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短时间维度异质性面板中的平均效应估计AI 解读

The commonly used two-way fixed effects estimator is biased under correlated heterogeneity and can lead to misleading inference. The mean group estimator proposed by \cite{PesaranSmith1995} is robust to correlated heterogeneity but requires the individual estimators to have second-order moments that could fail if the number of estimated coefficients ($k$) is close to the time dimension ($T$) of the panel. This paper focuses on panels where $T-k$ is mall (including $T=k$),…

共同相关效应动态面板模型的自助法推断AI 解读

We study recursive-design wild bootstrap inference for dynamic panel data models with unobserved common factors estimated by Common Correlated Effects. In the large N,T setting, the bootstrap reproduces the biased limiting distribution in pure autoregressive models, but fails to capture all bias and factor-estimation variance components in models with additional regressors, particularly under weak exogeneity. We trace this failure to holding regressors fixed across bootstrap…

交互效应异质性空间动态面板模型的IV估计AI 解读

This paper develops a Mean Group Instrumental Variables (MGIV) approach for spatial dynamic panel data models with interactive effects, under large N and T asymptotics. Unlike existing approaches that typically impose slope-parameter homogeneity, MGIV accommodates cross-sectional heterogeneity in slope coefficients. The proposed estimator is linear, making it computationally efficient and robust. Furthermore, it enables asymptotically valid inferences without requiring bias…

高维交互固定效应下的双重机器学习AI 解读

Factor structures are central to empirical work in economics and finance, and are usually used to model time-varying unobserved heterogeneity through interactive fixed effects (IFE). Existing IFE estimators rest on low-dimensional and linear specifications in the covariates, assumptions which are increasingly restrictive in applications drawing on rich datasets with controls of unknown functional form. This paper develops a Double Machine Learning estimator for the…

具有潜在结构的社会互动模型AI 解读

This paper studies estimation and inference of heterogeneous social interactions featuring group fixed effects and slope heterogeneity under latent structure. Theoretically, in a binary choice model with social interactions we characterize the asymptotic bias of the slope coefficient due to the presence of high dimensional incidental parameters. Computationally, we invoke parametric bootstrap method to debias and establish asymptotic validity. Furthermore, tailored…

动态异质分布回归面板模型及劳动收入应用AI 解读

We introduce a dynamic distribution regression panel data model with heterogeneous coefficients across units. The objects of primary interest are functionals of these coefficients, including predicted one-step-ahead and stationary cross-sectional distributions of the outcome variable. Coefficients and their functionals are estimated via fixed effect methods. We investigate how these functionals vary in response to counterfactual changes in initial conditions or covariate…

具有非线性因子结构的面板数据模型估计AI 解读

Panel data models with unobserved heterogeneity in the form of interactive effects standardly assume that the time effects -- or ``common factors'' -- enter linearly. This assumption is restrictive because it concerns an unobserved component of the model, for which a particular functional form is rarely justified. By contrast, linearity in the observable regressors can often be motivated by economic theory or empirical convention. Linearity in the factors has mainly…

异质系数面板模型中的经验贝叶斯估计AI 解读

We develop an empirical Bayes (EB) G-modeling framework for short-panel linear models with nonparametric prior for the random intercepts, slopes, dynamics, and non-spherical error variances. We establish identification and consistency of the nonparametric maximum likelihood estimator (NPMLE) under general conditions, and provide low-level sufficient conditions for several models of empirical interest. Conditions for regret consistency of the EB estimators are also…

追逐机会：美国州人口增长的溢出效应与驱动因素AI 解读

We study the drivers and spatial diffusion of U.S. state population growth using a dynamic spatial model for 49 states, 1965-2017. Methodologically, we recover the spatial network structure from the data, rather than imposing it a priori via contiguity or distance, and combine this with an IV estimator that permits heterogeneous slopes and interactive fixed effects. This unified design delivers consistent estimation and inference in a flexible spatial panel model with…

固定时间维度下异质性面板模型的双向均值组估计AI 解读

We consider a correlated random coefficient panel data model with two-way fixed effects and interactive fixed effects in a fixed T framework. We propose a two-way mean group (TW-MG) estimator for the expected value of the slope coefficient and propose a leave-one-out jackknife method for valid inference. We also consider a pooled estimator and provide a Hausman-type test for poolability. Simulations demonstrate the excellent performance of our estimators and inference…

