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EuroMInd-C: a Disaggregate Monthly Indicator of Economic Activity for the Euro Area and member countries

Listed author(s):
  • Cecilia Frale

    ()

    (Ministry of the Economy and Finance)

  • Stefano Grassi

    ()

    (University of Kent and CREATES)

  • Massimiliano Marcellino

    ()

    (EUI Florence)

  • Gianluigi Mazzi

    ()

    (EUROSTAT)

  • Tommaso Proietti

    ()

    (University of Rome "Tor Vergata")

The paper deals with the estimation of monthly indicators of economic activity for the Euro area and its largest member countries that possess the following attributes: relevance, representativeness and timeliness. Relevance is obtained by referring our monthly indicators to gross domestic product at chained volumes, the most important measure of the level of economic activity. Representativeness is achieved by entertaining a very large number of (timely) time series on monthly indicators relating to the level of economic activity, providing a more or less complete coverage. The indicators are modelled with a large scale parametric factor model. We discuss its specification and provide details on the statistical treatment. Computational efficiency is crucial to estimate a large scale parametric factor model of the dimension considered in our application (considering about 170 series). To achieve it we apply state of the art state space methods that can handle temporal aggregation, and any pattern of missing values.

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File URL: ftp://www.ceistorvergata.it/repec/rpaper/RP287.pdf
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Paper provided by Tor Vergata University, CEIS in its series CEIS Research Paper with number 287.

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Length: 40 pages
Date of creation: 01 Oct 2013
Date of revision: 01 Oct 2013
Handle: RePEc:rtv:ceisrp:287
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References listed on IDEAS
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  1. M. Ayhan Kose & Christopher Otrok & Eswar Prasad, 2012. "Global Business Cycles: Convergence Or Decoupling?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(2), pages 511-538, 05.
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  7. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
  8. B. Jungbacker & S.J. Koopman & M. van der Wel, 2009. "Dynamic Factor Analysis in The Presence of Missing Data," Tinbergen Institute Discussion Papers 09-010/4, Tinbergen Institute, revised 11 Mar 2011.
  9. Watson, Mark W. & Engle, Robert F., 1983. "Alternative algorithms for the estimation of dynamic factor, mimic and varying coefficient regression models," Journal of Econometrics, Elsevier, vol. 23(3), pages 385-400, December.
  10. Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, Elsevier.
  11. Gabriele Fiorentini & Enrique Sentana & Neil Shephard, 2004. "Likelihood-Based Estimation of Latent Generalized ARCH Structures," Econometrica, Econometric Society, vol. 72(5), pages 1481-1517, 09.
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  18. repec:hal:journl:peer-00844811 is not listed on IDEAS
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  21. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, 2009. "Survey Data as Coicident or Leading Indicators," Economics Working Papers ECO2009/19, European University Institute.
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