Econometric Modelling, Forecasting, and Inter-temporal Optimization in a Non-stationary World

The talk summarizes three `cases studies'. The first concerns economic forecasting where we establish the primacy of location shifts in forecast failure. Equilibrium-correction models (EqCMs) face serious forecasting problems, but mechanistic corrections help compared to just retaining a pre-break estimated model, although an estimated model of the break process could outperform. This sets the scene for the rest of the talk. The second concerns model selection. Economies are high dimensional, and forecast failure reveals non-constancy, so many features of models cannot be derived by prior reasoning, intrinsically involving empirical discovery and theory evaluation. Fitting a pre-specified model limits discovery, but automatic methods can formulate much more general models with many variables, long lag lengths and non-linearities, allowing for outliers, data contamination, and parameter shifts; select congruent parsimonious-encompassing models even with more candidate variables than observations, while embedding the relevant theory; then rigorously evaluate selected models to ascertain their viability. The third concerns inter-temporal optimization and the formation of `rational expectations', where misleading results follow from present approaches applied to realistic economies.