@techreport{NBERw1202,
title = "Forecasting and Conditional Projection Using Realistic Prior Distributions",
author = "Thomas Doan and Robert B. Litterman and Christopher A. Sims",
institution = "National Bureau of Economic Research",
type = "Working Paper",
series = "Working Paper Series",
number = "1202",
year = "1983",
month = "September",
doi = {10.3386/w1202},
URL = "http://www.nber.org/papers/w1202",
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 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.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 inevaluating causal hypotheses, without containing any such hypotheses themselves.},
}