Title: Learning and loss functions: comparing optimal and operational monetary policy rules

Authors: Eric Gaus; Srikanth Ramamurthy

Addresses: Ursinus College, 601 East Main St., Collegeville, PA 19426-1000, USA ' Sellinger School of Business, Loyola University Maryland, 4501, N. Charles Street, Baltimore, MD 21210, USA

Abstract: Modern Bayesian tools aided by MCMC techniques allow researchers to estimate models with increasingly intricate dynamics. This paper highlights the application of these tools with an empirical assessment of optimal versus operational monetary policy rules within a standard new Keynesian macroeconomic model with adaptive learning. The question of interest is which of the two policy rules - contemporaneous data or expectations of current variables - better describes the policy undertaken by the US central bank. Results for the data period 1954:III to 2007:I indicate that the data strongly favours contemporaneous expectations over real time data.

Keywords: adaptive learning; rational expectations; Bayesian econometrics; Markov chain Monte Carlo; MCMC; loss functions; monetary policy rules; dynamic modelling; Keynesian macroeconomics; contemporaneous expectations; real time data.

DOI: 10.1504/IJMMNO.2014.059941

International Journal of Mathematical Modelling and Numerical Optimisation, 2014 Vol.5 No.1/2, pp.64 - 78

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 20 Mar 2014 *

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