fixed-effects estimation

This study addresses the incidental parameter problem arising from unit-specific fixed effects in nonlinear panel data models, particularly when the number of time periods per unit is small and conventional estimators break down. The authors propose a novel projection-based approach that eliminates these incidental parameters without imposing assumptions on the joint distribution of fixed effects and covariates. By constructing an identified set through an implementable correspondence between observables and unobserved heterogeneity, and leveraging random set theory together with moment inequalities, they develop a distribution-free partial identification framework. This framework accommodates both static and dynamic models as well as discrete and continuous outcomes, enabling robust inference even in short panels.

研究在动态选择下处理时变协变量和潜在异质性对因果推断的影响，通过预处理结果历史识别相似个体，并提出基于核的双重稳健估计方法来解决动态平均处理效应的识别问题。

This paper addresses the identification and estimation of dynamic random-coefficient linear models with individual heterogeneity in short panel data. Due to predetermined regressors—such as lagged dependent variables—point identification is infeasible under conventional approaches. We therefore propose a semiparametric identification framework grounded in moment inequalities and distributional constraints, which—novelty—systematically characterizes the non-point-identified sets for the mean, variance, and cumulative distribution function of the random coefficients, accommodating discrete, continuous, and unbounded outcomes. We further develop a computationally tractable estimation and inference procedure, applying it to PSID data. Empirically, we find substantial unobserved heterogeneity in U.S. household income persistence; this heterogeneity constitutes a key structural driver of divergent consumption and saving behaviors across households.

This paper addresses the dual heterogeneity in panel data—cross-sectional latent group structures (homogeneous within groups, heterogeneous across groups) and smoothly evolving time-varying coefficients. We propose a time-varying latent group panel model that jointly captures both features. Methodologically, we innovatively integrate adaptive pairwise grouping fusion Lasso—enabling automatic group identification—with polynomial or B-spline bases to flexibly model coefficient trajectories over time, thereby unifying latent grouping and smooth temporal variation for the first time. Theoretically, we establish asymptotic normality and oracle efficiency for both the penalized and post-selection estimators. Simulation studies demonstrate high grouping accuracy and low estimation bias. Empirical application to global GDP carbon intensity reveals significant cross-country latent grouping and time-varying convergence patterns, confirming the method’s statistical robustness and substantive interpretability.

This paper addresses the challenge of dynamic forecasting and steady-state distribution inference in panel data with cross-sectional heterogeneity in unit-specific coefficients. We propose a dynamic heterogeneous distribution regression framework that jointly estimates individual-level heterogeneous coefficients and their functional targets—including one-step-ahead forecasts, steady-state cross-sectional distributions, and quantile treatment effects. To enable uniform asymptotically valid inference on functional parameters under unknown heterogeneity, we develop a novel cross-sectional bootstrap procedure—the first of its kind for such settings. The method integrates fixed-effects estimation, distribution regression, and quantile treatment effect modeling. Empirical application to PSID data reveals that negative income shocks significantly increase right-skewness in labor income distributions and raise poverty persistence rates, while higher education mitigates these effects; moreover, income mobility exhibits systematic heterogeneity across individuals. Simulation studies confirm the method’s robustness and reliability.

This study addresses the challenge of identifying causal effects in nonseparable models when unobserved time-varying individual heterogeneity is correlated with explanatory variables. The authors propose a novel approach that approximates the conditional average potential outcomes using linear sieve methods, combined with individual-specific ridge regression and bias correction. Within a large-T asymptotic framework, this method achieves point identification, circumventing the partial identification issues common in traditional approaches. The resulting estimator admits an empirical Bayes interpretation, accommodates discrete treatment variables, and provides a unified framework for estimating average causal effects, counterfactual consumer welfare measures, and individual tax elasticities. An empirical application to supermarket scanner data demonstrates the method’s effectiveness by precisely quantifying the average equivalent variation and deadweight loss induced by price increases.

This study addresses the challenge of conducting valid statistical inference on unit-specific coefficients in panel data exhibiting latent group structure. The authors propose a novel inference framework that first clusters units into a small number of latent groups and then explicitly accounts for uncertainty in group membership. Their approach involves two key components: constructing test statistics based on the minimal value over confidence sets for group assignments, and correcting for bias induced by potential group misclassification while developing standard errors robust to such misclassification. Theoretical analysis and simulation results demonstrate that, compared to conventional unit-by-unit time series methods, the proposed procedure yields substantially narrower confidence sets—particularly for units with high error variance—while maintaining proper size control and coverage accuracy, thereby avoiding inferential distortions caused by ignoring group assignment uncertainty.

Longitudinal data often exhibit multiple sources of heterogeneity, including divergent mean trajectories, increasing residual variance over time, and occasional outlying measurements. Conventional homogeneous models may yield inefficient parameter estimates and inflated variance assessments in such settings. This work proposes a novel Bayesian mixture model that, for the first time, incorporates covariate-driven binary indicator variables within a unified Bayesian framework to jointly model these three forms of heterogeneity via logistic regression. Inference is carried out using Markov chain Monte Carlo (MCMC) methods, and the approach facilitates posterior-probability-based model selection to evaluate the necessity of each heterogeneous component. Simulation studies demonstrate that the proposed method accurately identifies underlying heterogeneity structures and yields efficient fixed-effect estimates. Its practical utility is further corroborated through application to DHEAS hormone data from the Study of Women’s Health Across the Nation (SWAN).

本文研究了在固定且少量时间周期下，线性分位数面板模型中无限制个体异质性的识别问题，通过严格的外生性条件解决。

本文研究了具有个体特异性系数和灵活误差结构的动态面板数据模型，提出了一种基于逆Radon变换的多步估计方法以解决随机系数分布及误差密度估计问题。