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多维匹配的半非参数模型：最优传输方法AI 解读

This paper develops a set of empirically tractable and flexible sieve estimators for semi-nonparametric multidimensional matching models with transferable utility, focusing on worker-job matching. We generalize the parametric quadratic-Gaussian framework employed by Bojilov and Galichon (2016) and Lindenlaub (2017), which relies on joint normality of observed characteristics. We allow unrestricted distributions of characteristics and show identification of the production…

非参数二元选择模型的核化估计方法AI 解读

We propose a new estimator for nonparametric binary choice models that does not impose a parametric structure on either the systematic function of covariates or the distribution of the error term. A key advantage of our approach is its computational scalability in the number of covariates. For instance, even when assuming a normal error distribution as in probit models, commonly used sieves for approximating an unknown function of covariates can lead to a large-dimensional…

通过有限跨观测信息汇集界定处理效应AI 解读

We provide novel bounds on average treatment effects (on the treated) that are valid under an unconfoundedness assumption. Our bounds are designed to be robust in challenging situations, for example, when the conditioning variables take on a large number of different values in the observed sample, or when the overlap condition is violated. This robustness is achieved by only using limited "pooling" of information across observations. Namely, the bounds are constructed as…

工具变量回归中弱工具稳健的子向量推断：拉格朗日乘子检验与 Anderson–Rubin 置信集AI 解读

We propose a weak-instrument-robust subvector Lagrange multiplier test for instrumental variables regression. We show that it is asymptotically size-correct under a technical condition. This is the first weak-instrument-robust subvector test for instrumental variables regression to recover the degrees of freedom of the commonly used non-weak-instrument-robust Wald test. Additionally, we provide a closed-form solution for subvector confidence sets obtained by inverting the…

具有定向作用的工具变量与处理效应AI 解读

Multivalued treatments are commonplace in applications. We explore the use of discrete-valued instruments to control for selection bias in this setting. Our discussion revolves around the concept of targeting: which instruments target which treatments. It allows us to establish conditions under which counterfactual averages and treatment effects are point- or partially-identified for composite complier groups. We explore the additional identifying power of a positive…

集中化 MCMC 估计AI 解读

This paper studies a concentrated Markov Chain Monte Carlo (MCMC) estimation method that offers significant computational advantages while delivering valid inference for parameters of interest. By reducing the dimension of parameters to be sampled, this method substantially decreases computational time and memory usage relative to both traditional and some state-of-the-art Bayesian algorithms. Its applicability extends to correctly specified or misspecified likelihood…

离散数据联立方程模型的似然设定AI 解读

In this article, we examine the foundations of a rich and diverse literature in economics and derive the likelihood function of simultaneous equation models for discrete data as the invariant distribution of a suitably specified Markov process. This formulation offers a well-defined reduced form of the model and dispenses with the need for controversial recursivity requirements and ad hoc indeterminacy rules. The derivation resolves puzzling paradoxes highlighted in earlier…

含持续与暂时无效率的半参数随机前沿模型贝叶斯估计AI 解读

We propose kernel estimators for the densities of persistent and transient inefficiencies in a generalized panel data stochastic frontier model. This approach relaxes the conventional distributional assumptions imposed on the inefficiencies, thereby increasing robustness. A Bayesian sampling algorithm is developed to estimate all model parameters and unobserved entities, as well as the bandwidths needed to construct the densities of persistent and transient inefficiencies.…

气候计量中的贝叶斯时空变系数建模：时空高斯过程方法AI 解读

Quantifying the economic impacts of weather variables has been a key empirical topic in understanding the impacts of climate change. While panel methods, which utilize cross-sectional differences in within-unit over-time changes in weather variables, have been widely used to credibly identify the average effect of interest, there has been a growing interest in understanding the distribution of potentially heterogeneous weather effects. The heterogeneity of the effects across…

序数 Probit 模型的半参数贝叶斯估计：跨国、年龄与性别的生活满意度AI 解读

We employ a novel semiparametric Bayesian ordinal probit model to re-examine the relationships between age and life satisfaction (“happiness”) across countries and gender. Within our ordinal choice framework we introduce a new scheme for cutpoint simulation and develop a locally adaptive method for smoothing an otherwise erratic collection of age dummy variable coefficients. We find strong evidence that employment status is deeply intertwined with the reported happiness of…

金融素养与金融犯罪：回归不连续设计AI 解读

This study investigates how financial literacy shapes the propensity of individuals to commit financial crime. Using state-level administrative data on criminal charges linked to comprehensive public records , we exploit a policy-based discontinuity in grade level assignment based on individual birth dates that exogenously requires certain high school cohorts to attend a financial literacy course. Our estimates suggest that exposure to the course reduces the propensity to…

估计分位数最优处理规则的序贯分类学习AI 解读

Quantile optimal treatment regimes (OTRs) aim to assign treatments that maximize a specified quantile of patients’ outcomes. Compared to treatment regimes that target the mean outcomes, quantile OTRs offer fairer regimes when a lower quantile is selected, as it improves outcomes for vulnerable patients. In this paper, we propose a novel method for estimating quantile OTRs by reformulating the problem as a successive classification task, solvable via training a sequence of…

