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

Kausik Chaudhuri, Minjoo Kim () and Yongcheol Shin

Journal of the Royal Statistical Society Series A, 2016, vol. 179, issue 1, 65-102

Abstract: type="main" xml:id="rssa12109-abs-0001">

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.

Date: 2016
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