IDEAS home Printed from https://ideas.repec.org/h/eme/aecozz/s0731-905320150000035001.html
   My bibliography  Save this book chapter

An Overview of the Factor-augmented Error-Correction Model

In: Dynamic Factor Models

Author

Listed:
  • Anindya Banerjee
  • Massimiliano Marcellino
  • Igor Masten

Abstract

The Factor-augmented Error-Correction Model (FECM) generalizes the factor-augmented VAR (FAVAR) and the Error-Correction Model (ECM), combining error-correction, cointegration and dynamic factor models. It uses a larger set of variables compared to the ECM and incorporates the long-run information lacking from the FAVAR because of the latter’s specification in differences. In this paper, we review the specification and estimation of the FECM, and illustrate its use for forecasting and structural analysis by means of empirical applications based on Euro Area and US data.

Suggested Citation

  • Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2016. "An Overview of the Factor-augmented Error-Correction Model," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 3-41, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-905320150000035001
    DOI: 10.1108/S0731-905320150000035001
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/S0731-905320150000035001/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: no
    File URL: https://www.emerald.com/insight/content/doi/10.1108/S0731-905320150000035001/full/epub?utm_source=repec&utm_medium=feed&utm_campaign=repec&title=10.1108/S0731-905320150000035001
    Download Restriction: no
    File URL: https://www.emerald.com/insight/content/doi/10.1108/S0731-905320150000035001/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: no
    File URL: https://libkey.io/10.1108/S0731-905320150000035001?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Warne, A., 1993. "A Common Trends Model: Identification, Estimation and Inference," Papers 555, Stockholm - International Economic Studies.
    2. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    3. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    4. Jushan Bai & Serena Ng, 2004. "A PANIC Attack on Unit Roots and Cointegration," Econometrica, Econometric Society, vol. 72(4), pages 1127-1177, July.
    5. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    6. Adolfson, Malin & Laseen, Stefan & Linde, Jesper & Villani, Mattias, 2007. "Bayesian estimation of an open economy DSGE model with incomplete pass-through," Journal of International Economics, Elsevier, vol. 72(2), pages 481-511, July.
    7. Sandra Eickmeier, 2009. "Comovements and heterogeneity in the euro area analyzed in a non-stationary dynamic factor model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(6), pages 933-959.
    8. Bai, Jushan, 2004. "Estimating cross-section common stochastic trends in nonstationary panel data," Journal of Econometrics, Elsevier, vol. 122(1), pages 137-183, September.
    9. Xu Cheng & P eter C. B. Phillips, 2009. "Semiparametric cointegrating rank selection," Econometrics Journal, Royal Economic Society, vol. 12(s1), pages 83-104, January.
    10. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    11. Gengenbach, C. & Urbain, J.R.Y.J. & Westerlund, J., 2008. "Panel error correction testing with global stochastic trends," Research Memorandum 051, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    12. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(1), pages 387-422.
    13. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501, Decembrie.
    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. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2021. "Modelling non-stationary ‘Big Data’," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1556-1575.
    2. Di Iorio, Francesca & Fachin, Stefano, 2021. "Evaluating restricted common factor models for non-stationary data," Econometrics and Statistics, Elsevier, vol. 17(C), pages 64-75.
    3. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    4. Tobias Hartl, 2020. "Macroeconomic Forecasting with Fractional Factor Models," Papers 2005.04897, arXiv.org.
    5. Stoupos, Nikolaos & Nikas, Christos & Kiohos, Apostolos, 2023. "Turkey: From a thriving economic past towards a rugged future? - An empirical analysis on the Turkish financial markets," Emerging Markets Review, Elsevier, vol. 54(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2014. "Forecasting with factor-augmented error correction models," International Journal of Forecasting, Elsevier, vol. 30(3), pages 589-612.
