论文检索

空间动态面板中潜在群组结构的识别AI 解读

This paper considers the identification of latent group structures in spatial dynamic panels. We follow Lee and Yu (2010) and consider a rich spatial dynamic panel data (SDPD) model with two-way fixed effects. In addition, we also allow for latent panel structures where individuals can be classified into a few groups such that individuals within the same group share the common slope parameters and do not otherwise. Both the number of groups and the individuals’ group…

时变系数模型的设定检验AI 解读

Time-varying coefficient models have been widely used to characterize changing relationships among economic and financial variables. The existing literature usually specifies the time-varying coefficient vector as a stationary stochastic process, a deterministic function of time, and a unit root process, respectively. In this paper, we propose two tests to distinguish these three specifications. Both test statistics follow asymptotic normal distributions under the respective…

含交互固定效应的面板模型的剖面 GMM 估计AI 解读

This paper studies panel data models with interactive fixed effects where the regressors are allowed to be correlated with the idiosyncratic error terms. We propose a two-step profile GMM estimation procedure to estimate the parameters of interest. In the first step we obtain a preliminary consistent estimate of the slope coefficient via a nuclear-norm-regularization (NNR) based profile GMM procedure. In the second step, via an iterative procedure, we conduct post-NNR…

双维度异质性线性面板模型的一致有效推断AI 解读

This paper studies uniform inference in a linear panel data model when the slope coefficients may exhibit heterogeneity over both the individual and time dimensions and they can be correlated with the regressors. We propose a generalized two-way fixed effects (GTWFE) estimation procedure to estimate the model. To establish the asymptotic properties of the GTWFE estimators, we invert a number of large dimensional square matrices by approximating them with quasi-Kronecker…

通过 Classifier-Lasso 检测有效价格中的未观测异质性AI 解读

This article proposes a new measure of efficient price as a weighted average of bid and ask prices, where the weights are constructed from the bid-ask long-run relationships in a panel error-correction model (ECM). To allow for heterogeneity in the long-run relationships, we consider a panel ECM with latent group structures so that all the stocks within a group share the same long-run relationship and do not otherwise. We extend the Classifier-Lasso method to the ECM to…

含共同因子的高维向量自回归模型AI 解读

This paper studies high-dimensional vector autoregressions (VARs) augmented with common factors that allow for strong cross-sectional dependence. Models of this type provide a convenient mechanism for accommodating the interconnectedness and temporal co-variability that are often present in large dimensional systems. We propose an ℓ1-nuclear-norm regularized estimator and derive the non-asymptotic upper bounds for the estimation errors as well as large sample asymptotics for…

随机缺失下的因子模型：EM 估计、推断与交叉验证AI 解读

We consider the estimation and inference in approximate factor models with random missing values. We show that with the low rank structure of the common component, we can estimate the factors and factor loadings consistently with the missing values replaced by zeros. We establish the asymptotic distributions of the resulting estimators and those based on the EM algorithm. We also propose a cross-validation-based method to determine the number of factors in factor models with…

具有潜在分组结构与截面依赖的非平稳面板模型AI 解读

This paper proposes a novel Lasso-based approach to handle unobserved parameter heterogeneity and cross-section dependence in nonstationary panel models. In particular, a penalized principal component (PPC) method is developed to estimate group-specific long-run relationships and unobserved common factors and jointly to identify the unknown group membership. The PPC estimators are shown to be consistent under weakly dependent innovation processes. But they suffer an…

非线性面板中潜在分组结构的识别AI 解读

We propose a procedure to identify latent group structures in nonlinear panel data models where some regression coefficients are heterogeneous across groups but homogeneous within a group and the group number and membership are unknown. To identify the group structures, we consider the order statistics for the preliminary unconstrained consistent estimators of the regression coefficients and translate the problem of classification into the problem of break detection. Then we…

通过非参数回归检验因子模型的结构变化AI 解读

We propose a model-free test for structural changes in factor models. The basic idea is to regress the data on commonly estimated factors by local smoothing and compare the fitted values of time-varying factor loadings with those of time-invariant factor loadings estimated via principal component analysis. By construction, the test is designed to be powerful against both smooth structural changes and sudden structural breaks with a possibly unknown number of breaks and…

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…

识别协整面板中的潜在分组模式AI 解读

We consider a panel cointegration model with latent group structures that allows for heterogeneous long-run relationships across groups. We extend Su, Shi, and Phillips (2016, Econometrica 84(6), 2215–2264) classifier-Lasso (C-Lasso) method to the nonstationary panels and allow for the presence of endogeneity in both the stationary and nonstationary regressors in the model. In addition, we allow the dimension of the stationary regressors to diverge with the sample size. We…

