Title: Factor-augmented time series models with functional coefficients
Authors: Jiraroj Tosasukul - The University of York (United Kingdom) [presenting]
Abstract: A new class of functional-coefficient time series models is introduced, where the regressors consist of autoregressors and latent factor regressors, and the coefficients vary with the certain index variable. The unobservable factor regressors are estimated through imposing an approximate factor model on very high dimensional exogenous time series variables and subsequently implementing the classical principal component analysis. With the estimated factor regressors, a local linear smoothing method is used to estimate the coefficient functions and obtain a one-step ahead nonlinear forecast of the response variable, and then a bootstrap procedure is introduced to construct the prediction interval. In particular, our asymptotic theory shows that the local linear estimator and the nonlinear forecast using the estimated factor regressors are asymptotically equivalent to those using the true latent factor regressors. The developed methodology is further extended to the case of multivariate response vectors and the model is generalised to the factor-augmented vector time series model with functional coefficients. The latter substantially generalises the linear factor-augmented vector autoregressive model which has been extensively studied in the literature. Finally, some simulation studies and an empirical application are given to examine the finite-sample performance of the proposed models and methodologies.