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含交互固定效应的非平衡面板模型估计与推断AI 解读

This paper establishes the inferential theory for unbalanced panel data models with interactive fixed effects. We propose a two-step estimation algorithm with the first step obtaining an initial consistent estimator followed by an alternating maximization procedure. We prove that the alternating maximization procedure is a contractionary mapping and the final estimator is asymptotically normal, as long as the initial estimator is consistent. We also develop analytical bias…

一般缺失模式下高维因子模型的推断AI 解读

This paper establishes the inferential theory for the least squares estimation of large factor models with missing data. We propose a unified framework for asymptotic analysis of factor models that covers a wide range of missing patterns, including heterogenous random missing, selection on covariates/factors/loadings, block/staggered missing, mixed frequency and ragged edge. We establish the average convergence rates of the estimated factor space and loading space, the limit…

载荷机制切换高维因子模型的估计与推断AI 解读

This paper proposes maximum (quasi)likelihood estimation for high dimensional factor models with regime switching in the loadings. The model parameters are estimated jointly by the EM (expectation maximization) algorithm, which in the current context only requires iteratively calculating regime probabilities and principal components of the weighted sample covariance matrix . When regime dynamics are taken into account, smoothed regime probabilities are calculated using a…

高维广义因子模型的极大似然估计与推断：因子扩展回归应用AI 解读

This paper reestablishes the main results in Bai (2003) and Bai and Ng (2006) for generalized factor models, with slightly stronger conditions on the relative magnitude of N (number of subjects) and T (number of time periods). Convergence rates of the estimated factor space and loading space and asymptotic normality of the estimated factors and loadings are established under mild conditions that allow for linear, Logit, Probit, Tobit, Poisson and some other single-index…

具有多重结构变化的高维因子模型估计与检验AI 解读

This paper considers multiple changes in the factor loadings of a high dimensional factor model occurring at dates that are unknown but common to all subjects. Since the factors are unobservable, the problem is converted to estimating and testing structural changes in the second moments of the pseudo factors. We consider both joint and sequential estimation of the change points and show that the distance between the estimated and the true change points is Op(1). We find that…

结构不稳定高维因子模型的识别与估计AI 解读

This paper tackles the identification and estimation of a high dimensional factor model with unknown number of latent factors and a single break in the number of factors and/or factor loadings occurring at unknown common date. First, we propose a least squares estimator of the change point based on the second moments of estimated pseudo factors and show that the estimation error of the proposed estimator is Op(1). We also show that the proposed estimator has some degree of…