4 - Model Selection

Summary

We first discuss a phenomenon called data mining. This can involve multiple tests on which variables or correlations are relevant. If used improperly, data mining may associate with scientific misconduct. Next, we discuss one way to arrive at a single final model, involving stepwise methods. We see that various stepwise methods lead to different final models. Next, we see that various configurations in test situations, here illustrated for testing for cointegration, lead to different outcomes. It may be possible to see which configurations make most sense and can be used for empirical analysis. However, we suggest that it is better to keep various models and somehow combine inferences. This is illustrated by an analysis of the losses in airline revenues in the United States owing to 9/11. We see that out of four different models, three estimate a similar loss, while the fourth model suggests only 10 percent of that figure. We argue that it is better to maintain various models, that is, models that stand various diagnostic tests, for inference and for forecasting, and to combine what can be learned from them.