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The Quantitative Significance of the Lucas Critique

Preston J. Miller and William T. Roberds
Journal of Business & Economic Statistics
Vol. 9, No. 4 (Oct., 1991), pp. 361-387
DOI: 10.2307/1391237
Stable URL: http://www.jstor.org/stable/1391237
Page Count: 27
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The Quantitative Significance of the Lucas Critique
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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 article, we attempt to determine whether the Lucas critique is important quantitatively in a BVAR macro model that we construct. 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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