March 5, 2019, 15:30–17:00
Room MS 001
Econometrics and Empirical Economics Seminar
We first consider a class of vector autoregressive models with banded coefficient matrices. The setting represents a type of sparse structure for high-dimensional time series, though the implied autocovariance matrices are not banded. The structure is also practically meaningful when the order of component time series is arranged appropriately. We then consider such a banded autoregression in the context of spatio-temporal modelling, for which the conventional least squares estimation is no longer valid. Instead we apply the least squares method based on a Yule-Walker equation to estimate autoregressive coefficient matrices. Illustration with both simulated and real data examples will be presented.