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The Quantitative Significance of the Lucas Critique

Staff Report 109 | Published December 1, 1987

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Preston J. Miller Former Vice President and Monetary Adviser
The Quantitative Significance of the Lucas Critique


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.

Published in: _Journal of Business and Economic Statistics_ (Vol. 9, No. 4, October 1991, pp. 361-387) Published in: _The legacy of Robert Lucas, Jr._ (Vol. 2, 1999, pp. 449-475)