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

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  • Kausik Chaudhuri
  • Minjoo Kim
  • Yongcheol Shin

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.

Suggested Citation

  • Kausik Chaudhuri & Minjoo Kim & Yongcheol Shin, 2016. "Forecasting distributions of inflation rates: the functional auto-regressive approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(1), pages 65-102, January.
  • Handle: RePEc:bla:jorssa:v:179:y:2016:i:1:p:65-102
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    File URL: http://hdl.handle.net/10.1111/rssa.2016.179.issue-1
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