Working Paper: NBER ID: w1202
Authors: Thomas Doan; Robert Litterman; Christopher A. Sims
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.
Keywords: Bayesian methods; Vector autoregressions; Macroeconomic forecasting
JEL Codes: C11; C52; E37
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Bayesian method (C11) | improved forecasts (C53) |
| interaction among variables (C39) | captured by estimates (C13) |
| Bayesian model (C11) | policy analysis capability (D78) |
| model (C59) | characterization of interdependence without direct causality (C69) |