    2. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2017. "Structural FECM: Cointegration in large‐scale structural FAVAR models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(6), pages 1069-1086, September.
    3. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2016. "Non-Stationary Dynamic Factor Models for Large Datasets," Finance and Economics Discussion Series 2016-024, Board of Governors of the Federal Reserve System (U.S.).
    4. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2014. "Forecasting with factor-augmented error correction models," International Journal of Forecasting, Elsevier, vol. 30(3), pages 589-612.
    5. Barigozzi, Matteo & Lippi, Marco & Luciani, Matteo, 2021. "Large-dimensional Dynamic Factor Models: Estimation of Impulse–Response Functions with I(1) cointegrated factors," Journal of Econometrics, Elsevier, vol. 221(2), pages 455-482.
    6. Bagliano, Fabio C. & Morana, Claudio, 2009. "International macroeconomic dynamics: A factor vector autoregressive approach," Economic Modelling, Elsevier, vol. 26(2), pages 432-444, March.
    7. Rangan Gupta & Alain Kabundi & Stephen Miller & Josine Uwilingiye, 2014. "Using large data sets to forecast sectoral employment," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 229-264, June.
    8. von Borstel, Julia & Eickmeier, Sandra & Krippner, Leo, 2016. "The interest rate pass-through in the euro area during the sovereign debt crisis," Journal of International Money and Finance, Elsevier, vol. 68(C), pages 386-402.
    9. Kemal Bagzibagli, 2014. "Monetary transmission mechanism and time variation in the Euro area," Empirical Economics, Springer, vol. 47(3), pages 781-823, November.
    10. Cristina Conflitti and Matteo Luciani, 2019. "Oil Price Pass-through into Core Inflation," The Energy Journal, International Association for Energy Economics, vol. 0(Number 6).
    11. Claudia M. Buch & Sandra Eickmeier & Esteban Prieto, 2014. "Macroeconomic Factors and Microlevel Bank Behavior," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(4), pages 715-751, June.
    12. Eickmeier, Sandra & Ng, Tim, 2011. "Forecasting national activity using lots of international predictors: An application to New Zealand," International Journal of Forecasting, Elsevier, vol. 27(2), pages 496-511, April.
    13. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2014. "Dynamic Factor Models, Cointegration and Error Correction Mechanisms," Working Papers ECARES ECARES 2014-14, ULB -- Universite Libre de Bruxelles.
    14. Tibor Szendrei & Katalin Varga, 2020. "FISS – A Factor-based Index of Systemic Stress in the Financial System," Russian Journal of Money and Finance, Bank of Russia, vol. 79(1), pages 3-34, March.
    15. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    16. Chihwa Kao & Lorenzo Trapani & Giovanni Urga, 2012. "Asymptotics for Panel Models with Common Shocks," Econometric Reviews, Taylor & Francis Journals, vol. 31(4), pages 390-439.
    17. Jörg Breitung & In Choi, 2013. "Factor models," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 11, pages 249-265, Edward Elgar Publishing.
      • In Choi & Jorg Breitung, 2011. "Factor models," Working Papers 1121, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy), revised Dec 2011.
    18. Weigand Roland & Wanger Susanne & Zapf Ines, 2018. "Factor Structural Time Series Models for Official Statistics with an Application to Hours Worked in Germany," Journal of Official Statistics, Sciendo, vol. 34(1), pages 265-301, March.
    19. Helmut Lütkepohl, 2014. "Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey," Discussion Papers of DIW Berlin 1351, DIW Berlin, German Institute for Economic Research.
    20. Huh, Hyeon-seung & Kim, David & Kim, Won Joong & Park, Cyn-Young, 2015. "A factor-augmented VAR analysis of business cycle synchronization in east Asia and implications for a regional currency union," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 449-468.

    More about this item

    Keywords

    Dynamic factor models; cointegration; structural analysis; factor-augmented error correction models; FAVAR; C32; E17;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

    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:eme:aecozz:s0731-905320150000035001. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Emerald Support (email available below). General contact details of provider: .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.