Forecasting distributions of inflation rates: the functional auto-regressive approach

Chaudhuri, Kausik, Kim, Minjoo ORCID: 0000-0002-5454-2257 and Shin, Yongcheol (2016) Forecasting distributions of inflation rates: the functional auto-regressive approach. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, 179 (1). pp. 65-102.

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Official URL: https://rss.onlinelibrary.wiley.com/doi/full/10.11...

Abstract

In line with recent developments in the statistical analysis of functional data, we develop the semiparametric functional auto‐regressive modelling approach to the density forecasting analysis of national rates of inflation by using sectoral inflation rates in the UK over the period January 1997–September 2013. The pseudo‐out‐of‐sample forecasting evaluation and test results provide an overall support to superior performance of our proposed models over the aggregate auto‐regressive models and their statistical validity. The fan chart analysis and the probability event forecasting exercise provide further support for our approach in a qualitative sense, revealing that the modified functional auto‐regressive models can provide a complementary tool for generating the density forecast of inflation, and for analysing the performance of a central bank in achieving announced inflation targets. As inflation targeting monetary policies are usually set with recourse to the medium‐term forecasts, our proposed work may provide policy makers with an invaluably enriched information set.

Item Type:	Article
Additional Information:	Source info: Forthcoming in Journal of the Royal Statistical Society: Series A.
Uncontrolled Keywords:	Density and probability forecasting of UK inflation, Functional auto-regression, Non-parametric bootstrap, Time varying cross-sectional distribution
Depositing User:	Symplectic Admin
Date Deposited:	29 Oct 2018 12:22
Last Modified:	19 Jan 2023 01:13
DOI:	10.1111/rssa.12109
Open Access URL:	http://eprints.gla.ac.uk/100029/
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3028127