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Using Seasonal Models to Forecast Short-Run Inflation in Mexico

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Author Info
Carlos Capistrán
Christian Constandse
Manuel Ramos Francia

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Abstract

Since the adoption of inflation targeting, the seasonal appears to be the component that explains the major part of inflation's total variation in Mexico. In this context, we study the performance of seasonal time series models to forecast short-run inflation. Using multi-horizon evaluation techniques, we examine the real-time forecasting performance of four well-known seasonal models using data on 16 indices of the Mexican Consumer Price Index (CPI), including headline and core inflation. These models consider both, deterministic and stochastic seasonality. After selecting the best forecasting model for each index, we apply and compare two methods that aggregate hierarchical time series, the bottom-up method and an optimal combination approach. The best forecasts are able to compete with those taken from surveys of experts.

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File URL: http://www.banxico.org.mx/documents/%7BC394E560-3F85-FE01-0EF0-7560FA06AB9A%7D.pdf
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Publisher Info
Paper provided by Banco de México in its series Working Papers with number 2009-05.

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Date of creation: Jul 2009
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Handle: RePEc:bdm:wpaper:2009-05

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Web page: http://www.banxico.org.mx
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Related research
Keywords: Aggregated forecasts; bottom-up forecasting; forecast combination; hierarchical time series; inflation targeting; multi-horizon evaluation; seasonal unit roots.;

Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation

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References listed on IDEAS
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  3. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-84, September. [Downloadable!] (restricted)
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  4. Rob J. Hyndman & Roman A. Ahmed & George Athanasopoulos, 2007. "Optimal combination forecasts for hierarchical time series," Monash Econometrics and Business Statistics Working Papers 9/07, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
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  6. Hylleberg, S. & Engle, R. F. & Granger, C. W. J. & Yoo, B. S., 1990. "Seasonal integration and cointegration," Journal of Econometrics, Elsevier, vol. 44(1-2), pages 215-238. [Downloadable!] (restricted)
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  7. 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. [Downloadable!]
  8. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May. [Downloadable!] (restricted)
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  9. Hubrich, Kirstin, 2005. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," International Journal of Forecasting, Elsevier, vol. 21(1), pages 119-136. [Downloadable!] (restricted)
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  10. A. Espasa & E. Senra & R. Albacete, 2002. "Forecasting inflation in the European Monetary Union: A disaggregated approach by countries and by sectors," European Journal of Finance, Taylor and Francis Journals, vol. 8(4), pages 402-421, December. [Downloadable!] (restricted)
  11. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
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  12. Etienne Gagnon, 2007. "Price setting during low and high inflation: evidence from Mexico," International Finance Discussion Papers 896, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
    Other versions:
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