The Quantitative Significance of the Lucas Critique
AbstractT. Doan, R. Litterman, and C. Sims have suggested using conditional forecasts to do policy analysis with Bayesian vector autoregression models. Their method seems to violate the Lucas critique, which implies that coefficients of a Bayesian vector autoregression model will change when there is a change in policy rules. In this article, the authors attempt to determine whether the Lucas critique is important quantitatively in a Bayesian vector autoregression macro model that they construct. They find evidence following two candidate policy rule changes of significant coefficient instability and of a deterioration in the performance of the Doan, Litterman, and Sims method.
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Bibliographic InfoArticle provided by American Statistical Association in its journal Journal of Business and Economic Statistics.
Volume (Year): 9 (1991)
Issue (Month): 4 (October)
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- Preston J. Miller & William Roberds, 1987. "The quantitative significance of the Lucas critique," Staff Report 109, Federal Reserve Bank of Minneapolis.
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