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Identification of Macroeconomic Factors in Large Panels

  • Lasse Bork

    ()

    (Finance Research Group, Aarhus School of Business, University of Aarhus and CREATES)

  • Hans Dewachter

    (CES, University of Leuven, RSM Rotterdam and CESIFO.)

  • Romain Houssa

    ()

    (CRED and CEREFIM, University of Namur, CES, University of Leuven)

This paper presents a dynamic factor model in which the extracted factors and shocks are given a clear economic interpretation. The economic interpretation of the factors is obtained by means of a set of over-identifying loading restrictions, while the structural shocks are estimated following standard practices in the SVAR literature. Estimators based on the EM algorithm are developped. We apply this framework to a large panel of US monthly macroeconomic series. In particular, we identify nine macroeconomic factors and discuss the economic impact of monetary policy stocks. The results are theoretically plausible and in line with other findings in the literature.

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Paper provided by Department of Economics and Business Economics, Aarhus University in its series CREATES Research Papers with number 2009-43.

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Length: 43
Date of creation: 01 Sep 2009
Date of revision:
Handle: RePEc:aah:create:2009-43
Contact details of provider: Web page: http://www.econ.au.dk/afn/

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  12. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Journal of Econometrics, Elsevier, vol. 164(1), pages 188-205, September.
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  19. Christiano, Lawrence J. & Eichenbaum, Martin & Evans, Charles L., 1999. "Monetary policy shocks: What have we learned and to what end?," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 2, pages 65-148 Elsevier.
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  24. 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, Oxford University Press, vol. 120(1), pages 387-422.
  25. Otrok, Christopher & Whiteman, Charles H, 1998. "Bayesian Leading Indicators: Measuring and Predicting Economic Conditions in Iowa," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 997-1014, November.
  26. Bork, Lasse, 2009. "Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach," Finance Research Group Working Papers F-2009-03, University of Aarhus, Aarhus School of Business, Department of Business Studies.
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