IDEAS home Printed from https://ideas.repec.org/p/eui/euiwps/eco2009-19.html
   My bibliography  Save this paper

Survey Data as Coicident or Leading Indicators

Author

Listed:
  • Cecilia Frale
  • Massimiliano Marcellino
  • Gian Luigi Mazzi
  • Tommaso Proietti

Abstract

In this paper we propose a monthly measure for the euro area Gross Domestic Product (GDP) based on a small scale factor model for mixed frequency data, featuring two factors: the first is driven by hard data, whereas the second captures the contribution of survey variables as coincident indicators. Within this framework we evaluate both the in-sample contribution of the second survey-based factor, and the short term forecasting performance of the model in a pseudo-real time experiment. We find that the survey-based factor plays a significant role for two components of GDP: Industrial Value Added and Exports. Moreover, the two factor model outperforms in terms of out of sample forecasting accuracy the traditional autoregressive distributed lags (ADL) specifications and the single factor model, with few exceptions for Exports and in growth rates.

Suggested Citation

  • 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.
    • Handle: RePEc:eui:euiwps:eco2009/19
    as

    Download full text from publisher

    File URL: http://cadmus.eui.eu/dspace/bitstream/1814/11454/1/ECO_2009_19.pdf
    File Function: main text
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    2. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    3. Tommaso Proietti & Filippo Moauro, 2006. "Dynamic factor analysis with non-linear temporal aggregation constraints," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(2), pages 281-300.
    4. Pesaran, M. Hashem & Weale, Martin, 2006. "Survey Expectations," Handbook of Economic Forecasting, Elsevier.
    5. Filippo Moauro & Giovanni Savio, 2005. "Temporal disaggregation using multivariate structural time series models," Econometrics Journal, Royal Economic Society, vol. 8(2), pages 214-234, July.
    6. Filippo Altissimo & Riccardo Cristadoro & Mario Forni & Marco Lippi & Giovanni Veronese, 2010. "New Eurocoin: Tracking Economic Growth in Real Time," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1024-1034, November.
    7. Alex S. Morton & Granville Tunnicliffe-Wilson, 2004. "A class of modified high-order autoregressive models with improved resolution of low-frequency cycles," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(2), pages 235-250, March.
    8. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 169-173, April.
    9. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    10. 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.
    11. Demos, Antonis & Sentana, Enrique, 1998. "Testing for GARCH effects: a one-sided approach," Journal of Econometrics, Elsevier, vol. 86(1), pages 97-127, June.
    12. Tommaso Proietti & Cecilia Frale, 2011. "New proposals for the quantification of qualitative survey data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(4), pages 393-408, July.
    13. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 169-173, April.
    14. Harvey, Andrew & Proietti, Tommaso (ed.), 2005. "Readings in Unobserved Components Models," OUP Catalogue, Oxford University Press, number 9780199278695.
    15. Andrew Harvey & Chia-Hui Chung, 2000. "Estimating the underlying change in unemployment in the UK," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(3), pages 303-309.
    16. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    2. Robert Lehmann, 2015. "Survey-based indicators vs. hard data: What improves export forecasts in Europe?," ifo Working Paper Series 196, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    3. Lahiri, Kajal & Monokroussos, George, 2013. "Nowcasting US GDP: The role of ISM business surveys," International Journal of Forecasting, Elsevier, vol. 29(4), pages 644-658.
    4. Marco Cacciotti & Cecilia Frale & Serena Teobaldo, 2013. "A new methodology for a quarterly measure of the output gap," Working Papers 6, Department of the Treasury, Ministry of the Economy and of Finance.
    5. Cecilia Frale, Serena Teobaldo, Marco Cacciotti, Alessandra Caretta, 2013. "A Quarterly Measure Of Potential Output In The New European Fiscal Framework," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - Italian Review of Economics, Demography and Statistics, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 67(2), pages 181-197, April-Jun.
    6. Anna Norin, 2011. "Nowcasting of the Gross Regional Product," ERSA conference papers ersa10p768, European Regional Science Association.
    7. Moauro, Filippo, 2010. "A monthly indicator of employment in the euro area: real time analysis of indirect estimates," MPRA Paper 27797, University Library of Munich, Germany, revised 30 Dec 2010.
    8. Chrystalleni Aristidou & Kevin Lee & Kalvinder Shields, 2015. "Real-Time Data should be used in Forecasting Output Growth and Recessionary Events in the US," Discussion Papers 2015/13, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    9. Marcellino, Massimiliano & Sivec, Vasja, 2016. "Monetary, fiscal and oil shocks: Evidence based on mixed frequency structural FAVARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 335-348.
    10. Bisio, Laura & Moauro, Filippo, 2017. "Temporal disaggregation by dynamic regressions: recent developments in Italian quarterly national accounts," MPRA Paper 80211, University Library of Munich, Germany, revised 14 Jul 2017.
    11. Grassi, Stefano & Proietti, Tommaso & Frale, Cecilia & Marcellino, Massimiliano & Mazzi, Gianluigi, 2015. "EuroMInd-C: A disaggregate monthly indicator of economic activity for the Euro area and member countries," International Journal of Forecasting, Elsevier, vol. 31(3), pages 712-738.
    12. Maximo Camacho & Gabriel Perez-Quiros, 2010. "Introducing the euro-sting: Short-term indicator of euro area growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 663-694.
    13. repec:eee:intfor:v:33:y:2017:i:4:p:878-893 is not listed on IDEAS
    14. Kaufmann, Daniel & Scheufele, Rolf, 2017. "Business tendency surveys and macroeconomic fluctuations," International Journal of Forecasting, Elsevier, vol. 33(4), pages 878-893.
    15. Cecilia Frale & Libero Monteforte, "undated". "FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure," Working Papers 3, Department of the Treasury, Ministry of the Economy and of Finance.
    16. Raffaella Giacomini & Barbara Rossi, 2013. "Forecasting in macroeconomics," Chapters,in: Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 17, pages 381-408 Edward Elgar Publishing.
    17. Sieds, 2013. "Complete Volume LXVII n.2 2013," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - Italian Review of Economics, Demography and Statistics, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 67(2), pages 1-197, April-Jun.
    18. Bhattacharya, Rudrani & Pandey, Radhika & Veronese, Giovanni, 2011. "Tracking India Growth in Real Time," Working Papers 11/90, National Institute of Public Finance and Policy.
    19. repec:spr:soinre:v:135:y:2018:i:1:d:10.1007_s11205-016-1490-3 is not listed on IDEAS

    More about this item

    Keywords

    Survey data; Temporal Disaggregation. Multivariate State Space Models. Dynamic factor Models. Kalman filter and smoother. Chain-linking;

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eui:euiwps:eco2009/19. 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: (Julia Valerio). General contact details of provider: http://edirc.repec.org/data/deiueit.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.