Advanced Search
MyIDEAS: Login to save this article or follow this journal

Forecasting with factor-augmented error correction models

Contents:

Author Info

  • Banerjee, Anindya
  • Marcellino, Massimiliano
  • Masten, Igor

Abstract

As a generalization of the factor-augmented VAR (FAVAR) and of the Error Correction Model (ECM), Banerjee and Marcellino (2009) introduced the Factor-augmented Error Correction Model (FECM). The FECM combines error-correction, cointegration and dynamic factor models, and has several conceptual advantages over the standard ECM and FAVAR models. In particular, it uses a larger dataset than the ECM and incorporates the long-run information which the FAVAR is missing because of its specification in differences. In this paper, we examine the forecasting performance of the FECM by means of an analytical example, Monte Carlo simulations and several empirical applications. We show that FECM generally offers a higher forecasting precision relative to the FAVAR, and marks a useful step forward for forecasting with large datasets.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. 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.sciencedirect.com/science/article/pii/S0169207013000368
Download Restriction: Full text for ScienceDirect subscribers only

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.

Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 30 (2014)
Issue (Month): 3 ()
Pages: 589-612

as in new window
Handle: RePEc:eee:intfor:v:30:y:2014:i:3:p:589-612

Contact details of provider:
Web page: http://www.elsevier.com/locate/ijforecast

Related research

Keywords: Forecasting; Dynamic factor models; Error correction models; Cointegration; Factor-augmented error correction models; FAVAR;

Other versions of this item:

Find related papers by JEL classification:

References

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.:
as in new window
  1. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2005. "The generalised dynamic factor model: one sided estimation and forecasting," ULB Institutional Repository 2013/10129, ULB -- Universite Libre de Bruxelles.
  2. Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
  3. Engle, Robert F & Kozicki, Sharon, 1993. "Testing for Common Features," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 11(4), pages 369-80, October.
  4. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
  5. Jushan Bai & Serena Ng, 2001. "A PANIC Attack on Unit Roots and Cointegration," Boston College Working Papers in Economics 519, Boston College Department of Economics.
  6. 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.
  7. 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.
  8. Marcellino, Massimiliano & Schumacher, Christian, 2007. "Factor-MIDAS for now- and forecasting with ragged-edge data: a model comparison for German GDP," Discussion Paper Series 1: Economic Studies 2007,34, Deutsche Bundesbank, Research Centre.
  9. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February.
  10. Todd E. Clark & Kenneth D. West, 2005. "Approximately normal tests for equal predictive accuracy in nested models," Research Working Paper, Federal Reserve Bank of Kansas City RWP 05-05, Federal Reserve Bank of Kansas City.
  11. Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, Econometric Society, vol. 55(2), pages 251-76, March.
  12. Svensson, Lars E.O. & Rudebusch , Glenn, 1998. "Policy Rules for Inflation Targeting," Seminar Papers, Stockholm University, Institute for International Economic Studies 637, Stockholm University, Institute for International Economic Studies.
  13. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
  14. 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.
  15. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2008. "Forecasting Exchange Rates with a Large Bayesian VAR," Working Papers 634, Queen Mary, University of London, School of Economics and Finance.
  16. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Boston College Working Papers in Economics 440, Boston College Department of Economics.
  17. Bai, Jushan, 2004. "Estimating cross-section common stochastic trends in nonstationary panel data," Journal of Econometrics, Elsevier, vol. 122(1), pages 137-183, September.
  18. Charles Engel & Kenneth D. West, 2004. "Exchange Rates and Fundamentals," NBER Working Papers 10723, National Bureau of Economic Research, Inc.
  19. Antonello D'Agostino & Domenico Giannone & Paolo Surico, 2005. "(Un)Predictability and Macroeconomic Stability," Macroeconomics, EconWPA 0510024, EconWPA.
  20. Xu Cheng & Peter C.B. Phillips, 2008. "Semiparametric Cointegrating Rank Selection," Cowles Foundation Discussion Papers 1658, Cowles Foundation for Research in Economics, Yale University.
  21. Anindya Banerjee & Massimiliano Marcellino, 2008. "Factor-augmented Error Correction Models," Economics Working Papers ECO2008/15, European University Institute.
  22. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
  23. Jörg Breitung & Sandra Eickmeier, 2006. "Dynamic factor models," AStA Advances in Statistical Analysis, Springer, vol. 90(1), pages 27-42, March.
  24. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, Blackwell Publishing, vol. 39(s1), pages 3-33, 02.
  25. Refet S. Gürkaynak & Andrew T. Levin & Eric T. Swanson, 2006. "Does inflation targeting anchor long-run inflation expectations? evidence from long-term bond yields in the U.S., U.K., and Sweden," Working Paper Series 2006-09, Federal Reserve Bank of San Francisco.
  26. Hendry, David F., 2006. "Robustifying forecasts from equilibrium-correction systems," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 399-426.
  27. Carlo Ambrogio Favero & Massimilano Marcellino & Francesca Neglia, . "Principal components at work: The empirical analysis of monetary policy with large datasets," Working Papers 223, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  28. Hall, Anthony D & Anderson, Heather M & Granger, Clive W J, 1992. "A Cointegration Analysis of Treasury Bill Yields," The Review of Economics and Statistics, MIT Press, vol. 74(1), pages 116-26, February.
  29. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780198774501, October.
  30. Engle, Robert F & Kozicki, Sharon, 1993. "Testing for Common Features: Reply," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 11(4), pages 393-95, October.
  31. Clements, M.P. & Hendry, D.F., 1992. "Forecasting in Cointegrated Systems," Economics Series Working Papers 99139, University of Oxford, Department of Economics.
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 in new window

Cited by:
  1. repec:ecb:ecbwps:20111428 is not listed on IDEAS
  2. Helmut Lütkepohl, 2014. "Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey," SFB 649 Discussion Papers SFB649DP2014-004, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  3. Rangan Gupta & Alain Kabundi & Stephen M. Miller & Josine Uwilingiye, 2011. "Using Large Data Sets to Forecast Sectoral Employment," Working Papers 201101, University of Pretoria, Department of Economics.
  4. Diego Bastourre & Jorge Carrera & Javier Ibarlucia & Mariano Sardi, 2012. "Common Drivers in Emerging Market Spreads and Commodity Prices," BCRA Working Paper Series 201257, Central Bank of Argentina, Economic Research Department.
  5. Buss, Ginters, 2010. "A note on GDP now-/forecasting with dynamic versus static factor models along a business cycle," MPRA Paper 22147, University Library of Munich, Germany.
  6. Bušs, Ginters, 2009. "Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach," MPRA Paper 16684, University Library of Munich, Germany.
  7. Godbout, Claudia & Lombardi, Marco J., 2012. "Short-term forecasting of the Japanese economy using factor models," Working Paper Series 1428, European Central Bank.
  8. Guillermo Carlomagnol & Antoni Espasa, 2014. "The pairwise approach to model a large set of disaggregates with common trends," Statistics and Econometrics Working Papers, Universidad Carlos III, Departamento de Estadística y Econometría ws141309, Universidad Carlos III, Departamento de Estadística y Econometría.
  9. Qin, Duo & He, Xinhua, 2012. "Modelling the impact of aggregate financial shocks external to the Chinese economy," BOFIT Discussion Papers 25/2012, Bank of Finland, Institute for Economies in Transition.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:eee:intfor:v:30:y:2014:i:3:p:589-612. 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: (Zhang, Lei).

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 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.