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Forecasting Inflation in Mexico Using Factor Models: Do Disaggregated CPI Data Improve Forecast Accuracy?

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  • Raúl Ibarra-Ramírez

Abstract

In this paper we apply a dynamic factor model to generate out of sample forecasts for the inflation rate in Mexico. We evaluate the role of using a wide range of macroeconomic variables with particular interest on the importance of using CPI disaggregated data to forecast inflation. Our data set contains 54 macroeconomic series and 243 CPI subcomponents from 1988 to 2008. Our results indicate that: i) Factor models outperform the benchmark autoregressive model at horizons of one, two, four and six quarters, ii) Using disaggregated price data improves forecasting performance, and iii) The factors are related to key variables in the economy such as output growth and inflation.

Suggested Citation

  • Raúl Ibarra-Ramírez, 2010. "Forecasting Inflation in Mexico Using Factor Models: Do Disaggregated CPI Data Improve Forecast Accuracy?," Working Papers 2010-01, Banco de México.
  • Handle: RePEc:bdm:wpaper:2010-01
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    Citations

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    Cited by:

    1. Daniel Vaughan, 2013. "An Analysis of the Process of Disinflationary Structural Change: The Case of Mexico," Working Papers 2013-12, Banco de México.
    2. Raul Ibarra & Luis M. Gomez-Zamudio, 2017. "Are Daily Financial Data Useful for Forecasting GDP? Evidence from Mexico," ECONOMIA JOURNAL, THE LATIN AMERICAN AND CARIBBEAN ECONOMIC ASSOCIATION - LACEA, vol. 0(Spring 20), pages 173-203, April.
    3. Afees A. Salisu & Kazeem Isah, 2017. "Predicting US Inflation: Evidence from a New Approach," Working Papers 039, Centre for Econometric and Allied Research, University of Ibadan.
    4. repec:eee:intfor:v:33:y:2017:i:3:p:627-651 is not listed on IDEAS
    5. Moses Tule & Afees A. Salisu & Charles Chimeke, 2018. "You are what you eat: The role of oil price in Nigeria inflation forecast," Working Papers 040, Centre for Econometric and Allied Research, University of Ibadan.
    6. Robinson Durán & Evelyn Garrido & Carolina Godoy & Juan de Dios Tena, 2012. "Predicción de la inflación en México con modelos desagregados por componente," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 27(1), pages 133-167.
    7. repec:eee:ecmode:v:71:y:2018:i:c:p:134-158 is not listed on IDEAS
    8. Cesar Carrera & Alan Ledesma, 2015. "Aggregate Inflation Forecast with Bayesian Vector Autoregressive Models," Working Papers 2015-50, Peruvian Economic Association.
    9. Hyun Hak Kim, 2013. "Forecasting Macroeconomic Variables Using Data Dimension Reduction Methods: The Case of Korea," Working Papers 2013-26, Economic Research Institute, Bank of Korea.
    10. Afees A. Salisu & Kazeem Isah, 2017. "Predicting US CPI-Inflation in the presence of asymmetries, persistence, endogeneity, and conditional heteroscedasticity," Working Papers 026, Centre for Econometric and Allied Research, University of Ibadan.
    11. Carrera, Cesar & Ledesma, Alan, 2015. "Proyección de la inflación agregada con modelos de vectores autorregresivos bayesianos," Working Papers 2015-003, Banco Central de Reserva del Perú.
    12. Blazej Mazur, 2015. "Density forecasts based on disaggregate data: nowcasting Polish inflation," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 15, pages 71-87.

    More about this item

    Keywords

    Factor models; Inflation forecasting; Disaggregate information; Principal components; Forecast evaluation.;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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