Estimation procedure for a high dimensional dynamic autoregressive model . - Macapinlac, Michelle M. (University of the Philippines, 2016)
Abstract (We propose a dynamic model with high dimensional training set and high dimensional exogenous predictors. To address the dynamic behavior of the model and high dimensionally, an estimation procedure is developed through a hybrid of sparse principal components and nonparametric regression. Sparse principal components analysis reduces the dimension of the data while accounting for the dynamic behavior of the predictors. Lost information due to selection only for a few principal components is addressed by allowing a more flexible nonparametric structure of the contribution of the principal components. A simulations study illustrates the advantages of the proposed model relative to the AutoRegression with Google search (ARGO) model).