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1 décembre 2020
- handle: 10670/1.7o3mqb
Ce document est lié à :
10.24201/ee.v35i2.402
info:eu-repo/semantics/openAccess
Mots-clés
forecasting state space model principal component analysis monetary policy Kalman filter Diebold-Mariano test C32 C38 C53 E52Sujets proches
GDP Domestic product, Gross Prediction Forecasts FuturologyCiter ce document
Oscar de J. Gálvez-Soriano, « Nowcasting Mexico’s quarterly GDP using factor models and bridge equations », Estudios Económicos (México, D.F.), ID : 10670/1.7o3mqb
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Résumé
: 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. Using realtime 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.