Replication data for: Designing Optimal Macroeconomic Policy Rules under Parameter Uncertainty: A Stochastic Dominance Approach

Published: 25 August 2021| Version 2 | DOI: 10.17632/y4vntp5nvx.2


Research data associated with the manuscript: [1] Górajski, M., Kuchta, Z., 2021, Designing Optimal Macroeconomic Policy Rules under Parameter Uncertainty: A Stochastic Dominance Approach. This work is supported by the National Science Centre in Poland under Grant No. 2017/26/D/HS4/00942. It contains all user-defined MATLAB and R functions that implement our algorithms from Section 4 and replicate all results. We group them into seven folders: 1. main_data It performs the data preparation process. 2. main_estimation It estimates 24 versions of the Erceg, Henderson, and Levine (2000) small-scale DSGE model (EHL model). They differ by the Talor-type rule. We consider eight sets of the response variables in the policy rule (see Table 1) with three different dynamic specifications: backward-looking, cotemporaneous and forward-looking. 3. main_measuring_uncertainty It evaluates the MWL and OPFC distributions for 24 versions of the EHL model. 4. main_compare_losses It contains the novel EP Bayesian tests for the SDk relations from Section 4.2. We use these tests to compare the MWLs. 5. main_robust_simple_rules It replicates all Bayesian and min-max robust strategies from Section 4. 6. main_simulations It collects all codes that perform the simulations of the EHL model with SDk-optimal and estimated policy rules. 7. main_performance_BayesEP_SDk_tests It assesses the performance of the EP Bayesian tests for SDk relations. Abstract This paper offers a Bayesian decision-theoretic approach to policy evaluation in rational expectation models. First, we show how to correctly assess and rank simple policy rules under the welfare loss minimization criterion in the presence of uncertainty about the model's structural parameters. We consider a Bayesian policymaker who assesses the effectiveness of policy actions by comparing the distributions of welfare losses using stochastic dominance orderings. Second, we propose a new Bayesian testing procedure for verifying the k-degree stochastic dominance relation. Third, we apply our approach to a dynamic stochastic general equilibrium model, estimated for the U.S. economy. We show that using stochastic dominance to rank simple policy rules yields different rankings than using well-established robust approaches. The contemporaneous monetary policy rule that reacts to inflation and the output gap, with an interest rate smoothing mechanism, minimises the welfare loss for all decision-makers who admit infinite degree stochastic dominance preferences.


Steps to reproduce

see the ReadMe file


Uniwersytet Lodzki


Uncertainty, Macroeconomic Policy, Robust Control, Bayesian Analysis