Nowcasting del PIB de México usando modelos de factores y ecuaciones puente

Arbeitspapier

This paper evaluates five Nowcasting models that forecast Mexico's quarterly GDP: a Dynamic Factor Model (MFD), two Bridge Equation Models (BE) and two Principal Components Models (PCA). The results indicate that the average of the BE forecasts is statistically better than the rest of the models under consideration, according to the Diebold-Mariano (1995) accuracy test. In addition, using real-time information, the BE average is found to be more accurate than the median of the forecasts provided by the analysts surveyed by Bloomberg and the median of the experts who answer Banco de México's Survey of Professional Forecasters.

Object Details

• Language: Spanisch

References and Relationships

• Bibliographic citation: Series: Working Papers ; No. 2018-06

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models ; Monetary Policy
• Subject: Nowcasting ; Dynamic Factor Model ; Bridge Equations ; Principal Component Analysis ; Quarterly GDP ; Diebold-Mariano test

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Gálvez-Soriano, Oscar de Jésus
• Event: Veröffentlichung
• (who): Banco de México
• (where): Ciudad de México
• (when): 2018

Further information

• Handle: http://hdl.handle.net/10419/202472
• Last update: 10.03.2025, 11:44 AM CET