IDEAS home Printed from https://ideas.repec.org/p/fip/fednsr/412.html
   My bibliography  Save this paper

Dynamic hierarchical factor models

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr412.html
    Download Restriction: no
    File URL: https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr412.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Neville Francis & Michael T. Owyang & Ozge Savascin, 2017. "An endogenously clustered factor approach to international business cycles," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(7), pages 1261-1276, November.
    4. 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.
    5. Buch, Claudia M. & Eickmeier, Sandra & Prieto, Esteban, 2010. "Macroeconomic factors and micro-level bank risk," Discussion Paper Series 1: Economic Studies 2010,20, Deutsche Bundesbank.
    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. Krzysztof Beck & Karen Jackson, 2024. "International trade fluctuations: Global versus regional factors," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 57(1), pages 331-358, February.
    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. 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).
    10. 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.
    11. Stock, James H. & Watson, Mark, 2011. "Dynamic Factor Models," Scholarly Articles 28469541, Harvard University Department of Economics.
    12. Parker, Miles, 2018. "How global is “global inflation”?," Journal of Macroeconomics, Elsevier, vol. 58(C), pages 174-197.
    13. 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.
    14. Nagayasu, Jun, 2013. "Interdependence in Real Effective Exchange Rates: Evidence from the Dynamic Hierarchical Factor Model," MPRA Paper 45955, University Library of Munich, Germany.
    15. Luca Margaritella & Joakim Westerlund, 2023. "Using information criteria to select averages in CCE," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 405-421.
    16. 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.
    17. 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.
    18. 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.
    19. Hosszú, Zsuzsanna, 2018. "The impact of credit supply shocks and a new Financial Conditions Index based on a FAVAR approach," Economic Systems, Elsevier, vol. 42(1), pages 32-44.
    20. Bai, Jushan & Wang, Peng, 2012. "Identification and estimation of dynamic factor models," MPRA Paper 38434, University Library of Munich, Germany.
    21. 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.
    22. Chou, Ray Yeutien & Yen, Tso-Jung & Yen, Yu-Min, 2017. "Risk evaluations with robust approximate factor models," Journal of Banking & Finance, Elsevier, vol. 82(C), pages 244-264.
    23. 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

    Econometric models; Economic forecasting; Economic indicators; Markov processes;
    All these keywords.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fip:fednsr:412. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Gabriella Bucciarelli (email available below). General contact details of provider: https://edirc.repec.org/data/frbnyus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.