Multivariate Time Series: Cointegration & Forecasting

This lecture covers the concepts of multivariate time series analysis, focusing on cointegration and forecasting using ARMA models. It explains the Box-Jenkins approach for modeling stationary series, the persistence of U.S. inflation, and the optimal forecasting methods. The lecture also delves into the issues of spurious regression and the importance of detecting cointegration in variables. Practical applications include interest rates analysis and error-correction mechanisms. Various tests and models are discussed to enhance dynamic models for time series data.