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含影响节点与社群的高维分位数向量自回归AI 解读

Quantile vector autoregression (QVAR) models offer enhanced capabilities over vector autoregression (VAR) models in analyzing asymmetric interactions within multiple time series and are widely used in many areas, including finance and economics. However, in scenarios involving high-dimensional data where the number of time series N exceeds the length of the time series T, both parameter estimation and the theoretical establishment present significant challenges. Notably,…

具有网络关联固定效应的动态面板分位数回归AI 解读

This paper introduces a dynamic panel data quantile regression model with network-linked fixed effects, named DQR-NFE, in which unobserved individual heterogeneity is structured through an underlying network. The corresponding estimator is derived by incorporating a quantile network cohesion (QNC) penalty into the dynamic panel quantile regression framework. This penalty encourages connected units within the network to exhibit similar conditional quantiles, with a…