Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/202472
Authors: 
Gálvez-Soriano, Oscar de Jésus
Year of Publication: 
2018
Series/Report no.: 
Working Papers 2018-06
Abstract (Translated): 
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.
Subjects: 
Nowcasting
Dynamic Factor Model
Bridge Equations
Principal Component Analysis
Quarterly GDP
Diebold-Mariano test
JEL: 
C32
C38
E52
Document Type: 
Working Paper
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