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交互固定效应面板数据模型与（相对）小 TAI 解读

This paper studies the estimation of a linear panel data model with interactive fixed effects. A transformation is introduced which, after having been applied, renders the least squares (LS) estimator of Bai (2009) consistent and asymptotically unbiased when n is large and T is fixed. This is termed the transformed least squares (TLS) estimator. Going further, these properties are shown to also carry over to the large n, large T setting, provided T /n → 0. This contrasts…

基于离散傅里叶变换检验交互固定效应面板模型的结构变化AI 解读

We propose a discrete Fourier transform (DFT)-based test for detecting structural changes in panel data models with interactive fixed effects (IFE). When structural changes are present but ignored, the conventional asymptotic distribution of the least squares (LS) estimator of Bai (2009) may no longer provide reliable inference, and the resulting residuals contain signals of parameter instability. Our test exploits this feature by comparing the DFT of weighted residuals to…

交互固定效应面板中潜在组结构的收缩估计与识别AI 解读

This paper provides a framework for joint shrinkage estimation and identification of latent group structures in panel data models with interactive fixed effects and large number of explanatory variables. The latent structure of the model allows individuals to be classified into a number of groups where the number of groups and/or each individual’s group identity are unknown. A doubly penalized principal component estimation procedure using a pairwise fusion penalty and an…

具有时变交互固定效应的时变面板数据模型AI 解读

This paper introduces a time-varying (TV) panel data model with interactive fixed effects where both the coefficients and factor loadings are allowed to change smoothly over time. We propose a local version of the least squares and principal component method to estimate the TV coefficients, TV factor loadings, and common factors simultaneously. We provide a bias-corrected local least squares estimator for the TV coefficients and establish the limiting distributions and…

交互效应面板模型中的多重结构突变AI 解读

ABSTRACT This paper develops new econometric methods for multiple structural break detection in panel data models with interactive fixed effects. The new methods include tests for the presence of structural breaks, estimators for the number of breaks and their location, and a method for constructing asymptotically valid break date confidence intervals. The new methodology is applied to a large panel of US banks for a period characterized by massive quantitative easing…

长记忆下交互固定效应面板模型的稳健推断：频域方法AI 解读

This paper studies a linear panel data model with interactive fixed effects wherein regressors, factors and idiosyncratic error terms are all stationary but with potential long memory. The setup involves a new formulation of panel data models, where weakly dependent regressors, factors and idiosyncratic errors are embedded as a special case. Standard methods based on principal component decomposition and least squares estimation, as in Bai (2009), are found to be biased and…

具有时变潜在分组结构的面板数据模型AI 解读

This paper considers a linear panel model with interactive fixed effects and unobserved individual and time heterogeneities that are captured by some latent group structures and an unknown structural break, respectively. To enhance realism, the model may have different numbers of groups and/or different group memberships before and after the break. With preliminary nuclear norm regularized estimation followed by row- and column-wise linear regressions, we estimate the break…

具有交互效应的动态面板模型似然方法AI 解读

This paper studies dynamic panel models with a factor error structure that is correlated with the regressors. Both short panels (small T) and long panels (large T) are considered. A dynamic panel forms a simultaneous-equation system, and under the factor error structure, there exist constraints between the mean and the covariance matrix. We explore the constraints through a quasi-FIML (full information maximum likelihood) approach. The quasi-FIML approach does not estimate…

含个体、时间及交互效应的短期动态面板模型AI 解读

Summary This paper proposes a transformed quasi‐maximum likelihood (TQML) estimator for short dynamic fixed effects panel data models allowing for interactive effects through a multifactor error structure. The proposed estimator is robust to the heterogeneity of the initial values and common unobserved effects, while at the same time allowing for standard fixed and time effects. It is applicable to both stationary and unit root cases. The order condition for identification…

近单位根交互效应面板中的因子分析方法AI 解读

In a recent study, Bai (2013) proposes a new factor analytical (FA) method for estimation of stationary dynamic panel data models with fixed effects. Our interest in this method originates with the fact it does not require explicit demeaning of the data, a practice that is known to cause problems of bias and low power in near unit root panels. The purpose is to study the properties of FA when applied to such panels when the common component admits to a interactive effects…

具有交互固定效应的面板门槛模型AI 解读

This paper studies estimation and inference in a panel threshold model in the presence of interactive fixed effects. We study the asymptotic properties of the least squares estimators of the regression parameters in the shrinking-threshold-effect framework. We find that under some regularity conditions, the threshold parameter estimator possesses super-consistency in the sense that its estimation error has an asymptotically negligible effect on the asymptotic properties of…

This paper considers a class of generalized methods of moments (GMM) estimators for general dynamic panel models, allowing for weakly exogenous covariates and cross‐sectional dependence due to spatial lags, unspecified common shocks, and time‐varying interactive effects. We significantly expand the scope of the existing literature by allowing for endogenous time‐varying spatial weight matrices without imposing explicit structural assumptions on how the weights are formed. An…

长程依赖下的面板数据模型系统估计AI 解读

A general dynamic panel data model is considered that incorporates individual and interactive fixed effects allowing for contemporaneous correlation in model innovations. The model accommodates general stationary or nonstationary long-range dependence through interactive fixed effects and innovations, removing the necessity to perform a priori unit-root or stationarity testing. Moreover, persistence in innovations and interactive fixed effects allows for cointegration;…

含交互固定效应与多重结构突变的面板数据模型AI 解读

In this article, we consider estimation of common structural breaks in panel data models with unobservable interactive fixed effects. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the…

交互固定效应动态面板模型的收缩估计AI 解读

We consider the problem of determining the number of factors and selecting the proper regressors in linear dynamic panel data models with interactive fixed effects. Based on the preliminary estimates of the slope parameters and factors a la Bai (2009) and Moon and Weidner (2015), we propose a method for simultaneous selection of regressors and factors and estimation through the method of adaptive group Lasso (least absolute shrinkage and selection operator). We show that…

交互固定效应面板模型的设定检验AI 解读

In this paper, we propose a consistent nonparametric test for linearity in a large dimensional panel data model with interactive fixed effects. Both lagged dependent variables and conditional heteroskedasticity of unknown form are allowed in the model. We estimate the model under the null hypothesis of linearity to obtain the restricted residuals which are then used to construct the test statistic. We show that after being appropriately centered and standardized, the test…

交互固定效应面板模型中的同质性检验AI 解读

This paper proposes a residual-based Lagrange Multiplier (LM) test for slope homogeneity in large-dimensional panel data models with interactive fixed effects. We first run the panel regression under the null to obtain the restricted residuals and then use them to construct our LM test statistic. We show that after being appropriately centered and scaled, our test statistic is asymptotically normally distributed under the null and a sequence of Pitman local alternatives. The…

具有交互固定效应的面板数据模型AI 解读

This paper considers large N and large T panel data models with unobservable multiple interactive effects. These models are useful for both micro and macro econometric modelings. In earnings studies, for example, workers ’ motivation, persistence, and diligence combined to influence the earnings in addition to the usual argument of innate ability. In macroeconomics, the interactive effects represent unobservable common shocks and their heterogeneous responses over cross…