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Summary This paper focuses on estimating the coefficients and average partial effects of observed regressors in nonlinear panel data models with interactive fixed effects, using the common correlated effects framework. The proposed two-step estimation method involves applying principal component analysis to estimate the latent factors based on cross-sectional averages of the regressors in the first step, and jointly estimating the coefficients of the regressors and the…

交互固定效应高维面板模型的估计与推断AI 解读

We develop new econometric methods for estimation and inference in high‐dimensional panel data models with interactive fixed effects. Our approach can be regarded as a nontrivial extension of the very popular common correlated effects (CCE) approach. Roughly speaking, we proceed as follows: We first construct a projection device to eliminate the unobserved factors from the model by applying a dimensionality reduction transform to the matrix of cross‐sectionally averaged…

含交互固定效应的异质面板二元响应模型AI 解读

In this paper, we investigate binary response models for heterogeneous panel data with interactive fixed effects by allowing both the cross-sectional dimension and the temporal dimension to diverge. From a practical point of view, the proposed framework can be applied to predict the probability of corporate failure, conduct credit rating analysis, etc. Theoretically and methodologically, we build a link between a maximum likelihood estimation and a least squares approach,…

利用含截面依赖的函数系数面板模型检验资本资产定价模型AI 解读

This paper proposes a functional-coefficient panel data model with cross-sectional dependence motivated by re-examining the empirical performance of conditional capital asset pricing model. In order to characterize the time-varying property of assets’ betas and alpha, our proposed model allows the betas to be unknown functions of some macroeconomic and financial instruments. Moreover, a common factor structure is introduced to characterize cross-sectional dependence which is…

含固定效应与横截面依赖的分数阶单整面板估计AI 解读

We consider a large N,T heterogeneous panel data model with fixed effects, common factors allowing for cross-section dependence, and persistent data and errors, which are assumed fractionally integrated. We propose individual and common-correlation estimates for the slope parameters while error memory parameters are estimated from regression residuals. The individual parameter estimates are all T consistent, asymptotically normal and mutually uncorrelated, irrespective of…

交易碎片化对英国股票市场质量的影响AI 解读

Summary We investigate the effects of fragmentation in equity markets on the quality of trading outcomes in a panel of FTSE stocks over the period 2008–2011. This period coincided with a great deal of turbulence in the UK equity markets, which had multiple causes that need to be controlled for. To achieve this, we use the common correlated effects estimator for large heterogeneous panels. We extend this estimator to quantile regression to analyse the whole conditional…

欧元公司债券的风险因子AI 解读

SUMMARY This paper investigates the determinants of credit spread changes in euro‐denominated bonds. We adopt a factor model framework, inspired by the credit risk structural approach, as credit spread changes can be easily viewed as an excess return on corporate bonds over Treasury bonds. We try to assess the relative importance of market and idiosyncratic factors as an explanation of movements in credit spreads. We adopt a heterogeneous panel with a multifactor error model…