Modeling Functional Time Series and Mixed-Type Predictors With Partially Functional Autoregressions
以部分函数型自回归建模函数时间序列与混合类型预测变量
In many business and economics studies, researchers have sought to measure the dynamic dependence of curves with high-dimensional mixed-type predictors. We propose a partially functional autoregressive model (pFAR) where the serial dependence of curves is controlled by coefficient operators that are defined on a two-dimensional surface, and the individual and group effects of mixed-type predictors are estimated with a two-layer regularization. We develop an efficient…