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含交互固定效应的异质动态面板模型：Oracle 有效估计AI 解读

We propose a two-step procedure to estimate a heterogeneous dynamic panel data model with interactive fixed effects. We establish the asymptotic properties of the estimators and show that the final estimator is oracle efficient. We also propose a specification test for the null hypothesis of homogeneous slopes and study the asymptotic properties of the test statistic under both local and global alternatives. Simulations demonstrate the fine performance of the estimator and…

共同因子生成横截面依赖的高斯面板中渐近一致最强的单位根检验AI 解读

This paper considers testing for unit roots in Gaussian panels with cross-sectional dependence generated by common factors. Within our setup, we can analyze restricted versions of the two prevalent approaches in the literature, that of Moon and Perron (2004, Journal of Econometrics 122, 81–126), who specify a factor model for the innovations, and the PANIC setup proposed in Bai and Ng (2004, Econometrica 72, 1127–1177), who test common factors and idiosyncratic deviations…

非随机缺失下的矩阵补全及其在因果面板模型中的应用AI 解读

This paper develops an inferential framework for matrix completion when missing is not at random and without the requirement of strong signals. Our development is based on the observation that if the number of missing entries is small enough compared to the panel size, then they can be estimated well even when missing is not at random. Taking advantage of this fact, we divide the missing entries into smaller groups and estimate each group via nuclear norm regularization. In…

时间效应存在序列相关时的双向聚类标准误AI 解读

Abstract We propose improved standard errors and an asymptotic theory for two-way clustered panels. Our theory allow for arbitrary serial dependence in the common time effects, which is excluded by existing two-way methods. Our asymptotic distribution theory is the first which allows for this level of inter-dependence. Under weak conditions, we demonstrate that OLS is asymptotically normal, our proposed variance estimator is consistent, and t-ratios are asymptotically…

固定效应与广义 Mundlak 估计量AI 解读

Abstract We develop a new approach for estimating average treatment effects in observational studies with unobserved group-level heterogeneity. We consider a general model with group-level unconfoundedness and provide conditions under which aggregate balancing statistics—group-level averages of functions of treatments and covariates—are sufficient to eliminate differences between groups. Building on these results, we re-interpret commonly used linear fixed-effect regression…

目标主成分分析：高维面板数据的迁移学习AI 解读

This paper develops a novel method to estimate a latent factor model for a large target panel with missing observations by optimally using the information from auxiliary panel data sets. We refer to our estimator as target-PCA. Transfer learning from auxiliary panel data allows us to deal with a large fraction of missing observations and weak signals in the target panel. We show that our estimator is more efficient and can consistently estimate weak factors, which are not…

可分离匹配模型的估计AI 解读

Summary Most recent empirical applications of matching with transferable utility have imposed a natural restriction: that the joint surplus be separable in the sources of unobserved heterogeneity. We propose here two simple methods to estimate models in this class. The first method is a minimum distance estimator that relies on the generalized entropy of matching. The second applies to the more special but popular Choo and Siow model, which reformulates as a generalized…

潜在效用与置换不变性：显示偏好方法AI 解读

This paper provides partial identification results for latent utility models that satisfy an invariance property on unobservables such as exchangeability. We employ a simple revealed preference argument to “difference out” unobservables, obtaining identifying inequalities for utility indices. We show the differencing argument is also useful for counterfactual analysis. The framework generalizes existing work in discrete choice by allowing latent feasibility sets and by…

高维面板数据模型的 GMM 估计AI 解读

In this paper, we study a class of high dimensional moment restriction panel data models with interactive effects, where the factors are unobserved and these factor loadings are nonparametrically unknown smooth functions of individual characteristic variables. We allow the dimension of the parameter vector and the number of moment conditions to diverge with the sample size. This is a very general framework and is closely related to many existing linear and nonlinear panel…

以矩方法构造渐近无偏的合成控制AI 解读

A common approach to constructing a Synthetic Control unit is to fit on the outcome variable and covariates in pre-treatment time periods, but it has been shown by Ferman and Pinto (2021) that this approach does not provide asymptotic unbiasedness when the fit is imperfect and the number of controls is fixed. Many related panel methods have a similar limitation when the number of units is fixed. I introduce and evaluate a new method in which the Synthetic Control is…

双向聚类面板模型的固定 b 渐近理论AI 解读

This paper studies a cluster robust variance estimator proposed by Chiang, Hansen and Sasaki (2024) for linear panels. First, we show algebraically that this variance estimator (CHS estimator, hereafter) is a linear combination of three common variance estimators: the one-way unit cluster estimator, the “HAC of averages” estimator, and the “average of HACs” estimator. Based on this finding, we obtain a fixed-b asymptotic result for the CHS estimator and corresponding test…

具有潜在组结构的面板函数系数分位数回归AI 解读

This paper considers estimating functional-coefficient models in panel quantile regression with individual effects, allowing the cross-sectional and temporal dependence for large panel observations. A latent group structure is imposed on the heterogenous quantile regression models so that the number of nonparametric functional coefficients to be estimated can be reduced considerably. With the preliminary local linear quantile estimates of the subject-specific functional…

