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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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  1. James H. Stock & Mark W. Watson, 2006. "Why Has U.S. Inflation Become Harder to Forecast?," NBER Working Papers 12324, National Bureau of Economic Research, Inc.
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  14. Etienne Gagnon, 2009. "Price Setting During Low and High Inflation: Evidence from Mexico," The Quarterly Journal of Economics, MIT Press, vol. 124(3), pages 1221-1263, August.
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  17. James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
  18. Philip Hans Franses, 2007. "Constant vs. Changing Seasonality," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 6, pages 24-25, Spring.
  19. Duarte, Claudia & Rua, Antonio, 2007. "Forecasting inflation through a bottom-up approach: How bottom is bottom?," Economic Modelling, Elsevier, vol. 24(6), pages 941-953, November.
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