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Forecasting with Factor-augmented Error Correction Models

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  • Igor Masten
  • Massimiliano Marcellino
  • Anindya Banerjeey

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 di¤erences. 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 relative to the FAVAR, FECM generally o¤ers 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 European University Institute in its series RSCAS Working Papers with number 2009/32.

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Date of creation: 25 Jun 2009
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Handle: RePEc:rsc:rsceui:2009/32

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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. Rangan Gupta & Alain Kabundi & Stephen M. Miller & Josine Uwilingiye, 2011. "Using Large Data Sets to Forecast Sectoral Employment," Working papers 2011-02, University of Connecticut, Department of Economics, revised Aug 2012.
  2. 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.
  3. 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.
  4. Godbout, Claudia & Lombardi, Marco J., 2012. "Short-term forecasting of the Japanese economy using factor models," Working Paper Series 1428, European Central Bank.
  5. repec:ecb:ecbwps:20111428 is not listed on IDEAS
  6. 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.
  7. 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.
  8. 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.

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