The results show that optimal budgetary policies for the particular model employed are very sensitive with respect to stochastics affecting fiscal policy multipliers and especially to correlations of these parameters with other ones. This is also true if some other parameters are made stochastic, such as the coefficients of lagged endogenous variables. On the other hand, if the full covariance matrix of the parameters is taken into account, optimal policies are similar to those obtained in the deterministic case. We interpret this result as showing the reliability of optimal policy recommendations when the full covariance matrix is either taken into account or totally neglected, i.e., deterministic optimal policies can be relied on even in a world of uncertainty. On the other hand, neglecting correlations between model parameters leads to policy recommendations which ought not to be presented to policy makers as they are seriously flawed. This result is important for practical policy advisers because often econometric models are estimated by OLS and hence no estimate for the entire parameter covariance matrix is available. In this case, results based on deterministic optimization are to be preferred to those based on rudimentary stochastics.
From a more general perspective, if we compare the results of the deterministic or the fully stochastic optimization run to a simulation with extrapolations of policy instruments used as inputs, optimal policies turn out to be more countercyclical and dampen the amplitude of business cycle fluctuations. If this is in fact a goal of economic policy making, using an optimum control approach within a framework of quantitative economic policy could be recommended to political decision makers and their advisers as an instrument to generate insights into possibilities for improving policy making. In particular, discretionary countercyclical budgetary policies can lead to improvements with respect to macroeconomic target variables. In order to obtain reliable recommendations for such policies, parameter uncertainty should be either fully taken into account or, if this is not feasible, neglected completely.