Estimating the effect of central bank independence on inflation using longitudinal targeted maximum likelihood estimation

Journal of Causal Inference

article

Philipp F. M. Baumann¹  Michael Schomaker²  Enzo Rossi⁴ 

[1] KOF Swiss Economic Institute, Department of Management;Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT - University for Health Sciences;Centre for Infectious Disease Epidemiology & Research, School of Public Health and Family Medicine, University of Cape Town;Swiss National Bank, Department I

关键词: causal inference;    doubly robust;    super learning;    macroeconomics;    monetary policy;   

DOI  :  10.1515/jci-2020-0016

来源: De Gruyter

【 摘 要 】

The notion that an independent central bank reduces a country’s inflation is a controversial hypothesis. To date, it has not been possible to satisfactorily answer this question because the complex macroeconomic structure that gives rise to the data has not been adequately incorporated into statistical analyses. We develop a causal model that summarizes the economic process of inflation. Based on this causal model and recent data, we discuss and identify the assumptions under which the effect of central bank independence on inflation can be identified and estimated. Given these and alternative assumptions, we estimate this effect using modern doubly robust effect estimators, i.e., longitudinal targeted maximum likelihood estimators. The estimation procedure incorporates machine learning algorithms and is tailored to address the challenges associated with complex longitudinal macroeconomic data. We do not find strong support for the hypothesis that having an independent central bank for a long period of time necessarily lowers inflation. Simulation studies evaluate the sensitivity of the proposed methods in complex settings when certain assumptions are violated and highlight the importance of working with appropriate learning algorithms for estimation.

【 授权许可】

CC BY