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Dynamic hierarchical factor models

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  • Emanuel Moench
  • Serena Ng
  • Simon M. Potter

Abstract

This paper uses multi-level 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 a Markov chain Monte-Carlo algorithm that takes into account the hierarchical structure of the factors. We organize a panel of 447 series into blocks according to the timing of data releases and use a four-level model to study the dynamics of real activity at both the block and aggregate levels. While the effect of the economic downturn of 2007-09 is pervasive, growth cycles are synchronized only loosely across blocks. The state of the leading and the lagging sectors, as well as that of the overall economy, is monitored in a coherent framework.

Suggested Citation

  • Emanuel Moench & Serena Ng & Simon M. Potter, 2009. "Dynamic hierarchical factor models," Staff Reports 412, Federal Reserve Bank of New York.
  • Handle: RePEc:fip:fednsr:412
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    Cited by:

    1. Loschmann, Craig & Parsons, Christopher R. & Siegel, Melissa, 2015. "Does Shelter Assistance Reduce Poverty in Afghanistan?," World Development, Elsevier, vol. 74(C), pages 305-322.
    2. Elena Andreou & Bas J. M. Werker, 2012. "An Alternative Asymptotic Analysis of Residual-Based Statistics," The Review of Economics and Statistics, MIT Press, pages 88-99.
    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. Zhiguo He & Péter Kondor, 2016. "Inefficient Investment Waves," Econometrica, Econometric Society, vol. 84, pages 735-780, March.
    5. 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.
    6. Gérard Ballot & Fathi Fakhfakh & Fabrice Galia & Ammon Salter, 2011. "The Fateful Triangle Complementarities between product, process and organizational innovation in the UK and France," Working Papers halshs-00812141, HAL.
    7. Banica Logica & Stefan Liviu Cristian & Jurian Mariana, 2014. "Business Intelligence For Educational Purpose," Balkan Region Conference on Engineering and Business Education, De Gruyter Open, pages 333-338.
    8. Leonardo Gambacorta & David Marques‐Ibanez, 2011. "The bank lending channel: lessons from the crisis," Economic Policy, CEPR;CES;MSH, pages 135-182.
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    12. 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.
    13. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2013. "Forecasting by factors, by variables, by both or neither?," Journal of Econometrics, Elsevier, pages 305-319.
    14. 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.
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    16. Bai, Jushan & Wang, Peng, 2012. "Identification and estimation of dynamic factor models," MPRA Paper 38434, University Library of Munich, Germany.
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    19. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2013. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Journal of Business & Economic Statistics, Taylor & Francis Journals, pages 240-251.
    20. 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, pages 101-124.
    21. Parker, Miles, 2016. "The impact of disasters on inflation," Working Paper Series 1982, European Central Bank.
    22. 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.
    23. Nagayasu, Jun, 2013. "Interdependence in Real Effective Exchange Rates: Evidence from the Dynamic Hierarchical Factor Model," MPRA Paper 45955, University Library of Munich, Germany.
    24. repec:eee:jbfina:v:82:y:2017:i:c:p:244-264 is not listed on IDEAS
    25. Hallin, Marc & Liska, Roman, 2011. "Dynamic factors in the presence of blocks," Journal of Econometrics, Elsevier, pages 29-41.

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    Keywords

    Econometric models ; Economic forecasting ; Economic indicators ; Markov processes;

    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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