Testing part of a DSGE model by Indirect Inference

By Patrick Minford, Michael Wickens and Yongdeng Xu


e propose a new type of test. Its aim is to test subsets of the structural equations of a DSGE model. The test draws on the statistical inference for limited information models and the use of indirect inference to test DSGE models. Using Monte Carlo experiments on two subsets of equations of the Smets-Wouters model we show that the model has accurate size and good power in small samples. In a test of the Smets-Wouters model on US Great Moderation data we reject the specification of the wage-price but not the expenditure sector, pointing to the first as the source of overall model rejection.

It is very easy for DSGE model to be rejected. That should be expected, as they are a simplification of the real world and they are disciplined in ways that does not lend to data fitting at any cost. But as every research question that deserves its own model that highlights what is needed to answer that question and leaves aside what appears to be marginal, it is of great use to see whether the particular model features the right ingredients. This paper shows a method that can help us here, testing only part of the model, the one we really care about, without having the neglected parts dragging the whole model down.


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