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Charemza Diaz Makarova Quasi ex-ante Revised December 2017.pdf (542.84 kB)

Quasi Ex Ante Inflation Forecast Uncertainty

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journal contribution
posted on 2018-01-30, 15:43 authored by Wojciech 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

issn

0169-2070

Acceptance date

2018-01-23

Copyright date

2019

Publisher version

https://www.sciencedirect.com/science/article/pii/S0169207019300512

Notes

The file associated with this record is under embargo until 24 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above.

Temporal coverage: start date

2017-12-16

Temporal coverage: end date

2017-12-18

Language

en

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