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This book provides a thorough survey of the model-based literature on optimal monetary in a stochastic setting. The survey begins with the literature of the 1970s which focused on the information problem in policy design and extends to the New Keynesian approach of the 1990s which centered on evaluating alternative targeting strategies. New to the second edition is consideration of research since the world financial crisis on the role of financial markets and institutions in the conduct of monetary policy.
"We examine optimal and other monetary policies in a linear-quadratic setup with a relatively general form of model uncertainty, so-called Markov jump-linear-quadratic systems extended to include forward-looking variables. The form of model uncertainty our framework encompasses includes: simple i.i.d. model deviations; serially correlated model deviations; estimable regime-switching models; more complex structural uncertainty about very different models, for instance, backward- and forward-looking models; time-varying central-bank judgment about the state of model uncertainty; and so forth. We provide an algorithm for finding the optimal policy as well as solutions for arbitrary policy functions. This allows us to compute and plot consistent distribution forecasts---fan charts---of target variables and instruments. Our methods hence extend certainty equivalence and "mean forecast targeting" to more general certainty non-equivalence and "distribution forecast targeting.""--National Bureau of Economic Research web site
This paper uses a simple model of the Australian economy to empirically examine the consequences of parameter uncertainty for optimal monetary policy. Optimal policy responses are derived for a monetary authority that targets inflation and output stability. Parameter uncertainty is characterised by the estimated distribution of the model coefficient estimates. Learning is ruled out, so the monetary authority can commit to its ex ante policy response. For certain shocks, taking account of parameter uncertainty can recommend more, rather than less, activist use of the policy instrument. While this finding is specific to the model specification, parameter estimates and the shocks analysed, it contrasts with the widely held belief that the generic implication of parameter uncertainty is a more conservative policy.
This paper characterizes a robust optimal policy rule in a simple forward-looking model, when the policymaker faces uncertainty about model parameters and shock processes. We show that the robust optimal policy rule is likely to involve a stronger response of the interest rate to fluctuations in inflation and the output gap than is the case in the absence of uncertainty. Thus parameter uncertainty alone does not necessarily justify a small response of monetary policy to perturbations. However uncertainty may amplify the degree of "super-inertia" required by optimal monetary policy. We finally discuss the sensitivity of the results to alternative assumptions.
The form of bounded rationality characterizing the representative agent is key in the choice of the optimal monetary policy regime. While inflation targeting prevails for myopia that distorts agents' inflation expectations, price level targeting emerges as the optimal policy under myopia regarding the output gap, revenue, or interest rate. To the extent that bygones are not bygones under price level targeting, rational inflation expectations is a minimal condition for optimality in a behavioral world. Instrument rules implementation of this optimal policy is shown to be infeasible, questioning the ability of simple rules à la Taylor (1993) to assist the conduct of monetary policy. Bounded rationality is not necessarily associated with welfare losses.