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Financial variables as leading indicators of GDP growth: evidence from a MIDAS approach during the Great Recession/ created by Laurent Ferrara & Clément Marsilli

By: Contributor(s): Material type: TextTextSeries: Applied economics letters ; Volume 20, number 3New York: Taylor and Francis, 2013Content type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISSN:
  • 13504851
Subject(s): LOC classification:
  • HB1.A666 APP
Online resources: Abstract: The global economic recession, referred to as the Great Recession, endured by the main industrialized countries during the period 2008–09, in the wake of the financial and banking crises, has pointed out the current importance of the financial sector in macroeconomics. In this article, we evaluate the predictive power of some major financial variables to anticipate GDP growth in euro area countries during this specific period of time. In this respect, we implement a Mixed Data Sampling (MIDAS)-based modelling approach, put forward by Ghysels et al. (2007), that enables to forecast quarterly Gross Domestic Product (GDP) growth rates using exogenous variables sampled at higher frequencies. Empirical results show that, overall, stock prices help to improve the accuracy of GDP forecasts by comparison with a standard opinion survey variable, whereas oil prices and term spread appear to be less informative.
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Item type Current library Call number Vol info Copy number Status Notes Date due Barcode
Journal Article Journal Article Main Library - Special Collections HB1.A666 APP (Browse shelf(Opens below)) Vol. 20, no.3 (pages 233-237 ) SP17971 Not for loan For in house use only

The global economic recession, referred to as the Great Recession, endured by the main industrialized countries during the period 2008–09, in the wake of the financial and banking crises, has pointed out the current importance of the financial sector in macroeconomics. In this article, we evaluate the predictive power of some major financial variables to anticipate GDP growth in euro area countries during this specific period of time. In this respect, we implement a Mixed Data Sampling (MIDAS)-based modelling approach, put forward by Ghysels et al. (2007), that enables to forecast quarterly Gross Domestic Product (GDP) growth rates using exogenous variables sampled at higher frequencies. Empirical results show that, overall, stock prices help to improve the accuracy of GDP forecasts by comparison with a standard opinion survey variable, whereas oil prices and term spread appear to be less informative.

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