Nowcasting del PIB de México usando modelos de factores y ecuaciones puente - Oscar de Jésus Gálvez-Soriano (Banco de México, 2018)
– 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.
Keywords: C32; C38; E52; 330; Nowcasting; Dynamic Factor Model; Bridge Equations; Principal Component Analysis; Quarterly GDP; Diebold-Mariano test