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Forecasting with Factor-Augmented Error Correction Models

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  • Anindya Banerjee
  • Massimiliano Marcellino
  • Igor Masten

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 standard ECM and FAVAR models. In particular, it uses a larger dataset 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 examine the forecasting performance of the FECM by means of an analytical example, Monte Carlo simula- tions and several empirical applications. We show that relative to the FAVAR, FECM generally offers a higher forecasting precision and in general marks a very useful step forward for forecasting with large datasets.

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Bibliographic Info

Paper provided by Department of Economics, University of Birmingham in its series Discussion Papers with number 09-06.

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Length: 41 pages
Date of creation: Jun 2009
Date of revision:
Handle: RePEc:bir:birmec:09-06

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Postal: Edgbaston, Birmingham, B15 2TT
Web page: http://www.economics.bham.ac.uk
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Keywords: Forecasting; Dynamic Factor Models; Error Correction Models; Cointegration; Factor-augmented Error Correction Models; FAVAR;

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References

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Citations

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Cited by:
  1. Claudia Godbout & Marco J. Lombardi, 2012. "Short-Term Forecasting of the Japanese Economy Using Factor Models," Working Papers 12-7, Bank of Canada.
  2. repec:ecb:ecbwps:20111428 is not listed on IDEAS
  3. Rangan Gupta & Alain Kabundi & Stephen M. Miller & Josine Uwilingiye, 2011. "Using Large Data Sets to Forecast Sectoral Employment," Working Papers 1106, University of Nevada, Las Vegas , Department of Economics.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. Guillermo Carlomagnol & Antoni Espasa, 2014. "The pairwise approach to model a large set of disaggregates with common trends," Statistics and Econometrics Working Papers ws141309, Universidad Carlos III, Departamento de Estadística y Econometría.
  9. 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.

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