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

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  • Ibarra-Ramírez Raúl
  • Gómez-Zamudio Luis M.

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).

Suggested Citation

  • Ibarra-Ramírez Raúl & Gómez-Zamudio Luis M., 2017. "Are daily financial data useful for forecasting GDP? Evidence from Mexico," Working Papers 2017-17, Banco de México.
    • Handle: RePEc:bdm:wpaper:2017-17
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    Cited by:

    1. Lastunen, Jesse & Richiardi, Matteo, 2023. "Forecasting recovery from COVID-19 using financial data: An application to Vietnam," World Development Perspectives, Elsevier, vol. 30(C).
    2. Ibarra-Ramírez Raúl, 2021. "The Yield Curve as a Predictor of Economic Activity in Mexico: The Role of the Term Premium," Working Papers 2021-07, Banco de México.

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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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