Time Series Models: Autoregressive Processes

This lecture covers topics related to time series models, focusing on autoregressive processes. It explains concepts such as white noise, autoregression, unconditional moments of AR(1) process, moving average processes, white noise vs. AR(1), lag polynomial, stationarity, ergodicity, heteroskedastic AR(P) model, and model estimation methods. The lecture also delves into the comparison between white noise and MA(1) processes, as well as the properties and estimation of ARMA models.