Forecasting inflation under varying frequencies

Emmanuel Sirimal Silva*, Hossein Hassani, Jesús Otero, Christina Beneki

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
11 Downloads (Pure)

Abstract

This paper seeks to determine the impact of monthly and annual data frequencies on the accuracy of inflation forecasts attainable via econometric and subspace-based methods. The application considers food inflation across short and long run horizons in Colombia, a country with an inflation targeting regime. The data includes all 54 components of the food consumer price index (CPI) in Colombia from Jan. 1999 Oct. 2012, and the study forecasts the food CPI, and inflation using the parametric and nonparametric techniques of ARIMA, Exponential Smoothing (ETS), Holt-Winters (HW) and Singular Spectrum Analysis (SSA). We find that when forecasting the index, ARIMA forecasts are on average best, whilst for monthly inflation forecasting SSA is comparatively better and for annual, the results vary between SSA and ARIMA. These statistically significant findings give policy makers an option to select an apt forecasting model which suits their requirements.
Original languageEnglish
Pages (from-to)307-339
Number of pages33
JournalElectronic Journal of Applied Statistical Analysis
Volume11
Issue number1
Early online date26 Apr 2018
DOIs
Publication statusPublished - Apr 2018
Externally publishedYes

Keywords

  • ARIMA
  • Exponential Smoothing
  • Food inflation
  • Forecasting
  • Holt-Winters
  • Singular Spectrum Analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation

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