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Forecasting distributions of inflation rates: the functional auto‐regressive approach
Author(s) -
Chaudhuri Kausik,
Kim Minjoo,
Shin Yongcheol
Publication year - 2016
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12109
Subject(s) - inflation (cosmology) , econometrics , autoregressive model , set (abstract data type) , economics , computer science , monetary policy , macroeconomics , physics , theoretical physics , programming language
Summary 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.