Replication data for: Price-setting heterogeneity and robust monetary policy in a two-sector DSGE model of a small open economy

Published: 8 November 2022| Version 2 | DOI: 10.17632/ngwg444vpr.2
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Description

Research data associated with the manuscript: [1] Górajski, M., Kuchta, Z., Leszczyńska-Paczesna, A., 2022, Price-setting heterogeneity and robust monetary policy in a two-sector DSGE model of a small open economy The Readme file describes all user-defined MATLAB functions that solve the robust monetary policy rules and replicate the main results from Section 4. We group them into four folders: main_estimation, main_robust_simple_rules, main_sensitivity_analysis, and main_simulations. This work is supported by the National Science Centre in Poland under Grant No. 2017/26/D/HS4/00942. Abstract This paper offers a welfare analysis of robust simple monetary policy rules in a multi-sector dynamic stochastic general equilibrium model of a small open economy. The model assumes price-setting heterogeneity between two sectors of the economy: the production of food and energy goods, and the remaining consumption goods and services. We determine monetary policy rules that minimise the Bayesian risk and take into account the uncertainty of the economic environment. Using this approach we propose a robust price index. To illustrate an application, we estimate the model on Polish data and compare expected welfare losses under implementable monetary policy rules. We show that reacting to core inflation improves social welfare more than responding to headline inflation. Moreover, the choice between the robust headline and core inflation rules may depend on country-specific factors, such as the share of food and energy in a consumption bundle or the level of competitiveness.

Files

Steps to reproduce

See the Readme file.

Institutions

Uniwersytet Lodzki

Categories

Monetary Policy, Robust Control, Inflation, Economic Uncertainty, Bayesian Analysis, Open Economy Macroeconomics, Dynamic Stochastic General Equilibrium

Funding

Narodowe Centrum Nauki

Grant No. 2017/26/D/HS4/00942

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