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Are daily financial data useful for forecasting GDP? Evidence from Mexico

Luis M. Gómez-Zamudio and Raul Ibarra

No 2017-17, Working Papers from Banco de México

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).

Keywords: GDP Forecasting; Mixed Frequency Data; Daily Financial Data; Nowcasting (search for similar items in EconPapers)
JEL-codes: C22 C53 E37 (search for similar items in EconPapers)
Date: 2017-09
New Economics Papers: this item is included in nep-for and nep-mac
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