The quantitative significance of the Lucas critique
AbstractDoan, 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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Bibliographic InfoPaper provided by Federal Reserve Bank of Minneapolis in its series Staff Report with number 109.
Date of creation: 1987
Date of revision:
Publication status: Published in Journal of Business and Economic Statistics (Vol.9, n.4, October 1991, pp. 361-387)
Other versions of this item:
- Miller, Preston J & Roberds, William T, 1991. "The Quantitative Significance of the Lucas Critique," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(4), pages 361-87, October.
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