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未来预期重要时的个体反应估计AI 解读

We propose an approach to estimate how individuals’ expectations influence their responses to a counterfactual change. The approach relies on average partial effects, which recover counterfactual impacts under conditions that we specify. We propose a three-step estimation method that relies on panel data on subjective expectations. We illustrate our approach in a model of consumption and saving, focusing on the impact of an income tax that not only changes current income but…

稀疏网络的拟合优度检验AI 解读

The stochastic block model (SBM) has been widely used to analyze network data. Various goodness-of-fit tests have been proposed to assess the adequacy of model structures. To the best of our knowledge, however, none of the existing approaches are applicable for sparse networks in which the connection probability of any two communities is of order n−1logn, and the number of communities is divergent. To fill this gap, we propose a novel goodness-of-fit test for the stochastic…

MCA：面向条件众数的高维众数成分分析AI 解读

The modal factor model represents a novel statistical framework for dimension reduction in high dimensional panel data analysis. It is specifically designed to capture modal factors which exert a significant influence on the conditional mode of the distribution of the observables. Statistical inference for this model is developed based on a newly proposed modal component analysis, where modal factors and their corresponding loadings are estimated through maximizing a…

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…

含固定效应的门槛空间面板回归AI 解读

We introduce general estimation and inference methods for threshold spatial panel regression with two-way fixed effects in a diminishing-threshold-effects framework. A valid objective function is obtained through a simple adjustment on the concentrated quasi loglikelihood with fixed effects being concentrated out, which leads to a consistent estimation of all common parameters. We show that the estimation of threshold parameter has a negligible effect on the asymptotic…

使用 PyBLP 将微观数据纳入差异化产品需求估计AI 解读

We delineate a general framework for incorporating many types of micro data from summary statistics to full surveys of selected consumers into Berry, Levinsohn, and Pakes (1995)-style estimates of differentiated products demand systems. We extend recommended practices for BLP estimation in Conlon and Gortmaker (2020) to the case with micro data and implement them in our open-source package PyBLP. Monte Carlo experiments and empirical examples suggest that incorporating micro…

状态空间模型的粒子 MCMC 与相关粒子混合采样器AI 解读

Particle Markov Chain Monte Carlo (PMCMC) is a powerful approach to Bayesian computation for general state space models. Our article enables PMCMC to handle a larger number of observations and parameters compared to previous approaches by generating the parameters that are highly correlated with the states in a pseudo-marginal step(s) with the states ‘integrated out’; the rest of the parameters are generated conditional on the states. We make the pseudo-marginal step much…

矩不等式模型推断的实用指南AI 解读

Models defined by moment inequalities have become a standard modeling framework for empirical economists, spreading over a wide range of fields within economics. From the point of view of an empirical researcher, the literature on inference in moment inequality models is large and complex, including multiple survey papers that document the non-standard features these models possess, the main novel concepts behind inference in these models, and the most recent developments…

赌掉稳定：体育博彩对脆弱家庭的影响AI 解读

We estimate the causal effect of online sports betting on households’ investment, spending, and debt management decisions using household transaction data and a staggered difference-in-differences framework. Following legalization, sports betting spreads quickly, with both the number of participants and frequency of bets increasing over time. This increase does not displace other gambling or consumption but significantly reduces savings, as risky bets crowd out positive…

Lévy过程的快速无似然参数估计AI 解读

ABSTRACT Lévy processes are widely used in financial modeling due to their ability to capture discontinuities and heavy tails, which are common in high‐frequency asset return data. However, parameter estimation remains a challenge when associated likelihoods are unavailable or costly to compute. We propose a fast and accurate method for Lévy parameter estimation using the neural Bayes estimation (NBE) framework—a simulation‐based, likelihood‐free approach that leverages…

倾向得分的平滑设定检验AI 解读

ABSTRACT This paper proposes a simple smooth specification test for the propensity score in the spirit of Neyman (1937). We derive a particular restriction on the propensity score and construct the specification test based on it. Using the density‐weighting method, we transform the original specification testing problem into a test of the joint significance of the generalized Fourier coefficients. We propose a novel projection method to eliminate the parameter estimation…

干扰结构设定的条件随机化检验AI 解读

ABSTRACT This study proposes specification tests for interference structure in causal inference with spillovers. We focus on experimental settings in which the treatment assignment mechanism is known. To test whether a given exposure mapping adequately summarizes the true interference structure, we develop conditional randomization tests by utilizing the hierarchical relationship between the null and alternative exposure mappings. When there are multiple candidate exposure…

