Risultati della ricerca
Creator: Miller, Preston J. and Roberds, William Series: Staff report (Federal Reserve Bank of Minneapolis. Research Department) Number: 109 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.
Parola chiave: Coefficient instability, Bayesian vector autoregression, and Conditional forecasts
Creator: Chin, Daniel M., Geweke, John, and Miller, Preston J. Series: Staff report (Federal Reserve Bank of Minneapolis. Research Department) Number: 267 Abstract:
This paper presents a new method for predicting turning points. The paper formally defines a turning point; develops a probit model for estimating the probability of a turning point; and then examines both the in-sample and out-of-sample forecasting performance of the model. The model performs better than some other methods for predicting turning points.