Short-Run Assessment of French Economic Activity Using OPTIM
This paper describes a short-term projection model for French economic activity, OPTIM, the aim of which is twofold. First it gives an early estimate of real GDP growth for the previous quarter, when no figure has yet been released by Insee, the French National Statistical Institute, along with flash estimates for main GDP components (consumption, investment, inventories and external trade) together with a breakdown by sectors (services, manufacturing, construction, equipment, agri-food). This appears particularly useful for the short-run analysis. In this respect OPTIM may be considered as a traditional bridge equation model since it links a particular indicator available generally ahead of the release of the quarterly national accounts with a quarterly aggregate like GDP, consumption…. Second, this tool supplies also estimates for GDP growth and its main components for the current quarter and for the next quarter (i.e two and three quarters respectively following the latest reference period of Insee's GDP data release). A pool of (mainly) monthly variables is used, which are, sometimes, directly introduced in the specification but, more often, summarised by the implementation of a principal component analysis (PCA). The largest part of the set of indicators comprises survey data together with monthly traditional indicators (industrial production, consumption in manufactured goods…). But other data (in particular financial data) are also introduced. The outcomes of OPTIM rely on a relatively complex procedure involving about twenty equations and mixing two alternative approaches: a supply approach consisting in a direct modelling of GDP and a demand approach where GDP is the sum of consumption, investment, changes in stocks and net trade (exports minus imports). The discrepancy between these two estimates is distributed according to an original method, yielding a unique GDP estimation. The paper is organised as follows. Section 1 presents the main features of OPTIM. Section 2 deals with data description while section 3 addresses the data assessment's issue. In section 4, the main equations are described. Section 5 presents a general assessment of OPTIM in terms of forecasting record. Finally section 6 concludes and proposes some avenues for further developments.
|Date of creation:||2002|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://www.banque-france.fr/
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Francis X. Diebold & Robert S. Mariano, 1994.
"Comparing Predictive Accuracy,"
NBER Technical Working Papers
0169, National Bureau of Economic Research, Inc.
- Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
- Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
- Harvey, Campbell R., 1988. "The real term structure and consumption growth," Journal of Financial Economics, Elsevier, vol. 22(2), pages 305-333, December.
- Arturo Estrella & Frederic S. Mishkin, 1998.
"Predicting U.S. Recessions: Financial Variables As Leading Indicators,"
The Review of Economics and Statistics,
MIT Press, vol. 80(1), pages 45-61, February.
- Arturo Estrella & Frederic S. Mishkin, 1996. "Predicting U.S. recessions: financial variables as leading indicators," Research Paper 9609, Federal Reserve Bank of New York.
- Arturo Estrella & Frederic S. Mishkin, 1995. "Predicting U.S. Recessions: Financial Variables as Leading Indicators," NBER Working Papers 5379, National Bureau of Economic Research, Inc.
- Evan F. Koenig & Sheila Dolmas & Jeremy Piger, 2003.
"The Use and Abuse of Real-Time Data in Economic Forecasting,"
The Review of Economics and Statistics,
MIT Press, vol. 85(3), pages 618-628, August.
- Evan F. Koenig & Sheila Dolmas & Jeremy M. Piger, 2002. "The use and abuse of 'real-time' data in economic forecasting," Working Papers 2001-015, Federal Reserve Bank of St. Louis.
- Evan F. Koenig & Sheila Dolmas & Jeremy M. Piger, 2000. "The use and abuse of "real-time" data in economic forecasting," Working Papers 0004, Federal Reserve Bank of Dallas.
- Evan F. Koenig & Sheila Dolmas & Jeremy M. Piger, 2000. "The use and abuse of "real-time" data in economic forecasting," International Finance Discussion Papers 684, Board of Governors of the Federal Reserve System (U.S.).
- Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
- Lucrezia Reichlin & Mario Forni & Marc Hallin & Marco Lippi, 2001.
"Coincident and leading indicators for the Euro area,"
ULB Institutional Repository
2013/10137, ULB -- Universite Libre de Bruxelles.
- Forni, Mario, et al, 2001. "Coincident and Leading Indicators for the Euro Area," Economic Journal, Royal Economic Society, vol. 111(471), pages C62-85, May.
When requesting a correction, please mention this item's handle: RePEc:bfr:banfra:88. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael brassart)
If references are entirely missing, you can add them using this form.