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

Gómez-Zamudio, Luis M. and Ibarra, Raúl (2017) Are daily financial data useful for forecasting GDP? Evidence from Mexico. Economía, 17 (2). 173 - 203. ISSN 1529-7470

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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).

Item Type:	Article
Official URL:	https://economia.lse.ac.uk/
Additional Information:	© 2017 LACTEA
Divisions:	LSE
Subjects:	H Social Sciences > HC Economic History and Conditions H Social Sciences > HB Economic Theory H Social Sciences > HG Finance
JEL classification:	C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C22 - Time-Series Models C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Other Model Applications E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation
Date Deposited:	03 Sep 2024 16:33
Last Modified:	14 Sep 2024 10:03
URI:	http://eprints.lse.ac.uk/id/eprint/123310

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