Optimal control theory can be combined with the probability structure of a vector autoregression to investigate the tradeoffs available to policymakers. Such an approach obtains results based on a minimal set of assumptions about the economy and the structure of policy actions. This paper takes this approach to analyze the potential effectiveness of countercyclical monetary policy.

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 1963:3* We also describe how a model such as this can be used to make conditional projections and to analyse 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 in evaluating causal hypotheses, without containing any such hypotheses themselves.

Using optimal control theory and a vector autoregressive representation of the relationship between money and interest rates one can derive a feedback control procedure which defines the best possible tradeoff between interest rate volatility and money supply fluctuations and which could be used to reduce both from their current levels.