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Dynamic Hierarchical Factor Model

Author

Listed:
  • 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.

Suggested Citation

  • Emanuel Moench & Serena Ng & Simon Potter, 2013. "Dynamic Hierarchical Factor Model," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1811-1817, December.
    • Handle: RePEc:tpr:restat:v:95:y:2013:i:5:p:1811-1817
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    Cited by:

    1. Georges Bresson & Jean-Michel Etienne & Pierre Mohnen, 2011. "How important is innovation? A Bayesian factor-augmented productivity model on panel data," Working Papers halshs-00812155, HAL.
    2. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2013. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 240-251, April.
    3. Claudia M. Buch & Sandra Eickmeier & Esteban Prieto, 2014. "Macroeconomic Factors and Microlevel Bank Behavior," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(4), pages 715-751, June.
    4. Francis, Neville & Owyang, Michael T. & Savascin, Özge, 2012. "An endogenously clustered factor approach to international business cycles," Working Papers 2012-014, Federal Reserve Bank of St. Louis, revised 10 Feb 2017.
    5. Banica Logica & Stefan Liviu Cristian & Jurian Mariana, 2014. "Business Intelligence For Educational Purpose," Balkan Region Conference on Engineering and Business Education, Sciendo, vol. 1(1), pages 333-338, August.
    6. Guenter W. Beck & Kirstin Hubrich & Massimiliano Marcellino, 2016. "On the Importance of Sectoral and Regional Shocks for Price‐Setting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1234-1253, November.
    7. Koop, Gary & Korobilis, Dimitris, 2011. "UK macroeconomic forecasting with many predictors: Which models forecast best and when do they do so?," Economic Modelling, Elsevier, vol. 28(5), pages 2307-2318, September.
    8. Filippo Ferroni & Benjamin Klaus, 2015. "Euro Area business cycles in turbulent times: convergence or decoupling?," Applied Economics, Taylor & Francis Journals, vol. 47(34-35), pages 3791-3815, July.
    9. Miles Parker, 2016. "Global inflation: the role of food, housing and energy prices," Reserve Bank of New Zealand Discussion Paper Series DP2016/05, Reserve Bank of New Zealand.
    10. 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.
    11. 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.
    12. 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).
    13. repec:eee:ecosys:v:42:y:2018:i:1:p:32-44 is not listed on IDEAS
    14. Bai, Jushan & Wang, Peng, 2012. "Identification and estimation of dynamic factor models," MPRA Paper 38434, University Library of Munich, Germany.
    15. Stock, James H. & Watson, Mark, 2011. "Dynamic Factor Models," Scholarly Articles 28469541, Harvard University Department of Economics.
    16. Förster, Marcel & Jorra, Markus & Tillmann, Peter, 2014. "The dynamics of international capital flows: Results from a dynamic hierarchical factor model," Journal of International Money and Finance, Elsevier, vol. 48(PA), pages 101-124.
    17. repec:eee:jmacro:v:58:y:2018:i:c:p:174-197 is not listed on IDEAS
    18. 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.
    19. Nagayasu, Jun, 2013. "Interdependence in Real Effective Exchange Rates: Evidence from the Dynamic Hierarchical Factor Model," MPRA Paper 45955, University Library of Munich, Germany.
    20. repec:eee:jbfina:v:82:y:2017:i:c:p:244-264 is not listed on IDEAS
    21. Hallin, Marc & Liska, Roman, 2011. "Dynamic factors in the presence of blocks," Journal of Econometrics, Elsevier, vol. 163(1), pages 29-41, July.

    More about this item

    Keywords

    diffusion index forecasting; monitoring; large dimensional panel;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

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