Optimal Monetary Policy Under Uncertainty in DSGE Models: A Markov Jump-Linear-Quadratic Approach

We study the design of optimal monetary policy under uncertainty in a dynamic stochastic general equilibrium model. We use a Markov jump-linear-quadratic (MJLQ) approach to study policy design, proxying 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 testing affect policy and losses.

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  1. Zampolli, Fabrizio, 2006. " Optimal monetary policy in a regime-switching economy: The response to abrupt shifts in exchange rate dynamics ," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1527-1567.

Citations

    Edward Herbst & David Lopez-Salido & Christopher Gust, 2017. " Forward Guidance with Bayesian Learning and Estimation ," 2017 Meeting Papers 1189, Society for Economic Dynamics.

Most related items

  1. Lars E. O. Svensson & Noah Williams, 2008. " Optimal monetary policy under uncertainty: a Markov jump-linear-quadratic approach ," Review, Federal Reserve Bank of St. Louis, vol. 90(Jul), pages 275-294.
  2. Svensson, Lars E. O. & Williams, Noah, 2006. " Bayesian and adaptive optimal policy under model uncertainty ," CFS Working Paper Series 2007/11, Center for Financial Studies (CFS).

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