Multi-horizon inflation forecasts using disaggregated data

Carlos Capistrán (), Christian Constandse and Manuel Ramos-Francia
Authors registered in the RePEc Author Service: Manuel Ramos Francia

Economic Modelling, 2010, vol. 27, issue 3, 666-677

Abstract: In this paper we use multi-horizon evaluation techniques to produce monthly inflation forecasts for up to twelve months ahead. The forecasts are based on individual seasonal time series models that consider both, deterministic and stochastic seasonality, and on disaggregated Consumer Price Index (CPI) data. After selecting the best forecasting model for each index, we compare the individual forecasts to forecasts produced using two methods that aggregate hierarchical time series, the bottom-up method and an optimal combination approach. Applying these techniques to 16 indices of the Mexican CPI, we find that the best forecasts for headline inflation are able to compete with those taken from surveys of experts.

Keywords: Aggregated; forecasts; Bottom-up; forecasting; Forecast; combination; Hierarchical; time; series; Inflation; targeting; Seasonal; unit; roots (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (27)

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