Doan, Thomas; Litterman, Robert; Sims, Christopher Forecasting and conditional projection using realistic prior distributions (with discussion). (English) Zbl 0613.62142 Econom. Rev. 3, 1-144 (1984). This paper develops a forecasting procedure based on a Bayesian method for estimating vector autoregressions. The procedure is applied to 10 macroeconomic variables and is shown to improve out-of-sample forecasts relative to univariate equations. Although cross-variable 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 1983:3. We also describe how a model such as this can be used to make conditional projections and to analyze policy alternatives. As an example, we analyze a Congressional Budget Office forecast made in 1982:12. Although 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, information that may help in evaluating causal hypotheses without containing any such hypotheses. Cited in 1 ReviewCited in 75 Documents MSC: 62P20 Applications of statistics to economics 62F15 Bayesian inference 62M20 Inference from stochastic processes and prediction 91B84 Economic time series analysis Keywords:forecasting; vector autoregressions; conditional projections; causal hypotheses PDF BibTeX XML Cite \textit{T. Doan} et al., Econom. Rev. 3, 1--144 (1984; Zbl 0613.62142) Full Text: DOI OpenURL