Likelihood and other approaches to prediction in dynamic models (Q1105969)

We consider the problem of generating multi-period predictions from two simple dynamic models, an autoregressive model and a geometric random walk. The autoregressive model constitutes a useful paradigm for many of the practical problems of prediction because it possesses a number of features that differentiate it sharply from the standard linear regression model. The geometric random walk model is widely used in macroeconomics and finance and is fundamentally non-normal.