面板数据模型中个体效应与时间效应的判定AI 解读

In this paper we propose a jackknife method to determine individual and time effects in linear panel data models. We first show that when both the serial and cross-sectional correlations among the idiosyncratic error terms are weak, our jackknife method can pick up the correct model with probability approaching one (w.p.a.1). In the presence of moderate or strong degree of serial correlation, we modify our jackknife criterion function and show that the modified jackknife…

具有潜在分组结构的面板门槛回归AI 解读

In this paper, we consider the least squares estimation of a panel structure threshold regression (PSTR) model where both the slope coefficients and threshold parameters may exhibit latent group structures. We study the asymptotic properties of the estimators of the latent group structure and the slope and threshold coefficients. We show that we can estimate the latent group structure correctly with probability approaching 1 and the estimators of the slope and threshold…

高维资产条件时变因子模型中的 Alpha 检验AI 解读

For conditional time-varying factor models with high-dimensional assets, this article proposes a high-dimensional alpha (HDA) test to assess whether there exist abnormal returns on securities (or portfolios) over the theoretical expected returns. To employ this test effectively, a constant coefficient test is also introduced. It examines the validity of constant alphas and factor loadings. Simulation studies and an empirical example are presented to illustrate the finite…

高维数据下的非可分模型AI 解读

This paper studies non-separable models with a continuous treatment when the dimension of the control variables is high and potentially larger than the effective sample size. We propose a three-step estimation procedure to estimate the average, quantile, and marginal treatment effects. In the first stage we estimate the conditional mean, distribution, and density objects by penalized local least squares, penalized local maximum likelihood estimation, and numerical…

含交互固定效应的半参数单指标面板模型：理论与实践AI 解读

In this paper, we propose a single-index panel data model with unobserved multiple interactive fixed effects. This model has the advantages of being flexible and of being able to allow for common shocks and their heterogeneous impacts on cross sections, thus making it suitable for the investigation of many economic issues. The asymptotic theories are established accordingly. Our Monte Carlo simulations show that our methodology works well for large N and T cases. In our…

具有潜在分组结构的时变面板模型的筛估计AI 解读

We propose a heterogeneous time-varying panel data model with a latent group structure that allows the coefficients to vary over both individuals and time. We assume that the coefficients change smoothly over time and form different unobserved groups. When treated as smooth functions of time, the individual functional coefficients are heterogeneous across groups but homogeneous within a group. We propose a penalized-sieve-estimation-based classifier-Lasso (C-Lasso) procedure…

断点数量未知的大维因子模型估计AI 解读

In this paper we study the estimation of a large dimensional factor model when the factor loadings exhibit an unknown number of changes over time. We propose a novel three-step procedure to detect the breaks if any and then identify their locations. In the first step, we divide the whole time span into subintervals and fit a conventional factor model on each interval. In the second step, we apply the adaptive fused group Lasso to identify intervals containing a break. In the…

交互固定效应面板模型中潜在分组模式的识别AI 解读

We consider the estimation of latent grouped patterns in dynamic panel data models with interactive fixed effects. We assume that the individual slope coefficients are homogeneous within a group and heterogeneous across groups but each individual’s group membership is unknown to the researcher. We consider penalized principal component (PPC) estimation by extending the penalized-profile-likelihood-based C-Lasso of Su, Shi, and Phillips (2016) to panel data models with cross…

面板数据模型中的同质分组识别：理论与应用AI 解读

Summary This paper studies the estimation of a panel data model with latent structures where individuals can be classified into different groups with the slope parameters being homogeneous within the same group but heterogeneous across groups. To identify the unknown group structure of vector parameters, we design an algorithm called Panel‐CARDS. We show that it can identify the true group structure asymptotically and estimate the model parameters consistently at the same…

潜在面板结构中的组数确定：收入与民主的应用AI 解读

We consider a latent group panel structure as recently studied by Su, Shi, and Phillips (2016), where the number of groups is unknown and has to be determined empirically. We propose a testing procedure to determine the number of groups. Our test is a residual-based Lagrange multiplier-type test. We show that after being appropriately standardized, our test is asymptotically normally distributed under the null hypothesis of a given number of groups and has the power to…

空间自回归模型的设定检验AI 解读

This article considers a simple test for the correct specification of linear spatial autoregressive models, assuming that the choice of the weight matrix Wn is true. We derive the limiting distributions of the test under the null hypothesis of correct specification and a sequence of local alternatives. We show that the test is free of nuisance parameters asymptotically under the null and prove the consistency of our test. To improve the finite sample performance of our test,…

时变因子模型的估计与检验AI 解读

Conventional factor models assume that factor loadings are fixed over a long horizon of time, which appears overly restrictive and unrealistic in applications. In this paper, we introduce a time-varying factor model where factor loadings are allowed to change smoothly over time. We propose a local version of the principal component method to estimate the latent factors and time-varying factor loadings simultaneously. We establish the limiting distributions and uniform…