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Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach

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  • Lasse Bork

    () (Aarhus School of Business, University of Aarhus,and CREATES)

Abstract

Economy-wide effects of shocks to the US federal funds rate are estimated in a state space model with 120 US macroeconomic and financial time series driven by the dynamics of the federal funds rate and a few dynamic factors. This state space system is denoted a factor-augmented VAR (FAVAR) by Bernanke et al. (2005). I estimate the FAVAR by the fully parametric one-step EM algorithm as an alternative to the two-step principal component method and the one-step Bayesian method in Bernanke et al. (2005). The EM algorithm which is an iterative maximum likelihood method estimates all the parameters and the dynamic factors simultaneously and allows for classical inference. I demonstrate empirically that the same impulse responses but better fit emerge robustly from a low order FAVAR with eight correlated factors compared to a high order FAVAR with fewer correlated factors, for instance four factors. This empirical result accords with one of the theoretical results from Bai & Ng (2007) in which it is shown that the information in complicated factor dynamics may be substituted by panel information.

Suggested Citation

  • Lasse Bork, 2009. "Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach," CREATES Research Papers 2009-11, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2009-11
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    Cited by:

    1. André Binette & Tony Chernis, Daniel de Munnik & Daniel de Munnik, 2017. "Global Real Activity for Canadian Exports: GRACE," Discussion Papers 17-2, Bank of Canada.
    2. Hacioglu, Sinem & Tuzcuoglu, Kerem, 2016. "Interpreting the latent dynamic factors by threshold FAVAR model," Bank of England working papers 622, Bank of England.
    3. Romain Houssa & Lasse Bork & Hans Dewachter, 2008. "Identification of Macroeconomic Factors in Large Panels," Working Papers 1010, University of Namur, Department of Economics.
    4. repec:spr:empeco:v:53:y:2017:i:1:d:10.1007_s00181-017-1254-1 is not listed on IDEAS
    5. Andrés Felipe Londoño & Jorge Andrés Tamayo & Carlos Alberto Velásquez, 2012. "Dinámica de la política monetaria e inflación objetivo en Colombia: una aproximación FAVAR," Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 30(68), pages 14-71, Junio.
    6. Antonello D’Agostino & Michele Modugno & Chiara Osbat, 2017. "A Global Trade Model for the Euro Area," International Journal of Central Banking, International Journal of Central Banking, vol. 13(4), pages 1-34, December.
    7. Kemal Bagzibagli, 2014. "Monetary transmission mechanism and time variation in the Euro area," Empirical Economics, Springer, vol. 47(3), pages 781-823, November.
    8. Tony Chernis & Rodrigo Sekkel, 2017. "A dynamic factor model for nowcasting Canadian GDP growth," Empirical Economics, Springer, vol. 53(1), pages 217-234, August.
    9. repec:gam:jecnmx:v:5:y:2017:i:2:p:20-:d:98854 is not listed on IDEAS

    More about this item

    Keywords

    Monetary policy; large cross-sections; factor-augmented vector autoregression; EM algorithm; state space;

    JEL classification:

    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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