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A Model to Estimate the Composite Index of Economic Activity in Romania – IEF-RO

  • Albu, Lucian Liviu

    ()

    (Research Professor, Institute for Economic Forecasting)

One of the most significant impediments for short-term forecasts is the frequency of publishing GDP. At present, national institutes of statistics are publishing officially registered GDP only quarterly. In our study, we tried to build a composite indicator based on usually monthly data and to use it in order to obtain short-term forecasts for economic activity at national level. This indicator could be useful taking into account that actually there is no synthetic indicator to describe the short-run dynamics of economic activity. Thus, such an estimating model we are proposing for the Romanian economy is coming from the last results in this field, especially from the OECD methodology. Moreover, to validate the main hypotheses of the estimating model for the composite indicator in the case of the Romanian economy we used the quarterly data and, as benchmark indicator was considered the quarterly published GDP. Using certain models based on composite indicators (leading indicators, coincidence indicators, and post-cycle indicators), beside other models to analyse high frequency time series and to obtain sort-term forecasts (such as principal component method, so-called virtual monthly GDP method or various interpolating methods), it can result in richer information for the business environment which in modern times founds itself in an accelerated process of change.

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Article provided by Institute for Economic Forecasting in its journal Romanian Journal for Economic Forecasting.

Volume (Year): 5 (2008)
Issue (Month): 2 (June)
Pages: 44-50

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Handle: RePEc:rjr:romjef:v:5:y:2008:i:2:p:44-50
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  1. Stanica, Cristian, 2004. "Aplicatii privind exprimarea PIB-ului potential trimestrial," Working Papers of Macroeconomic Modelling Seminar 040203, Institute for Economic Forecasting.
  2. Dean Croushore & Tom Stark, 1999. "A real-time data set for macroeconomists," Working Papers 99-4, Federal Reserve Bank of Philadelphia.
  3. Rodrik, Dani & Alesina, Alberto, 1994. "Distributive Politics and Economic Growth," Scholarly Articles 4551798, Harvard University Department of Economics.
  4. James H. Stock & Mark W. Watson, 1993. "A Procedure for Predicting Recessions with Leading Indicators: Econometric Issues and Recent Experience," NBER Chapters, in: Business Cycles, Indicators and Forecasting, pages 95-156 National Bureau of Economic Research, Inc.
  5. Stock, J.H. & Watson, M.W., 1989. "New Indexes Of Coincident And Leading Economic Indicators," Papers 178d, Harvard - J.F. Kennedy School of Government.
  6. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
  7. Christophe Croux & Mario Forni & Lucrezia Reichlin, 2001. "A Measure Of Comovement For Economic Variables: Theory And Empirics," The Review of Economics and Statistics, MIT Press, vol. 83(2), pages 232-241, May.
  8. Stark, Tom & Croushore, Dean, 2002. "Forecasting with a real-time data set for macroeconomists," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 507-531, December.
  9. Albu, Lucian-Liviu, 2006. "A dynamic model to estimate the long-run trends in potential GDP," MPRA Paper 3708, University Library of Munich, Germany.
  10. Scutaru, Cornelia & Stanica, Cristian Nicolae, 2005. "Output Gap And Shocks Dynamics. The Case Of Romania," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 2(4), pages 25-43.
  11. Keith R. Phillips, 2004. "A new monthly index of the Texas business cycle," Working Papers 0401, Federal Reserve Bank of Dallas.
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