Nowcasting Mexico's quarterly GDP using factor models and bridge equations

Oscar de J. Gálvez-Soriano
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Oscar de J. Gálvez-Soriano: Banco de México
Authors registered in the RePEc Author Service: Oscar de Jesús Gálvez Soriano

Estudios Económicos, 2020, vol. 35, issue 2, 213-265

Abstract: Short term forecasts of GDP have become a necessary practice among central banks in order to take better informed monetary policy decisions. In this paper, I evaluate five nowcasting models that I used to forecast Mexico's quarterly GDP in the short run: a dynamic factor model (DFM), two bridge equation (BE) models and two models based on principal components analysis (PCA). The results indicate that the average of the two BE forecasts is statistically better than the rest of the models under consideration, according to the Diebold-Mariano accuracy test (Diebold and Mariano, 1995). Using real-time information, I show that the average of the BE models is also more accurate than the median of the forecasts provided by the analysts surveyed by Bloomberg, the median of the experts who answer Banco de México’s Survey of Professional Forecasters and the rapid GDP estimate released by INEGI.

Keywords: forecasting; state space model; principal component analysis; monetary policy; Kalman filter; Diebold-Mariano test (search for similar items in EconPapers)
JEL-codes: C32 C38 C53 E52 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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