Model Choice and Prediction

This lecture covers model choice and prediction in time series analysis. The instructor discusses formal methods for comparing models, such as the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). The lecture also delves into forecasting techniques, including mean square error minimization and the concept of forecasting errors. Additionally, the presentation explores forecasting in autoregressive (AR) and moving average (MA) processes, as well as general ARMA models. The importance of understanding the properties of forecasting errors and the implications for different types of models is emphasized.