T. 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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Volume (Year): 9 (1991) Issue (Month): 4 (October) Pages: 361-87 Download reference. The following formats are available: HTML
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Daniel M. Chin & John F. Geweke & Preston J. Miller, 2000.
"Predicting turning points,"
Staff Report
267, Federal Reserve Bank of Minneapolis.
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