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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…

平滑结构变化下 FAVAR 模型的时变模型平均AI 解读

Factor-augmented vector autoregressive (FAVAR) models serve as a powerful tool for forecasting economic and financial variables, particularly in environments with a large number of predictors. In this study, we propose a time-varying model-averaging method for FAVAR (TVMA-FAVAR) that accommodates smooth structural changes. To estimate the optimal time-varying combination weights, we develop a local leave-one-out cross-validation (LLOCV) criterion that is asymptotically…

高维条件因子模型AI 解读

This paper studies estimation and variable selection in conditional factor models with high-dimensional instruments, where the coefficient matrix exhibits a low-rank and row-sparse structure. We propose a multi-stage estimation procedure that combines nuclear norm regularization and adaptive group LASSO regression to consistently estimate latent factors and row-sparse loading coefficients, while selecting relevant instrumental characteristics. We establish theoretical…

状态变化因子模型的筛估计AI 解读

In this paper, we propose a sieve approach to estimate state-varying factor models, where the factor loadings vary over specific state variables. Our methodology consists of a two-step estimation procedure for the parameters of interest. In the first step, we achieve preliminary consistent estimates of the factors and factor loadings via nuclear norm regularization (NNR). In the second step, we perform post-NNR iterative least squares estimations for the factors and factor…

时变因子模型的区分检验AI 解读

Time-varying factor models have been widely used to model changing relationships among economic and financial variables. The existing literature usually specifies the time-varying factor loadings as deterministic functions of time or unit root processes. This article proposes two consistent tests to distinguish between these two specifications based on a randomization approach. By setting the null hypothesis as either specification, we show that the proposed test statistics…

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…

基于离散傅里叶变换的严格平稳性检验AI 解读

This paper proposes a model-free test for the strict stationarity of a potentially vector-valued time series using the discrete Fourier transform (DFT) approach. We show that the DFT of a residual process based on the empirical characteristic function weakly converges to a zero spectrum in the frequency domain for a strictly stationary time series and a nonzero spectrum otherwise. The proposed test is powerful against various types of nonstationarity including deterministic…

时变因子扩展向量自回归模型的估计与推断AI 解读

We introduce a time-varying (TV) factor-augmented vector autoregressive (FAVAR) model to capture the TV behavior in the factor loadings and the VAR coefficients. To consistently estimate the TV parameters, we first obtain the unobserved common factors via the local principal component analysis (PCA) and then estimate the TV-FAVAR model via a local smoothing approach. The limiting distribution of the proposed estimators is established. To gauge possible sources of TV features…

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…

分布中的多个结构突变：经验特征函数方法AI 解读

We estimate and test for multiple structural breaks in distribution via an empirical characteristic function approach. By minimizing the sum of squared generalized residuals, we can consistently estimate the break fractions. We propose a sup- F type test for structural breaks in distribution as well as an information criterion and a sequential testing procedure to determine the number of breaks. We further construct a class of derivative tests to gauge possible sources of…

利用离散傅里叶变换检验高维因子模型的结构变化AI 解读

We propose a new test for structural changes in large dimensional factor models using a discrete Fourier transform (DFT) approach. When structural changes occur, the conventional principal component analysis may fail to estimate the common factors and factor loadings consistently, and the estimated residuals contain information about the structural changes. This allows us to compare the DFT of the estimated residuals weighted by the estimated common factors with the null…

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…

基于特征函数与非参数回归的条件独立检验AI 解读

We propose a characteristic function based test for conditional independence, applicable to both cross-sectional and time series data. We also derive a class of derivative tests, which deliver model-free tests for such important hypotheses as omitted variables, Granger causality in various moments and conditional uncorrelatedness. The proposed tests have a convenient asymptotic null N (0, 1) distribution, and are asymptotically locally more powerful than a variety of related…

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…