Charemza Diaz Makarova Quasi ex-ante Revised December 2017.pdf (542.84 kB)
Quasi Ex Ante Inflation Forecast Uncertainty
journal contributionposted 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.
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.
CitationInternational Journal of Forecasting, 2019, 35(3), pp. 994-1007
Author affiliation/Organisation/COLLEGE OF SOCIAL SCIENCES, ARTS AND HUMANITIES/School of Business
Source11th International Conference on Computational and Financial Econometrics (CFE 2017), University of London, UK
- AM (Accepted Manuscript)