存在隐藏混杂时过参数化模型的在线推断AI 解读

Abstract In this paper, we study online estimation and inference of regression coefficients in the presence of hidden confounders by leveraging over-parameterized models. Unlike existing offline approaches that rely on factor and sparse models, our closed-form estimator simultaneously removes hidden-confounder bias and is directly applicable to streaming data. Using tools from random matrix theory, we analyse phase transition phenomena in the variance of the coefficient…

混杂变量非可忽略缺失下中介分析的有效非参数推断AI 解读

Mediation analysis is widely used for exploring treatment mechanisms; however, it faces challenges when nonignorable missing confounders are present. Efficient inference of mediation effects and the efficiency loss due to nonignorable missingness have been rarely studied in the literature because of the difficulties arising from the ill-posed inverse problem. In this paper, we propose a general shadow variable framework for identifying mediation effects, allowing shadow…

通过预测分配实现超大规模网络社区检测AI 解读

Massive network datasets are becoming increasingly common in scientific applications. Existing community detection methods encounter significant computational challenges for such massive networks due to two reasons. First, the full network needs to be stored and analyzed on a single server, leading to high memory costs. Second, existing methods typically use matrix factorization or iterative optimization using the full network, resulting in high runtimes. We propose a…

利用特殊回归变量识别第一类与第二类错误概率：司法误判应用AI 解读

We nonparametrically identify Type I and Type II error rates when mistakes are unobserved. Our strategy reframes the setting as a misclassified binary choice model, using a special regressor to identify the misclassification probabilities. Identification requires an exclusion restriction and a large support condition. When the exclusion restriction is in doubt, we show how our estimands can be interpreted as bounds under a weaker monotonicity condition. Bounds are also…

利用双重机器学习估计直接与间接分位数处理效应AI 解读

We propose a framework to disentangle the quantile treatment effect of a binary treatment at a specific rank into an indirect quantile treatment effect that operates through a mediator and an unmediated direct quantile treatment effect. We establish identification results for these effects under the sequential ignorability assumption and propose double/debiased machine learning estimators, based on the efficient influence functions of the cumulative distribution functions of…

未观测混杂下预测算法的稳健设计与评估AI 解读

Abstract Predictive algorithms inform consequential decisions in settings with selective labels: outcomes are observed only for units selected by past decision makers. This creates an identification problem under unobserved confounding — when selected and unselected units differ in unobserved ways that affect outcomes. We propose a framework for robust design and evaluation of predictive algorithms that bounds how much outcomes may differ between selected and unselected…

作为组织问题的国家能力AI 解读

Abstract We investigate how technologies that reduce the costs of monitoring by central authorities have shaped the historical transition from small patrimonial states to large bureaucratic organizations. Our analysis is based on a novel dataset that traces changes in the organizational structure and geographic presence of the U.S. federal government over the 19th century. To identify causal effects, we develop a new identification strategy that exploits the expansion of the…

带函数型协变量的随机区块模型：变分非参数推断AI 解读

We propose a functional stochastic block model whose vertices involve functional data information. This new model extends the classic stochastic block model with vector-valued nodal information, and finds applications in real-world networks whose nodal information could be functional curves. Examples include international trade data in which a network vertex (country) is associated with the annual or quarterly GDP over a certain time period, and MyFitnessPal data in which a…

含议价机制双向拍卖的非参数识别与估计AI 解读

This article studies the nonparametric identification and estimation of double auctions with one buyer and one seller. This model assumes that both bidders submit their own sealed bids, and the transaction price is determined by a weighted average between the submitted bids when the buyer’s offer is higher than the seller’s ask. It captures the bargaining process between two parties. Working within this double auction model, we first establish the nonparametric…

先学习再决策：数据市场设计的学习方法AI 解读

As data marketplaces become increasingly central to the digital economy, it is crucial to design efficient pricing mechanisms that optimize revenue while ensuring fair and adaptive pricing. We introduce the Maximum Auction-to-Posted Price (MAPP) mechanism, a novel two-stage approach that first estimates the bidders’ value distribution through auctions and then determines the optimal posted price based on the learned distribution. We establish that MAPP is individually…

Abstract Adaptive experiments use preliminary analyses of the data to inform further course of action and are commonly used in many disciplines including medical and social sciences. Because the null hypothesis and experimental design are data-dependent, it has long been recognized that statistical inference for adaptive experiments is not straightforward. Most existing methods only apply to specific adaptive designs and rely on strong assumptions. In this work, we propose…

线性模型中利用异方差进行稳健推断AI 解读

Summary We study inference via heteroskedasticity in linear models commonly used for macroeconomic policy analysis, where covariate endogeneity must often be addressed with limited time and data. Our framework nests standard heteroskedasticity-based approaches, allows for new non-nested restrictions, and does not require ex ante regime labelling. We propose an easily implementable weak identification robust test and derive sufficient conditions for its validity. Simulation…