Report

The quantitative significance of the Lucas critique

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;

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

Bibliographic Information

Provider: Federal Reserve Bank of Minneapolis

Part of Series: Staff Report

Publication Date: 1987

Number: 109