含交互固定效应的高维面板数据模型：估计与推断AI 解读

We develop new econometric methods for estimation and inference in high-dimensional panel data models with interactive fixed effects. Our approach can be regarded as a non-trivial extension of the very popular common correlated effects (CCE) approach. Roughly speaking, we proceed as follows: We first construct a projection device to eliminate the unobserved factors from the model by applying a dimensionality reduction transform to the matrix of cross-sectionally averaged…

面板数据估计与推断：同质性与异质性AI 解读

In this paper, we define an underlying data generating process that allows for different magnitudes of cross-sectional dependence, along with time series autocorrelation. This is achieved via high-dimensional moving average processes of infinite order (HDMA($\infty$)). Our setup and investigation integrates and enhances homogenous and heterogeneous panel data estimation and testing in a unified way. To study HDMA($\infty$), we extend the Beveridge-Nelson decomposition to a…

异质性面板模型的协变量调整深度因果学习AI 解读

This paper studies the task of estimating heterogeneous treatment effects in causal panel data models, in the presence of covariate effects. We propose a novel Covariate-Adjusted Deep Causal Learning (CoDEAL) for panel data models, that employs flexible model structures and powerful neural network architectures to cohesively deal with the underlying heterogeneity and nonlinearity of both panel units and covariate effects. The proposed CoDEAL integrates nonlinear covariate…

短时间维度面板模型中的异质自回归AI 解读

This paper considers a first-order autoregressive panel data model with individual-specific effects and heterogeneous autoregressive coefficients defined on the interval (-1,1], thus allowing for some of the individual processes to have unit roots. It proposes estimators for the moments of the cross-sectional distribution of the autoregressive (AR) coefficients, assuming a random coefficient model for the autoregressive coefficients without imposing any restrictions on the…

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…

非线性面板数据模型的共同相关效应估计AI 解读

This paper focuses on estimating the coefficients and average partial effects of observed regressors in nonlinear panel data models with interactive fixed effects, using the common correlated effects (CCE) framework. The proposed two-step estimation method involves applying principal component analysis to estimate latent factors based on cross-sectional averages of the regressors in the first step, and jointly estimating the coefficients of the regressors and factor loadings…

弱依赖下高维动态面板异质处理效应的估计与推断AI 解读

This paper provides estimation and inference methods for a conditional average treatment effects (CATE) characterized by a high-dimensional parameter in both homogeneous cross-sectional and unit-heterogeneous dynamic panel data settings. In our leading example, we model CATE by interacting the base treatment variable with explanatory variables. The first step of our procedure is orthogonalization, where we partial out the controls and unit effects from the outcome and the…

低秩面板分位数回归：估计与推断AI 解读

In this paper, we propose a class of low-rank panel quantile regression models which allow for unobserved slope heterogeneity over both individuals and time. We estimate the heterogeneous intercept and slope matrices via nuclear norm regularization followed by sample splitting, row- and column-wise quantile regressions and debiasing. We show that the estimators of the factors and factor loadings associated with the intercept and slope matrices are asymptotically normally…

共同相关效应模型的简便替代方法AI 解读

We study estimation of factor models in a fixed-T panel data setting and significantly relax the common correlated effects (CCE) assumptions pioneered by Pesaran (2006) and used in dozens of papers since. In the simplest case, we model the unobserved factors as functions of the cross-sectional averages of the explanatory variables and show that this is implied by Pesaran's assumptions when the number of factors does not exceed the number of explanatory variables. Our…

二维异质面板数据模型的双向整合分析AI 解读

Heterogeneous panel data models that allow the coefficients to vary across individuals and/or change over time have received increasingly more attention in statistics and econometrics. This paper proposes a two-dimensional heterogeneous panel regression model that incorporate a group structure of individual heterogeneous effects with cohort formation for their time-variations, which allows common coefficients between nonadjacent time points. A bi-integrative procedure that…

利用因子斜率的低秩估计推断异质效应AI 解读

We study a panel data model with general heterogeneous effects where slopes are allowed to vary across both individuals and over time. The key dimension reduction assumption we employ is that the heterogeneous slopes can be expressed as having a factor structure so that the high-dimensional slope matrix is low-rank and can thus be estimated using low-rank regularized regression. We provide a simple multi-step estimation procedure for the heterogeneous effects. The procedure…