I continue to see counterfactuals on the stimulus package that are generated from structural models of the U.S. economy. These counterfactuals, however, are not useful for ex post policy analysis because they provide the same predictions ex ante. John Taylor makes this point using a simple example:
- Consider two models relating the size of the stimulus package (symbolized by S) to GDP (symbolized by Y). Model A is Y= αS + Z and Model B is Y = Z, where Z is an unobservable shock and α is a coefficient which we set to 1.5.
- Suppose that a stimulus is enacted with S = 2, but Y falls to -1. Then the shock implied by Model A is Z = – 4 while the shock implied by model B is Z= -1.
- Now consider policy evaluation of the stimulus based on a counterfactual where there is no stimulus so S=0.
- Economists using Model A would say:
Just as we predicted, the stimulus package worked. Without it, Y would have fallen to -4 rather than -1. The decline in output would have been 4 times as deep, a Great Depression 2.0.
- Economists using Model B would simply say:
Just as we predicted the stimulus package didn’t work.
- The best way to way to deal with this problem is to look empirically at the direct effect of the stimulus using actual data, but without imposing a specific model structure like Model A or Model B.
Taylor’s own empirical estimates suggest the stimulus failed.
Thanks for the tip. Keep up the good work.
Uhhh… someone here is just begging the question. The issue isn’t whether the stimulus failed, obviously it failed seeing the economy is not where we want it to be, the issue is why? Did it fail because it’s not big enough or because stimuluses (stimuli?) don’t work? I’m not sure what this post adds to that discussion.
My contribution to the discussion can be found here:
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