FNETS：高维时间序列的因子调整网络估计与预测AI 解读

–We propose FNETS, a methodology for network estimation and forecasting of high-dimensional time series exhibiting strong serial- and cross-sectional correlations. We operate under a factor-adjusted vector autoregressive (VAR) model which, after accounting for pervasive co-movements of the variables by common factors, models the remaining idiosyncratic dynamic dependence between the variables as a sparse VAR process. Network estimation of FNETS consists of three steps: (i)…

人力资本与迁移：一个警示性案例AI 解读

We analyze the impact that the option of migration might have on human capital accumulation. We show that, when the return to education for migrants is lower in the destination than in the origin, the overall incentive to accumulate human capital is reduced as restrictions on migration are relaxed. We use panel data from the Chinese Household Income Project to document that the return to education for rural individuals is lower in urban areas than in rural areas. We then use…

收入动态与技能的代际传递AI 解读

This paper develops and estimates a two-factor model of intergenerational skill transmission when earnings inequality reflects differences in individual skills and other non-skill shocks. We consider heterogeneity in both initial skills and skill growth rates, allowing variation in skill growth to change over the lifecycle. Using administrative tax data on two linked generations of Canadians covering 37 years, we exploit covariances in log earnings (at different ages) both…

高维遗传数据预测中的分块估计与参考面板估计AI 解读

Genetic prediction holds immense promise for translating genetic discoveries into medical advances. As the high-dimensional covariance matrix (or the linkage disequilibrium (LD) pattern) of genetic variants often presents a block-diagonal structure, numerous methods account for the dependence among variants in predetermined local LD blocks. Moreover, due to privacy considerations and data protection concerns, genetic variant dependence in each LD block is typically estimated…

具有时变内生性的相关随机系数面板模型AI 解读

This paper studies a class of linear panel models with random coefficients. We do not restrict the joint distribution of the time-invariant unobserved heterogeneity and the covariates. We investigate identification of the average partial effect (APE) when fixed-effect techniques cannot be used to control for the correlation between the regressors and the time-varying disturbances. Relying on control variables, we develop a constructive two-step identification argument. The…

内生相关随机系数模型中的汽油需求价格弹性估计AI 解读

Summary We propose a per‐cluster instrumental variable (PCIV) approach for estimating linear correlated random coefficient models in the presence of contemporaneous endogeneity and two‐way fixed effects. This approach estimates heterogeneous effects and aggregates them to population averages. We demonstrate consistency, showing robustness over standard estimators, and provide analytic standard errors for robust inference. In Monte Carlo simulation, PCIV performs relatively…

基于独立性准则的面板结构 VAR 统计识别AI 解读

Summary This paper introduces a novel panel approach to structural vector autoregressive analysis. For identification, we impose independence of structural innovations at the pooled level. We demonstrate robustness of the method under cross‐sectional correlation and heterogeneity through simulation experiments. In an empirical application on monetary policy transmission in the Euro area, we find that bond spreads rise significantly after an unexpected monetary tightening.…

灵活的面板随机生产前沿模型AI 解读

Summary We propose a flexible stochastic production frontier model with fixed effects for the panel data in which the semiparametric frontier is additive with bivariate interactions. To avoid potential misspecification and/or “wrong skew problem” due to distributional assumptions, we model the conditional mean of the inefficiency to depend on environmental variables and to be known up to a vector of parameters. We propose a difference‐based estimator for parameters…

起讫点流量空间自回归模型的极大似然估计AI 解读

We introduce a spatial autoregressive hurdle model for nonnegative origin–destination flows yN,ij. The model incorporates a hurdle formulation to elucidate the different data-generating processes for zero and positive flows. Our model specifies three types of spatial influences on flow yN,ij that quantify the impact of third-party characteristics on the flow yN,ij: (i) the effect of outflows from origin j, (ii) the effect of inflows to destination i, and (iii) the effect of…

摘要暂缺

时变处理的边际结构均值模型：工具变量估计AI 解读

Robins introduced Marginal Structural Models (MSMs), a general class of counterfactual models for the joint effects of time-varying treatment regimes in complex longitudinal studies subject to time-varying confounding. In his work, identification of MSM parameters is established under a Sequential Randomization Assumption (SRA), which rules out unmeasured confounding of treatment assignment over time. We consider sufficient conditions for identification of the parameters of…

含终止事件纵向数据的二元时变系数模型核估计AI 解读

We propose a nonparametric bivariate time-varying coefficient model for longitudinal measurements with the occurrence of a terminal event that is subject to right censoring. The time-varying coefficients capture the longitudinal trajectories of covariate effects along with both the followup time and the residual lifetime. The proposed model extends the parametric conditional approach given terminal event time in recent literature, and thus avoids potential model…