Forecasting And Conditional Projection Using Realistic Prior Distributions

Thomas Doan, Robert Litterman, Christopher Sims

Research output: Contribution to journalArticlepeer-review

768 Scopus citations


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. 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.

Original languageEnglish (US)
Pages (from-to)1-100
Number of pages100
JournalEconometric Reviews
Issue number1
StatePublished - Jan 1 1984

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics


  • Bayesian analysis
  • conditional projections:
  • forecasting
  • macroeconomic modeling
  • vector autoregressions


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