Multivariate Time Series and Spectral Representation

This lecture delves into the analysis of multivariate time series, focusing on spectral representation and estimation techniques. The instructor explains the concept of p-variate time series, spectral density functions, and cross-spectral densities. The lecture covers the estimation of auto-covariance and spectral density functions, emphasizing the importance of second-order stationarity. Various spectral estimation methods, including periodogram and tapering, are discussed to improve the accuracy of spectral estimators.