Forecasting Distribution of Inflation Rates: Functional Autoregressive Approach
Forthcoming in Journal of the Royal Statistical Society: Series A.
45 Pages Posted: 20 Feb 2010 Last revised: 26 Feb 2015
Date Written: November 30, 2014
In line with the recent developments on the statistical analysis of functional data, we develop the semiparametric functional autoregressive (FAR) modeling approach to the density forecasting analysis of national inflation rates 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 autoregressive models and their statistical validity. The fan-chart analysis and the probability event forecasting exercise provide a further support for our approach in a qualitative sense, revealing that the modified FAR models can provide a complementary tool for generating the density forecast of inflation, and analyse the performance of the central bank in achieving announced inflation target. As inflation targeting monetary policies are usually set with recourse to the medium-term forecasts, our proposed work may provide policymakers with an invaluably enriched information set.
Keywords: Time-varying Cross-sectional Distribution, Functional Autoregression, Nonparametric Bootstrap, Density and Probability Forecasting of the UK Inflation
JEL Classification: C14, C53, E31
Suggested Citation: Suggested Citation