Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/174470
Authors: 
Gomez-Zamudio, Luis M.
Ibarra-Ramírez, Raúl
Year of Publication: 
2017
Series/Report no.: 
Working Papers, Banco de México 2017-17
Abstract: 
This article evaluates the use of financial data sampled at high frequencies to improve short-term forecasts of quarterly GDP for Mexico. In particular, the mixed data sampling (MIDAS) regression model is employed to incorporate both quarterly and daily frequencies while remaining parsimonious. To preserve parsimony, factor analysis and forecast combination techniques are used to summarize the information contained in a dataset containing 392 daily financial series. Our findings suggest that the MIDAS model that incorporates daily financial data lead to improvements for quarterly forecasts of GDP growth over traditional models that either rely only on quarterly macroeconomic data or average daily financial data. Furthermore, we explore the ability of the MIDAS model to provide forecast updates for GDP growth (nowcasting).
Subjects: 
GDP Forecasting
Mixed Frequency Data
Daily Financial Data
Nowcasting
JEL: 
C22
C53
E37
Document Type: 
Working Paper
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