Marco Del Negro, Raiden Hasegawa, and Frank Schorfheide — Combining Models for Forecasting and Policy Analysis

As we noted before, the ideal model combination is the one that puts all the weight on the model that has the most accurate projection. The problem is, you know which one that is only afterwards. A measure of success is the extent to which your real-time combination produces a forecast that is as accurate as possible to that of the best model. The chart shows that the forecast accuracy of the dynamic pools approach is quite close to that of the best model most of the time, and in particular during the Great Recession. This confirms that the bucket tilted early enough in the game and shifted the weight toward the model with financial frictions. We show in the paper that, because of this timeliness, our procedure does generally better than the competition in terms of out-of-sample forecasting performance. 

Now, imagine you are a policymaker and are contemplating the implementation of a given policy. Consider also that this policy achieves the desired outcome in one model, but not in the other. Clearly the decision of whether to implement the policy depends on which model is the right one. You do not quite know that, but you do know the weights. Using them to come up with an assessment of the pros and cons of a given policy is the topic of our next post.

FRBNY — Liberty Street Economics
Combining Models for Forecasting and Policy Analysis
Marco Del Negro, Raiden Hasegawa, and Frank Schorfheide