资源丰富经济体的财政政策状态AI 解读

ABSTRACT We analyse fiscal policy in resource‐rich economies using a novel Bayesian regime‐switching panel model. The identified regimes capture pro‐ or countercyclical fiscal behaviour by allowing regime‐specific shifts in the average fiscal stance, while the switches between the regimes have the interpretation of changes in fiscal policy. Applying the model to a panel of sixteen oil‐producing economies, we show that fiscal policy has alternated between a procyclical and…

欧洲劳动力市场的机会性技能升级：广泛复现证据AI 解读

ABSTRACT We build on Modestino et al., who analysed “opportunistic upskilling” during the 2007 recession in the US, by extending their approach to European labour markets following recent economic shocks. Using a comprehensive dataset of online job postings (2019–2023) and an instrumental variable approach leveraging Ukrainian refugee inflows, we find that increases in labour supply have a significant positive effect on both education and experience requirements. Our…

政策评估中合成控制的正则化AI 解读

ABSTRACT We propose a unified framework for interpreting and comparing a broad class of synthetic control (SC) methods. Our framework is built on an analysis of a mean‐squared prediction error (MSPE) bound for the counterfactual predicted by a generic SC method, without imposing a specific outcome model. Using this framework, we develop a generalized SC method that provides a more comprehensive regularization of the MSPE bound than several existing SC methods. Through…

三重稳健面板估计量AI 解读

ABSTRACT This paper studies estimation of causal effects in a panel data setting. We introduce a new estimator, the Triply Robust Panel (TROP) estimator, that combines a flexible model for the potential outcomes based on a low‐rank factor structure on top of a two‐way‐fixed effect specification, with unit weights intended to upweight units similar to the treated units and time weights intended to upweight time periods close to the treated time periods. We study the…

混合采样频率异质面板模型的估计AI 解读

ABSTRACT This paper studies heterogeneous mixed‐frequency panel data models, focusing on linear specifications that allow heterogeneity in both aggregation weights and slope coefficients. To address potential correlations between the heterogeneity and the covariates, we first show that, under additional normalization conditions, the mean‐group estimator delivers consistent and asymptotically normal slope estimates but biased weights estimators. As an alternative, we propose…

双重广义线性模型：似然与贝叶斯方法AI 解读

Edilberto Cepeda-Cuervo. Boca Raton, FL: Chapman & Hall/CRC Press, 2026, xiii + 146 pp., $159.99(e-book), $66.99(H), ISBN: 978-1-003-68741-2(e-book), ISBN: 978-1-041-16997-0(H).In generalized linea...

基于共形化闭合检验的集体异常检测与计数AI 解读

This paper develops a flexible distribution-free method for collective outlier detection and enumeration, designed for situations in which the presence of outliers can be detected powerfully even though their precise identification may be challenging due to the sparsity, weakness, or elusiveness of their signals. This method builds upon recent developments in conformal inference and integrates classical ideas from other areas, including multiple testing, locally most…

Beta 树：具有置信陈述的多元直方图AI 解读

Multivariate histograms are difficult to construct due to the curse of dimensionality. Motivated by k-d trees in computer science, we show how to construct an efficient data-adaptive partition of Euclidean space that possesses the following two properties: With high confidence the distribution from which the data are generated is close to uniform on each rectangle of the partition; and despite the data-dependent construction we can give guaranteed finite sample simultaneous…

近似邻域干扰下的辅助变量调整AI 解读

Randomized experiments are the gold standard for causal inference. However, traditional assumptions, such as the Stable Unit Treatment Value Assumption (SUTVA), often fail in real-world settings where interference between units is present. Network interference, in particular, has garnered significant attention. Structural models, like the linear-in-means model, are commonly used to describe interference, but they rely on the correct specification of the model, which can be…

两项双层随机前沿研究的复现AI 解读

ABSTRACT We replicate the two foundational studies which proposed the two‐tier stochastic frontier model, first in the cross‐sectional setting and then for the fixed effects panel data model. We explore matters of software then and now, different mathematical formulations of the likelihood, and alternative current software tools. We also highlight several data issues that arose in both replications.

协方差矩阵估计中的迁移学习：最优性与自适应性AI 解读

Transfer learning, which leverages auxiliary data to improve inference in a target domain, has become a powerful tool in machine learning. In this work, we study minimax and adaptive estimation of large bandable covariance matrices under a transfer learning framework.We first establish the minimax rate of convergence under squared spectral norm loss and propose a rate-optimal estimator. Our analysis reveals phase transitions determined by the source-target disparity, the…

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…