This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Factor-MIDAS for now- and forecasting with ragged-edge data: A model comparison for German GDP

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Marcellino, Massimiliano
Schumacher, Christian

Additional information is available for the following registered author(s):

Abstract

This paper compares different ways to estimate the current state of the economy using factor models that can handle unbalanced datasets. Due to the different release lags of business cycle indicators, data unbalancedness often emerges at the end of multivariate samples, which is sometimes referred to as the `ragged edge' of the data. Using a large monthly dataset of the German economy, we compare the performance of different factor models in the presence of the ragged edge: static and dynamic principal components based on realigned data, the Expectation-Maximisation (EM) algorithm and the Kalman smoother in a state-space model context. The monthly factors are used to estimate current quarter GDP, called the `nowcast', using different versions of what we call factor-based mixed-data sampling (Factor-MIDAS) approaches. We compare all possible combinations of factor estimation methods and Factor-MIDAS projections with respect to nowcast performance. Additionally, we compare the performance of the nowcast factor models with the performance of quarterly factor models based on time-aggregated and thus balanced data, which neglect the most timely observations of business cycle indicators at the end of the sample. Our empirical findings show that the factor estimation methods don't differ much with respect to nowcasting accuracy. Concerning the projections, the most parsimonious MIDAS projection performs best overall. Finally, quarterly models are in general outperformed by the nowcast factor models that can exploit ragged-edge data

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.cepr.org/pubs/dps/DP6708.asp
File Format: application/pdf
File Function:
Download Restriction: CEPR Discussion Papers are free to download for our researchers, subscribers and members. If you fall into one of these categories but have trouble downloading our papers, please contact us at subscribers@cepr.org

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Publisher Info
Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 6708.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length:
Date of creation: Feb 2008
Date of revision:
Handle: RePEc:cpr:ceprdp:6708

Contact details of provider:
Postal: Centre for Economic Policy Research, 53--56 Great Sutton Street, London EC1V 0DG
Phone: 44 - 20 - 7183 8801
Fax: 44 - 20 - 7183 8820

Order Information:
Email:

For technical questions regarding this item, or to correct its listing, contact: ().

Related research
Keywords: business cycle; large factor models; MIDAS; missing values; mixed-frequency data; nowcasting;

Other versions of this item:

Find related papers by JEL classification:
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation

This paper has been announced in the following NEP Reports:

