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Factor-augmented Error Correction Models

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  • Anindya Banerjee
  • Massimiliano Marcellino

Abstract

This paper brings together several important strands of the econometrics literature: errorcorrection, cointegration and dynamic factor models. It introduces the Factor-augmented Error Correction Model (FECM), where the factors estimated from a large set of variables in levels are jointly modelled with a few key economic variables of interest. With respect to the standard ECM, the FECM protects, at least in part, from omitted variable bias and the dependence of cointegration analysis on the specific limited set of variables under analysis. It may also be in some cases a refinement of the standard Dynamic Factor Model (DFM), since it allows us to include the error correction terms into the equations, and by allowing for cointegration prevent the errors from being non-invertible moving average processes. In addition, the FECM is a natural generalization of factor augmented VARs (FAVAR) considered by Bernanke, Boivin and Eliasz (2005) inter alia, which are specified in first differences and are therefore misspecified in the presence of cointegration. The FECM has a vast range of applicability. A set of Monte Carlo experiments and two detailed empirical examples highlight its merits in finite samples relative to standard ECM and FAVAR models. The analysis is conducted primarily within an in-sample framework, although the out-of-sample implications are also explored.

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Bibliographic Info

Paper provided by IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University in its series Working Papers with number 335.

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Date of creation: 2008
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Handle: RePEc:igi:igierp:335

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Citations

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Cited by:
  1. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2010. "Forecasting with Factor-augmented Error Correction," Discussion Papers 09-06r, Department of Economics, University of Birmingham.
  2. 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.
  3. Lombardi, Marco J. & Osbat, Chiara & Schnatz, Bernd, 2010. "Global commodity cycles and linkages a FAVAR approach," Working Paper Series 1170, European Central Bank.
  4. Ron Smith & Gylfi Zoega, 2007. "Global Factors, Unemployment Adjustment and the Natural Rate," Kiel Working Papers 1367, Kiel Institute for the World Economy.
  5. Giovanni Melina & Stefania Villa, 2011. "Fiscal Policy and Lending Relationships," Birkbeck Working Papers in Economics and Finance 1103, Birkbeck, Department of Economics, Mathematics & Statistics.
  6. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2010. "Forecasting with Factor-augmented Error Correction Models," CEPR Discussion Papers 7677, C.E.P.R. Discussion Papers.
  7. Chris Bloor & Troy Matheson, 2008. "Analysing shock transmission in a data-rich environment: A large BVAR for New Zealand," Reserve Bank of New Zealand Discussion Paper Series DP2008/09, Reserve Bank of New Zealand.
  8. 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.
  9. Claudio Morana, 2010. "Heteroskedastic Factor Vector Autoregressive Estimation of Persistent and Non Persistent Processes Subject to Structural Breaks," ICER Working Papers - Applied Mathematics Series 36-2010, ICER - International Centre for Economic Research.

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