Arbeitspapier

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

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).

Sprache
Englisch

Erschienen in
Series: Working Papers ; No. 2017-17

Klassifikation
Wirtschaft
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Forecasting Models; Simulation Methods
Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
Thema
GDP Forecasting
Mixed Frequency Data
Daily Financial Data
Nowcasting

Ereignis
Geistige Schöpfung
(wer)
Gomez-Zamudio, Luis M.
Ibarra-Ramírez, Raúl
Ereignis
Veröffentlichung
(wer)
Banco de México
(wo)
Ciudad de México
(wann)
2017

Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Gomez-Zamudio, Luis M.
  • Ibarra-Ramírez, Raúl
  • Banco de México

Entstanden

  • 2017

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