Time Series: Estimation and Spectral Representation

This lecture covers the estimation of time series models, focusing on the properties of the estimator, variance convergence, and correlation in the process. It also discusses the concept of mean ergodicity, aliasing in sampling, and the spectral representation of time series. The instructor explains the cross-covariance and cross-spectral density functions for bivariate processes, as well as the properties of the spectral density function. The lecture concludes with the analysis of p-variate time series, joint CDF modeling, and second-order stationarity. Various spectral representations and their implications are explored in detail.