Creator: Doan, Thomas., Litterman, Robert B., and Sims, Christopher A. Series: Working paper (Federal Reserve Bank of Minneapolis. Research Dept.) Number: 243 Abstract:
This paper develops a forecasting procedure based on a Bayesian method for estimating vector autoregressions. The procedure is applied to ten macroeconomic variables and is shown to improve out-of-sample forecasts relative to univariate equations. Although cross-variables responses are damped by the prior, considerable interaction among the variables is shown to be captured by the estimates. We provide unconditional forecasts as of 1982:12 and 1963:3* We also describe how a model such as this can be used to make conditional projections and to analyse policy alternatives. As an example, we analyze a Congressional Budget Office forecast made in 1982:12. While no automatic causal interpretations arise from models like ours, they provide a detailed characterization of the dynamic statistical interdependence of a set of economic variables, which may help in evaluating causal hypotheses, without containing any such hypotheses themselves.
Keyword: Bayesian methods, Forecasting, and Macroeconomics Subject (JEL): C11 - Econometric and statistical methods : General - Bayesian analysis and E27 - Macroeconomics : Consumption, saving, production, employment, and investment - Forecasting and simulation