The Role of Financial Variables in Predicting Economic Activity in the Euro Area
IMF Working Papers, November 1, 2009
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- The Role of Financial Variables in Predicting Economic Activity in the Euro Area
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Bibliographic details
- Authors: Marco Lombardi, Raphael A Espinoza, Fabio Fornari
- Published: November 1, 2009
- Series: IMF Working Papers
- DOI: https://doi.org/10.5089/9781451873887.001
Research question and approach
- Investigates whether financial variables carry additional information for forecasting euro area GDP beyond the predictive power of the U.S. business cycle.
- Uses vector autoregressions (VARs) including:
- U.S. GDP and euro area GDP as a minimal set of variables.
- Growth in the Rest of the World (an aggregation of seven small countries).
- Selected combinations of financial variables.
- Assessment methods:
- In-sample impulse responses.
- Out-of-sample forecast exercises using the Root Mean Square Error (RMSE) metric.
- Forecasting ability also assessed as if in real time (conditionally on the information available at the time of the forecast).
Key findings
- Cross-cycle lead:
- "The U.S. business cycle typically leads the European cycle by a few quarters and this can be used to forecast euro area GDP."
- In-sample dynamics:
- "Impulse responses (in-sample) show that shocks to financial variables influence real activity."
- Out-of-sample forecasting (RMSE):
- "According to out-of-sample forecast exercises using the Root Mean Square Error (RMSE) metric, this macro-financial linkage would be weak: financial indicators do not improve short and medium term forecasts of real activity in the euro area, even when their timely availability, relative to GDP, is exploited."
- Real-time (ex ante) assessment:
- "When forecasting ability is assessed as if in real time (conditionally on the information available at the time of the forecast), we find that models using financial variables would have been preferred, ex ante, in several episodes, in particular between 1999 and 2002."
- Interpretation and implication:
- "This result is partly due to the 'average' nature of the RMSE metric."
- Policy-relevant implication: "One should not discard, on the basis of RMSE statistics, the use of predictive models that include financial variables if there is a theoretical prior that a financial shock is affecting growth."
Subjects and keywords
- Subject: Financial statistics, Loans, Stock markets, Vector autoregression, Yield curve
- Keywords: area GDP, dividend yield, euro area, GDP growth, trade statistics, WP
Content in this bundle
- _wp09241 - References