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非随机缺失数据下的共同基金绩效贝叶斯评估：中国市场应用AI 解读

This paper studies mutual fund performance evaluation, where non-random missing data is an important issue. Ignoring sample selection issues can lead to biased parameter estimates and invalid performance evaluation. Therefore, we propose a novel Beta selection model to characterize this non-random missingness mechanism. We can significantly reduce the bias in alpha estimates, by introducing our Beta selection mechanism into the Noise-Reduced Alpha (NRA) model of Harvey and…

主成分分析能否保留因子载荷的稀疏性？AI 解读

This article studies the principal component analysis (PCA) estimation of weak factor models with sparse loadings. We uncover an intrinsic near-sparsity preservation property for the PCA estimators of loadings, which comes from the approximately (block) upper triangular structure of the rotation matrix. It suggests an asymmetric relationship among factors: the sparsity of the rotated loadings for a stronger factor can be contaminated by the loadings from weaker ones, but the…

时变系数面板模型的设定检验AI 解读

This paper provides nonparametric specification tests for the commonly used homogeneous and stable coefficients structures in panel data models. We first obtain the augmented residuals by estimating the model under the null hypothesis and then run auxiliary time series regressions of augmented residuals on covariates with time-varying coefficients (TVCs) via sieve methods. The test statistic is then constructed by averaging the squared fitted values, which are close to zero…

群组数过度设定的面板模型识别与估计AI 解读

We propose a simple and fast approach to identify and estimate the unknown group structure in panel models by adapting the M-estimation method. We consider both linear and nonlinear panel models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership is unknown to researchers. The main result of the paper is that under certain assumptions, our approach is able to provide uniformly consistent estimation as long…

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…