Multi-horizon inflation forecasts using disaggregated data
AbstractIn 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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Bibliographic InfoArticle provided by Elsevier in its journal Economic Modelling.
Volume (Year): 27 (2010)
Issue (Month): 3 (May)
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Web page: http://www.elsevier.com/locate/inca/30411
Aggregated forecasts Bottom-up forecasting Forecast combination Hierarchical time series Inflation targeting Seasonal unit roots;
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