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. Authors
provided uniconditional forecasts as 1982:12 and 1983:3. They also describes how
such as this can be used to make conditional projections and to analyze policy alternatives.
As an example, they analyzed a Congressional Budget Office forecast made in 1982:12.
While no automatic casual interpretations arise from models like ours, they provide
a detailed characterization of the dynamic statistical interdependence of a set of
economic variable, which may help in evaluating casual hypoteses, without containing
any such hypotheses themselves.
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