References listed on IDEAS
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.:
  1. 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. [Downloadable!]
  2. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor and Francis Journals, vol. 26(1), pages 53-90. [Downloadable!] (restricted)
  3. Martin Schneider & Martin Spitzer, 2004. "Forecasting Austrian GDP using the generalized dynamic factor model," Working Papers 89, Oesterreichische Nationalbank (Austrian Central Bank). [Downloadable!]
  4. Angelini, Elena & Henry, Jerome & Marcellino, Massimiliano, 2006. "Interpolation and backdating with a large information set," Journal of Economic Dynamics and Control, Elsevier, vol. 30(12), pages 2693-2724, December. [Downloadable!] (restricted)
    Other versions:
  5. Schumacher, Christian & Breitung, Jörg, 2006. "Real-time forecasting of GDP based on a large factor model with monthly and quarterly data," Discussion Paper Series 1: Economic Studies 2006,33, Deutsche Bundesbank, Research Centre. [Downloadable!]
  6. Bernanke, Ben S. & Boivin, Jean, 2003. "Monetary policy in a data-rich environment," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 525-546, April. [Downloadable!] (restricted)
    Other versions:
  7. Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, Elsevier. [Downloadable!] (restricted)
  8. Christine De Mol & Domenico Giannone & Lucrezia Reichlin, 2006. "Forecasting using a large number of predictors - Is Bayesian regression a valid alternative to principal components?," Working Paper Series 700, European Central Bank. [Downloadable!]
    Other versions:
  9. Altissimo, Filippo & Cristadoro, Riccardo & Forni, Mario & Lippi, Marco & Veronese, Giovanni, 2006. "New EuroCOIN: Tracking Economic Growth in Real Time," CEPR Discussion Papers 5633, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
  10. Chevillon, Guillaume & Hendry, David F., 2005. "Non-parametric direct multi-step estimation for forecasting economic processes," International Journal of Forecasting, Elsevier, vol. 21(2), pages 201-218. [Downloadable!] (restricted)
    Other versions:
  11. Christian Schumacher, 2007. "Forecasting German GDP using alternative factor models based on large datasets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 271-302. [Downloadable!]
    Other versions:
  12. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2006. "A quasi maximum likelihood approach for large approximate dynamic factor models," Working Paper Series 674, European Central Bank. [Downloadable!]
    Other versions:
  13. Ziegler, Christina & Eickmeier, Sandra, 2006. "How good are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Discussion Paper Series 1: Economic Studies 2006,42, Deutsche Bundesbank, Research Centre. [Downloadable!]
  14. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2003. "Macroeconomic forecasting in the Euro area: Country specific versus area-wide information," European Economic Review, Elsevier, vol. 47(1), pages 1-18, February. [Downloadable!] (restricted)
    Other versions:
  15. Bai, Jushan & Ng, Serena, 2007. "Determining the Number of Primitive Shocks in Factor Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 52-60, January. [Downloadable!] (restricted)
  16. Breitung, Jörg & Eickmeier, Sandra, 2005. "Dynamic factor models," Discussion Paper Series 1: Economic Studies 2005,38, Deutsche Bundesbank, Research Centre. [Downloadable!]
    Other versions:
  17. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2005. "Leading Indicators for Euro-area Inflation and GDP Growth," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 785-813, December. [Downloadable!] (restricted)
    Other versions:
  18. Domenico Giannone & Lucrezia Reichlin & David Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
    Other versions:
  19. Marta Banbura & Gerhard Rünstler, 2007. "A look into the factor model black box - publication lags and the role of hard and soft data in forecasting GDP," Working Paper Series 751, European Central Bank. [Downloadable!]
  20. Kapetanios, George & Marcellino, Massimiliano, 2006. "A Parametric Estimation Method for Dynamic Factor Models of Large Dimensions," CEPR Discussion Papers 5620, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
  21. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January. [Downloadable!] (restricted)
    Other versions:
  22. Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," NBER Working Papers 11285, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
    Other versions:
  23. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," CEPR Discussion Papers 6706, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
  24. Banerjee, Anindya & Marcellino, Massimiliano, 2006. "Are there any reliable leading indicators for US inflation and GDP growth?," International Journal of Forecasting, Elsevier, vol. 22(1), pages 137-151. [Downloadable!] (restricted)
    Other versions:
  25. Helmut Lütkepohl & Ralf Brüggemann, 2006. "A small monetary system for the euro area based on German data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 683-702. [Downloadable!]
    Other versions:
  26. Antonello D’Agostino & Domenico Giannone & Paolo Surico, 2006. "(Un)Predictability and macroeconomic stability," Working Paper Series 605, European Central Bank. [Downloadable!]
    Other versions:
  27. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September. [Downloadable!] (restricted)
    Other versions:
  28. Kapetanios, George & Labhard, Vincent & Price, Simon, 2008. "Forecast combination and the Bank of England's suite of statistical forecasting models," Economic Modelling, Elsevier, vol. 25(4), pages 772-792, July. [Downloadable!] (restricted)
    Other versions:
  29. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526. [Downloadable!] (restricted)
    Other versions:
  30. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2003. "Do financial variables help forecasting inflation and real activity in the euro area?," Journal of Monetary Economics, Elsevier, vol. 50(6), pages 1243-1255, September. [Downloadable!] (restricted)
    Other versions:
  31. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  32. Jushan Bai & Serena Ng, 2006. "Confidence Intervals for Diffusion Index Forecasts and Inference for Factor-Augmented Regressions," Econometrica, Econometric Society, vol. 74(4), pages 1133-1150, 07. [Downloadable!] (restricted)
  33. Antonello D'Agostino & Domenico Giannone, 2006. "Comparing alternative predictors based on large-panel factor models," Working Paper Series 680, European Central Bank. [Downloadable!]
    Other versions:
  34. Campbell, Sean D., 2007. "Macroeconomic Volatility, Predictability, and Uncertainty in the Great Moderation: Evidence From the Survey of Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 191-200, April. [Downloadable!] (restricted)
  35. A.H.J. den Reijer, 2005. "Forecasting Dutch GDP using Large Scale Factor Models," DNB Working Papers 028, Netherlands Central Bank, Research Department. [Downloadable!]
Full references

Cited by:
(explanations, 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.)

  1. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2009. "Forecasting with Factor-Augmented Error Correction Models," Discussion Papers 09-06, Department of Economics, University of Birmingham. [Downloadable!]
    Other versions:
  2. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2009. "Pooling versus model selection for nowcasting with many predictors: an application to German GDP," Discussion Paper Series 1: Economic Studies 2009,03, Deutsche Bundesbank, Research Centre. [Downloadable!]
    Other versions:
  3. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," Economics Working Papers ECO2009/32, European University Institute. [Downloadable!]
  4. Chen, Pu, 2009. "A Note on Updating Forecasts When New Information Arrives between Two Periods," Economics Discussion Papers 2009-22, Kiel Institute for the World Economy. [Downloadable!]
  5. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area," Discussion Paper Series 1: Economic Studies 2009,07, Deutsche Bundesbank, Research Centre. [Downloadable!]
Statistics
Access and download statistics

Did you know? You too can volunteer for RePEc, for example by editing a NEP report.

This page was last updated on 2009-11-25.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.