Econometric Modelling of Time Series with Outlying Observations

Economies are buffeted by natural shocks, wars, policy changes, and other unanticipated events. Observed data can be subject to substantial revisions. Consequently, a “correct” theory can manifest serious mis-specification if just fitted to data ignoring its time-series characteristics. Modelling U.S. expenditure on food, the simplest theory implementation fails to describe the evidence. Embedding that theory in a general framework with dynamics, outliers and structural breaks and using impulse-indicator saturation, the selected model performs well, despite commencing with more variables than observations (see Doornik, 2009b), producing useful robust forecasts. Although this illustration involves a simple theory, the implications are generic and apply to sophisticated theories.