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Multi-horizon inflation forecasts using disaggregated data

  • Capistrán, Carlos
  • Constandse, Christian
  • Ramos-Francia, Manuel

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.

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Article provided by Elsevier in its journal Economic Modelling.

Volume (Year): 27 (2010)
Issue (Month): 3 (May)
Pages: 666-677

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Handle: RePEc:eee:ecmode:v:27:y:2010:i:3:p:666-677
Contact details of provider: Web page: http://www.elsevier.com/locate/inca/30411

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  17. Franses, Philip Hans, 1996. "Periodicity and Stochastic Trends in Economic Time Series," OUP Catalogue, Oxford University Press, number 9780198774549.
  18. Capistran, Carlos, 2006. "On comparing multi-horizon forecasts," Economics Letters, Elsevier, vol. 93(2), pages 176-181, November.
  19. Daniel Chiquiar & Antonio Noriega & Manuel Ramos-Francia, 2010. "A time-series approach to test a change in inflation persistence: the Mexican experience," Applied Economics, Taylor & Francis Journals, vol. 42(24), pages 3067-3075.
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