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Dynamic Hierarchical Factor Model

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Author Info

  • Emanuel Moench

    (Federal Reserve Bank of New York)

  • Serena Ng

    (Columbia University)

  • Simon Potter

    (Federal Reserve Bank of New York)

Abstract

This paper uses multilevel factor models to characterize within- and between-block variations as well as idiosyncratic noise in large dynamic panels. Block-level shocks are distinguished from genuinely common shocks, and the estimated block-level factors are easy to interpret. The framework achieves dimension reduction and yet explicitly allows for heterogeneity between blocks. The model is estimated using an MCMC algorithm that takes into account the hierarchical structure of the factors. The importance of block-level variations is illustrated in a four-level model estimated on a panel of 445 series related to different categories of real activity in the United States. © 2013 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

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Bibliographic Info

Article provided by MIT Press in its journal Review of Economics and Statistics.

Volume (Year): 95 (2013)
Issue (Month): 5 (December)
Pages: 1811-1817

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Handle: RePEc:tpr:restat:v:95:y:2013:i:5:p:1811-1817

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Web page: http://mitpress.mit.edu/journals/

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Related research

Keywords: diffusion index forecasting; monitoring; large dimensional panel;

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Cited by:
  1. Neville Francis, 2012. "The Low-Frequency Impact of Daily Monetary Policy Shock," 2012 Meeting Papers 198, Society for Economic Dynamics.
  2. Bai, Jushan & Wang, Peng, 2012. "Identification and estimation of dynamic factor models," MPRA Paper 38434, University Library of Munich, Germany.
  3. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2010. "Should macroeconomic forecasters use daily financial data and how?," University of Cyprus Working Papers in Economics 09-2010, University of Cyprus Department of Economics.
  4. Michael Kirker, 2010. "What drives core inflation? A dynamic factor model analysis of tradable and nontradable prices," Reserve Bank of New Zealand Discussion Paper Series DP2010/13, Reserve Bank of New Zealand.
  5. Beck, Guenter W. & Hubrich, Kirstin & Marcellino, Massimiliano, 2009. "On the importance of sectoral shocks for price-setting," CFS Working Paper Series 2009/32, Center for Financial Studies (CFS).
  6. Gary Koop & Dimitris Korobilis, 2011. "UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?," Working Papers 1118, University of Strathclyde Business School, Department of Economics.
  7. Nagayasu, Jun, 2013. "Interdependence in Real Effective Exchange Rates: Evidence from the Dynamic Hierarchical Factor Model," MPRA Paper 45955, University Library of Munich, Germany.
  8. Georges Bresson & Jean-Michel Etienne & Pierre Mohnen, 2011. "How important is innovation? A Bayesian factor-augmented productivity model on panel data," TEPP Working Paper 2011-06, TEPP.
  9. Neville Francis & Michael T. Owyang & Özge Savascin, 2012. "An endogenously clustered factor approach to international business cycles," Working Papers 2012-014, Federal Reserve Bank of St. Louis.
  10. Neville Francis & Eric Ghysels & Michael T. Owyang, 2011. "The low-frequency impact of daily monetary policy shocks," Working Papers 2011-009, Federal Reserve Bank of St. Louis.
  11. Hallin, Marc & Liska, Roman, 2011. "Dynamic factors in the presence of blocks," Journal of Econometrics, Elsevier, vol. 163(1), pages 29-41, July.
  12. Hubrich, Kirstin & Marcellino, Massimiliano & Beck, Günter W., 2011. "On the importance of sectoral and regional shocks for price-setting," Working Paper Series 1334, European Central Bank.
  13. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2013. "Forecasting by factors, by variables, by both or neither?," Journal of Econometrics, Elsevier, vol. 177(2), pages 305-319.

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