posted on 2018-01-30, 15:43authored byWojciech Charemza, Carlos Díaz, Svetlana Makarova
We propose a measure of the effects of monetary policy based on analysis of the distribution
of ex-post inflation forecast uncertainty. We argue that the difference between the
distributions of the ex-ante and ex-post uncertainties reflects the impact of monetary policy
decisions. Using the theoretical background of the New Keynesian model with imperfect
information and a monetary policy rule, we derive a proxy for ex-ante inflation uncertainty
called quasi ex-ante forecast uncertainty, which is to an extent free of the effects of monetary
policy decisions. It is computed using the parameters of a weighted skew-normal distribution
fitted to forecast errors. Further, we introduce the compound strength measure of monetary
policy and the uncertainty ratio, which approximates the impact that monetary policy has on
reducing inflation forecast uncertainty. A nonlinear relationship is found between compound
strength and the independence of central banks for 38 countries. The quasi ex-ante forecast
uncertainty is applied for the BRICS countries (Brazil, Russia, India, China and South
Africa), the UK and the US. It is concluded that the greatest policy effect in reducing
inflation forecast uncertainty occurs for countries which conduct either a well-established and
relatively pure inflation targeting policy, like South Africa and the UK, or clandestine
inflation targeting, like India and the US. The smallest reduction is for countries like China
and Russia that mix inflation targeting with exchange rate stabilisation.
Funding
Financial support of the ESRC/ORA project RES-360-25-0003
Probabilistic Approach to Assessing Macroeconomic Uncertainties and the Opus 8 project
2014/15/B/HS4/04263 Modelling macroeconomic uncertainty of the National Science Centre
in Poland is gratefully acknowledged. This research used the ALICE High Performance
Computing Facility at the University of Leicester. We also thank Cristina Bodea for sharing
her data with us.
History
Citation
International Journal of Forecasting, 2019, 35(3), pp. 994-1007
Author affiliation
/Organisation/COLLEGE OF SOCIAL SCIENCES, ARTS AND HUMANITIES/School of Business
Source
11th International Conference on Computational and Financial Econometrics (CFE 2017), University of London, UK
Version
AM (Accepted Manuscript)
Published in
International Journal of Forecasting
Publisher
Elsevier for International Institute of Forecasters
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