RDP 1999-10: The Implications of Uncertainty for Monetary Policy 7. Conclusion

This paper extends Brainard's formulation of policy-making under uncertainty in several directions. First, it generalises the solution of the optimal policy problem to accommodate multiple time-periods and multiple objectives for policy. This generalisation develops the stochastic properties of the equations relating target variables to the policy instrument from the estimated relationships defining the underlying economic model.

Whereas uncertainty about the effectiveness of monetary policy tends to recommend more conservative policy, we explore the intuition for why other forms of parameter uncertainty may actually lead to more aggressive policy. In a simple example, we show that uncertainty about the dynamics of an economy can be a source of additional policy activism. However, this consequence of parameter uncertainty is only relevant in a multi-period generalisation of the Brainard model.

In the context of any specific model, it is an empirical issue to determine the exact implications of parameter uncertainty for monetary policy. We examine this using a small linear model of the Australian economy that captures the key channels of the monetary policy transmission mechanism within an open-economy framework. Optimal policy responses ignoring parameter uncertainty are compared with optimal responses that explicitly take parameter uncertainty into account. While the differences between these policy responses vary with the source of shocks to the economy, our evidence suggests that, for most shocks in our model, parameter uncertainty motivates somewhat more aggressive use of the instrument.

Although the results in this paper are reported as deviations from equilibrium, the method used to construct optimal policy responses under parameter uncertainty is also applicable in a forecasting environment where past data must be taken into account. The approach is applicable to all backward-looking linear models in which the objectives of the policy-maker are quadratic.

The simulations also demonstrate how frequency-sampling techniques can be used to evaluate the analytic expression for optimal policy under parameter uncertainty, despite the presence of complex expectations terms. This approach to policy determination is as practical, and more theoretically appealing, than the application of alternative rules-based approaches.

While the findings of the paper are of considerable interest, they should not be overstated. In particular, the implications of parameter uncertainty are dependent upon the type of shock being accommodated. They are also dependent upon the specification of the model. For example, changes to the model specification could substantially alter the measured uncertainty attached to the effectiveness of policy relative to the measured uncertainty associated with the model's dynamics. If the techniques developed in this paper are to be of wider use, the underlying model must first be well understood and carefully specified. Also, it is worth remembering that, although our model suggests that optimal monetary policy taking account of uncertainty is more activist for most kinds of shocks, the difference in policy response is quite small relative to the degree of conservatism that is actually practiced by most central banks.

In this paper we have not sought to argue that conservative monetary policy is not optimal. In fact, there are probably a number of good reasons why conservative policy may be optimal. Instead, the central message of the paper is that, if we are to motivate conservative monetary policy, then explanations other than Brainard's are required.