This paper proposes an estimation method for persistent and transitory monetary shocks using the monetary policy modeling proposed in Andolfatto et al, [Journal of Monetary Economics, 55 (2008), pp.: 406-422]. The contribution of the paper is threefold: a) to deal with non-Gaussian innovations, we consider a convenient reformulation of the state-space representation that enables us to use the Kalman filter as an optimal estimation algorithm. Now the state equation allows expectations play a significant role in explaining the future time evolution of monetary shocks; b) it offers the possibility to perform maximum likelihood estimation for all the parameters involved in the monetary policy, and c) as a consequence, we can estimate the conditional probability that a regime change has occurred in the current period given an observed monetary shock. Empirical evidence on US monetary policy making is provided through the lens of a Taylor rule, suggesting that the Fed’s policy was implemented accordingly with the macroeconomic conditions after the Great Moderation. The use of the particle filter produces similar quantitative and qualitative findings. However, our procedure has much less computational cost.Kalman filter, Non-normality, Particle filter, Monetary policy
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