Information-driven Business Cycles: A Primal Approach

By Ryan Chahrour and Robert Ulbricht

We develop a methodology to estimate DSGE models with incomplete information, free of parametric restrictions on information structures. First, we define a “primal” economy in which deviations from full information are captured by wedges in agents’ equilibrium expectations. Second, we provide implementability conditions, which ensure the existence of an information structure that implements these wedges. We apply the approach to estimate a New Keynesian model in which firms, households and the monetary authority have dispersed information about business conditions and productivity is the only aggregate fundamental. The estimated model fits the data remarkably well, with informational shocks able to account for the majority of U.S. business cycles. Output is driven mainly by household sentiments, whereas firm errors largely determine inflation. Our estimation indicates that firms and the central bank learn the aggregate state of the economy quickly, while household confusion about aggregate conditions is sizable and persistent.

The discussion about informational issues in the business cycle literature has made huge strides in the past years. Now we are at the point of estimating such models, even with information sets differing by type of agents. Households seem to be crucial here, and I wonder how of the fluctuations could be avoided by better economic and financial literacy, if this is a way to interpret the incomplete information. One good reason to check out the economic and financial literacy or data offerings at the St. Louis Fed.


One Response to Information-driven Business Cycles: A Primal Approach

  1. Robert Ulbricht says:

    Thanks for mentioning our paper. Your question regarding the source of information frictions is an interesting one. We generally think about them as constraints intrinsic to the “information technology” of agents. Limitations on the literacy of agents as you suggest might be one reason. For this one to be covered by our approach, financial illiteracy would need to translate into a lack of information (e.g., because of higher processing costs as in the rational inattention literature) as opposed to manifesting itself through suboptimal inference from a given set of signals.

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