01984cam a22002537 4500001000600000003000500006005001700011008004100028100001800069245016000087260006600247490004100313500002000354520094600374530006101320538007201381538003601453700002501489700002501514710004201539830007601581856003701657856003601694w1202NBER20171021032516.0171021s1983 mau||||fs|||| 000 0 eng d1 aDoan, Thomas.10aForecasting and Conditional Projection Using Realistic Prior Distributionsh[electronic resource] /cThomas Doan, Robert B. Litterman, Christopher A. Sims. aCambridge, Mass.bNational Bureau of Economic Researchc1983.1 aNBER working paper seriesvno. w1202 aSeptember 1983.3 aThis 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. aHardcopy version available to institutional subscribers. aSystem requirements: Adobe [Acrobat] Reader required for PDF files. aMode of access: World Wide Web.1 aLitterman, Robert B.1 aSims, Christopher A.2 aNational Bureau of Economic Research. 0aWorking Paper Series (National Bureau of Economic Research)vno. w1202.4 uhttp://www.nber.org/papers/w120241uhttp://dx.doi.org/10.3386/w1202