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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 study the design of optimal monetary policy under uncertainty in a dynamic stochastic general equilibrium models. We use a Markov jump-linear-quadratic (MJLQ) approach to study policy design, approximating the uncertainty by different discrete modes in a Markov chain, and by taking mode-dependent linear-quadratic approximations of the underlying model. This allows us to apply a powerful methodology with convenient solution algorithms that we have developed. We apply our methods to a benchmark New Keynesian model, analyzing how policy is affected by uncertainty, and how learning and active experimentation affect policy and losses.
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.