This paper presents a new approach to measure the parameter uncertainty for optimal simple
monetary policy rules in the New Keynesian dynamic stochastic general equilibrium models.
More precisely, we propose a new algorithm which enables to directly introduce parameter
uncertainty into the optimal simple precommitment rule problem. As a result we find
distributions of the optimal monetary policy reactions and the minimized welfare losses. To
compare the distributions of the monetary policy parameters and the welfare losses we apply
the first order stochastic dominance ordering (SD1). The SD1 inequality between the
probability distribution is verified by means of the Kolmogorov-Smirnov test. The proposed
algorithms are applied to the Erceg, Henderson and Levine (2000) small-scale closed
economy model estimated for the Polish economy. For the welfare-loss-minimizing central
bank, we examine three types of the dynamic specification of its policy rule: backward-,
current- and forward-looking. Finally, for a given set of optimal and implementable monetary
policy rules, we show that the fully specified forward-looking monetary policy rule with
interest rate smoothing mechanism minimizes the welfare-loss in the sense of the stochastic
ordering SD1.This work was supported by the National Science Centre in Poland under Grant No. 2017/26/D/HS4/00942
