Testing Macro Models for Policy Use-An Insurrection in Applied Modelling

What is it about?

I describe a new departure in classical testing methods based on Indirect Inference. This test compares the model's simulated behaviour with the behaviour found in the data and checks the closeness of the match. This test gives policymakers, anxious to know if their models give reliable policy conclusions, a way to find out. 'Monte Carlo experiments' - whereby one sees by repeated trials on an assumed model how well the method performs- show that the method has strong discriminating power between the correct model and incorrect models. This power allows users to focus use the test flexibly: they can set the test threshold so that they obtain both good power and a good chance of finding a tractable model. Once they have found a model version that is not rejected by the test, they can then discover the range of error to which their policy results are vulnerable.

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Why is it important?

This is important because it gives policymakers a new and powerful tool for gauging how good their models are.

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