Forecasting Inflation in Mexico using factor models: Do disaggregated CPI data improve forecast accuracy?

Arbeitspapier

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.

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• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Papers ; No. 2010-01

Classification and Topics

• Classification: Wirtschaft ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Forecasting Models; Simulation Methods ; Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
• Subject: Factor models ; Inflation forecasting ; Disaggregate information ; Principal components ; Forecast evaluation

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Ibarra-Ramírez, Raúl
• Event: Veröffentlichung
• (who): Banco de México
• (where): Ciudad de México
• (when): 2010

Further information

• Handle: http://hdl.handle.net/10419/83700