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

Luis M. Gómez-Zamudio and Raul Ibarra

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: This article evaluates the use of financial data sampled at high frequencies to improve short-term forecasts of quarterly GDP for Mexico. The model uses both quarterly and daily sampling frequencies while remaining parsimonious. In particular, the mixed data sampling (MIDAS) regression model is employed to deal with the multi-frequency problem. To preserve parsimony, factor analysis and forecast combination techniques are used to summarize the information contained in a data set containing 392 daily financial series. Our findings suggest that the MIDAS model incorporating daily financial data leads to improvements in quarterly forecasts of GDP growth over traditional models that either rely only on quarterly macroeconomic data or average daily frequency data. The evidence suggests that this methodology improves the forecasts for the Mexican GDP notwithstanding its higher volatility relative to that of developed countries. 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)
Pages: 31 pages
Date: 2017-04-01
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Published in Economía, 1, April, 2017, 17(2), pp. 173 - 203. ISSN: 1529-7470

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http://eprints.lse.ac.uk/123310/ Open access version. (application/pdf)

Related works:
Journal Article: Are Daily Financial Data Useful for Forecasting GDP? Evidence from Mexico (2017)
Working Paper: Are daily financial data useful for forecasting GDP? Evidence from Mexico (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:123310

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