The past as prologue : a new approach to forecasting

It is common practice to forecast social, political, and economic outcomes by polling people about their intentions. This approach is direct, but it can be unreliable in settings where it is hard to identify a representative sample, or where subjects have an incentive to conceal their true intentions or beliefs. The authors propose that, as a substitute or a supplement, forecasters use historical outcomes to predict future ones. The relevance of historical events, however, is not guaranteed. The authors apply a novel technique called Partial Sample Regression to identify, in a mathematically precise way, the subset of events that are most relevant to the present. The outcomes of those events are then weighted by their relevance and averaged to give a prediction for the future. The authors illustrate their technique by showing that it correctly predicted the winner of the last six U.S. presidential elections based only on the political, geopolitical, and economic circumstances of the election year