nonparametric objects of interest that are functionals of a conditional distribution, such as average treatment effects conditional on a low-dimensional set of covariates. We define a conditional influence function (CIF) as the Gateaux derivative of a conditional parameter with respect to perturbations of the conditional law of a single observation given the conditioning variable. CIFs generalize classical influence functions and can be used to (i) construct…

劳动者侧歧视：信念与偏好AI 解读

When agents choose among options with unobserved attributes to maximize expected utility, their choices conflate preferences with beliefs about those attributes. We develop a novel theory-driven identification strategy for such settings, showing that compensating differentials separately identify beliefs from preferences. Noting that workers’ preferences and beliefs shape labor market outcomes, yet remain understudied, we apply our method to provide the first evidence on the…

样本选择下观测数据中总体平均与分位处理效应的部分识别AI 解读

ABSTRACT This article partially identifies population treatment effects in observational data under both non‐random treatment assignment and sample selection. Bounds are provided for both average and quantile population treatment effects, combining assumptions for the selected and the non‐selected subsamples. We show how different assumptions help narrow identification regions, and we illustrate our methods by partially identifying the effect of maternal education on the…

用分布回归双重差分评估最低工资引入对工资、收入与工时分布的影响AI 解读

ABSTRACT We estimate the causal effects of the introduction of the minimum wage in Germany on the distributions of hourly wages, monthly earnings, and hours worked, combining rich large‐scale data sources. We compare linear and non‐linear versions of distribution regression difference‐in‐differences (DR‐DiD) and also estimate bivariate DR‐DiD. We show that the introduction of the minimum wage in 2015 led to shifts in low hourly wages around the minimum wage threshold…

矩阵分位数因子模型AI 解读

This paper introduces a matrix quantile factor model for matrix-valued data with low-rank structure. We estimate the row and column factor spaces via minimizing the empirical check loss function with orthogonal rotation constraints. We show that the estimates converge at rate (min{p1p2,p2T,p1T})−1/2 in the average Frobenius norm, where p1, p2 and T are the row dimensionality, column dimensionality and length of the matrix sequence, respectively. This rate is faster than that…

高维干扰参数下分位数回归高维效应的去相关检验AI 解读

Quantile regression provides a flexible framework in econometrics for modeling the relationship between a response variable and multivariate predictors, particularly when heterogeneous effects are present. This paper addresses the challenge of testing high-dimensional coefficients in quantile regression in the presence of high-dimensional nuisance parameters. We first extend a recently proposed score test by incorporating a convolution-type smoothed quantile loss, which…

存在少报的有序选择模型中的变化中的变化AI 解读

We develop a Difference-in-Differences framework for discrete, ordered outcomes subject to underreporting. Such outcomes commonly arise in self-reported surveys on socially undesirable or stigmatized behaviors, where respondents may conceal their true behavior. For a discrete Changes-in-Changes model that is shown to admit an equivalent threshold-crossing representation, we derive nonparametric bounds for the counterfactual and factual outcome distributions as well as for…

基于鞅后验的贝叶斯分位数估计与回归AI 解读

Abstract Quantile estimation and regression within the Bayesian framework is challenging as the choice of likelihood and prior is not obvious. In this paper, we introduce a novel Bayesian nonparametric method for quantile estimation and regression based on the recently introduced martingale posterior (MP) framework. The core idea of the MP is that posterior sampling is equivalent to predictive imputation, which allows us to break free of the stringent likelihood-prior…

分位数网络向量自回归中的双向同质性分组AI 解读

We propose a two-way grouped network quantile (TGNQ) autoregression model for time series data observed on networks with substantial heterogeneity and complex directional interactions. Motivated by prior studies on network effects in directed networks, our model assigns each node two latent group memberships to flexibly capture asymmetric and heterogeneous interactions among users. These memberships, along with model parameters, can be consistently estimated using the…

测地线双重差分AI 解读

Summary Difference-in-differences (DID) is a widely used quasi-experimental design for causal inference, traditionally applied to scalar or Euclidean outcomes, while extensions to outcomes residing in non-Euclidean spaces remain limited. Existing methods for such outcomes have primarily focused on univariate distributions, leveraging linear operations in the space of quantile functions, but these approaches cannot be directly extended to outcomes in general metric spaces. In…

高维分位数回归的无模型特征筛选与错误发现控制AI 解读

High-dimensional quantile regressions are powerful tools for capturing the diverse relationships between predictors and response variables across different quantile levels. However, existing feature screening methods for quantile regressions often rely on restrictive model assumptions, limiting their applicability in complex data settings. In this paper, we propose a novel model-free feature screening framework for high-dimensional quantile regressions, leveraging a novel…

STRAP：利用随机投影的多元同质性平滑检验AI 解读

In this paper, we propose STRAP (Smooth Tests using RAdom Projections), projected smooth tests for the equality of two multivariate distributions. We employ random projections to map the multivariate data into one-dimensional data and transform the projected data via the probability integral transformation. Under the null hypothesis of homogeneity, the transformed data follow the standard uniform distribution, reducing the problem to testing for uniformity. Building on…

含内生性的删失部分线性分位数回归AI 解读

This paper considers the estimation of a censored partial linear quantile regression model with endogeneity. Our method extends those for partial linear quantile regression (Lee, 2003, Qu et al. 2024), nonparametric quantile regression (Qu and Yoon, 2015, Belloni et al., 2019), and censored linear quantile regression with endogeneity (Chen, 2018). Our estimation method involves a tractable sequential-multiple-step procedure. We establish the uniform consistency, the uniform…

含治愈比例的删失数据分位数回归AI 解读

Quantile regression is emerging as a valuable statistical tool for exploring complete information on the exposure-outcome relationship. This paper investigates a novel inference procedure for quantile regression models to analyze survival data with a cure fraction. A two-component mixture cure model is used, in which the cure fraction follows a logistic model and the uncured individuals follow a linear quantile regression model. Our main interest lies in the estimation of…

全局非正曲率空间上的分位数AI 解读

Abstract We propose a notion of geometric quantiles on Hadamard spaces, or global non-positive curvature spaces. After providing some definitions and basic properties, including scaled isometry equivariance and a necessary condition on the gradient of the quantile loss function, we investigate asymptotic properties, such as strong consistency and joint asymptotic normality. We provide a detailed description of how to compute quantiles using a gradient descent algorithm in…

近历元依赖重尾时间序列的极端局部线性分位数回归AI 解读

This paper develops a data-driven inference procedure for extreme analysis of data with near epoch dependence (NED), a condition less restrictive than traditional dependence structures like α-mixing, making it particularly useful for analyzing heavy-tailed time series data. To capture nonlinear data structures, we propose a new framework that combines local linear quantile regression with extreme value theory. We first study the asymptotic properties of the local-linear…