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

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JournalJournal of the Royal Statistical Society: Series A (Statistics in Society)
DateE-pub ahead of print - 2 Mar 2015
DatePublished (current) - Jan 2016
Issue number1
Volume179
Number of pages38
Pages (from-to)65-102
Early online date2/03/15
Original languageEnglish

Abstract

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.

    Research areas

  • Time-varying Cross-sectional Distribution, Functional Autoregression, Nonparametric Bootstrap, Density and Probability Forecasting of the UK Inflation.

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