The Quantitative Significance of the Lucas Critique

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Creator Series Issue number
  • 109
Date Created
  • 1987-12
Abstract
  • Doan, Litterman, and Sims (DLS) have suggested using conditional forecasts to do policy analysis with Bayesian vector autoregression (BVAR) models. Their method seems to violate the Lucas critique, which implies that coefficients of a BVAR model will change when there is a change in policy rules. In this paper we construct a BVAR macro model and attempt to determine whether the Lucas critique is important quantitatively. We find evidence following two candidate policy rule changes of significant coefficient instability and of a deterioration in the performance of the DLS method.

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Date Modified
  • 11/08/2019
Corporate Author
  • Federal Reserve Bank of Minneapolis. Research Department
Publisher
  • Federal Reserve Bank of Minneapolis
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