The quantitative significance of the Lucas critique

Preston J. Miller and William Roberds

No 109, Staff Report from Federal Reserve Bank of Minneapolis

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.

Keywords: Vector autoregression; Forecasting (search for similar items in EconPapers)
Date: 1987
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Citations: View citations in EconPapers (6)

Published in Journal of Business and Economic Statistics (Vol.9, n.4, October 1991, pp. 361-387)

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Journal Article: The Quantitative Significance of the Lucas Critique (1991)
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedmsr:109

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