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Identification and estimation of dynamic factor models

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  • Bai, Jushan
  • Wang, Peng

Abstract

We consider a set of minimal identification conditions for dynamic factor models. These conditions have economic interpretations, and require fewer number of restrictions than when putting in a static-factor form. Under these restrictions, a standard structural vector autoregression (SVAR) with or without measurement errors can be embedded into a dynamic factor model. More generally, we also consider overidentification restrictions to achieve efficiency. General linear restrictions, either in the form of known factor loadings or cross-equation restrictions, are considered. We further consider serially correlated idiosyncratic errors with heterogeneous coefficients. A numerically stable Bayesian algorithm for the dynamic factor model with general parameter restrictions is constructed for estimation and inference. A square-root form of Kalman filter is shown to improve robustness and accuracy when sampling the latent factors. Confidence intervals (bands) for the parameters of interest such as impulse responses are readily computed. Similar identification conditions are also exploited for multi-level factor models, and they allow us to study the spill-over effects of the shocks arising from one group to another.

Suggested Citation

  • Bai, Jushan & Wang, Peng, 2012. "Identification and estimation of dynamic factor models," MPRA Paper 38434, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:38434
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    File URL: https://mpra.ub.uni-muenchen.de/38434/2/MPRA_paper_38434.pdf
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Bjørnland, Hilde C. & Ravazzolo, Francesco & Thorsrud, Leif Anders, 2017. "Forecasting GDP with global components: This time is different," International Journal of Forecasting, Elsevier, vol. 33(1), pages 153-173.
    2. David Backus & Mikhail Chernov & Stanley E. Zin, 2013. "Identifying Taylor Rules in Macro-Finance Models," NBER Working Papers 19360, National Bureau of Economic Research, Inc.
    3. Hilde C. Bjørnland & Leif Anders Thorsrud, 2015. "Commodity prices and fiscal policy design: Procyclical despite a rule," Working Papers No 5/2015, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    4. Hilde C. Bjørnland & Leif A. Thorsrud, 2016. "Boom or Gloom? Examining the Dutch Disease in Two‐speed Economies," Economic Journal, Royal Economic Society, vol. 126(598), pages 2219-2256, December.
    5. Aßmann, Christian & Boysen-Hogrefe, Jens & Pape, Markus, 2016. "Bayesian analysis of static and dynamic factor models: An ex-post approach towards the rotation problem," Journal of Econometrics, Elsevier, vol. 192(1), pages 190-206.
    6. Aßmann, Christian & Boysen-Hogrefe, Jens & Pape, Markus, 2014. "Bayesian analysis of dynamic factor models: An ex-post approach towards the rotation problem," Kiel Working Papers 1902, Kiel Institute for the World Economy (IfW).
    7. Leif Anders Thorsrud, 2016. "Words are the new numbers: A newsy coincident index of business cycles," Working Paper 2016/21, Norges Bank.
    8. Chuliá, Helena & Guillén, Montserrat & Uribe, Jorge M., 2017. "Measuring uncertainty in the stock market," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 18-33.
    9. Christian Menden & Christian R. Proaño, 2017. "Dissecting the financial cycle with dynamic factor models," IMK Working Paper 183-2017, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    10. Antolin-Diaz, Juan & Drechsel, Thomas & Petrella, Ivan, 2014. "Following the Trend: Tracking GDP when Long-Run Growth is Uncertain," CEPR Discussion Papers 10272, C.E.P.R. Discussion Papers.
    11. Sungyup Chung & Geoffrey J.D. Hewings, 2015. "Competitive and Complementary Relationship between Regional Economies: A Study of the Great Lake States," Spatial Economic Analysis, Taylor & Francis Journals, vol. 10(2), pages 205-229, June.
    12. repec:taf:quantf:v:17:y:2017:i:12:p:1965-1994 is not listed on IDEAS
    13. Helena Chuliá & Montserrat Guillén & Jorge M. Uribe, 2015. "Mortality and Longevity Risks in the United Kingdom: Dynamic Factor Models and Copula-Functions," Working Papers 2015-03, Universitat de Barcelona, UB Riskcenter.
    14. Menden, Christian & Proaño, Christian R., 2017. "Dissecting the financial cycle with dynamic factor models," BERG Working Paper Series 126, Bamberg University, Bamberg Economic Research Group.
    15. Aleksandra Halka & Grzegorz Szafrański, 2014. "What common factors are driving inflation in CEE countries?," EcoMod2014 6977, EcoMod.

    More about this item

    Keywords

    dynamic factor models; multi-level factor models; impulse response function; spill-over effects;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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