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Dynamic factors in the presence of blocks

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Author Info

  • Hallin, Marc
  • Liska, Roman

Abstract

Macroeconometric data often come under the form of large panels of time series, themselves decomposing into smaller but still quite large subpanels or blocks. We show how the dynamic factor analysis method proposed in Forni et al. (2000), combined with the identification method of Hallin and Liska (2007), allows for identifying and estimating joint and block-specific common factors. This leads to a more sophisticated analysis of the structures of dynamic interrelations within and between the blocks in such datasets, along with an informative decomposition of explained variances. The method is illustrated with an analysis of a dataset of Industrial Production Indices for France, Germany, and Italy.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 163 (2011)
Issue (Month): 1 (July)
Pages: 29-41

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Handle: RePEc:eee:econom:v:163:y:2011:i:1:p:29-41

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Web page: http://www.elsevier.com/locate/jeconom

Related research

Keywords: Panel data High dimensional time series data Dynamic factor model Dynamic principal components;

References

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  2. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Boston College Working Papers in Economics 440, Boston College Department of Economics.
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Citations

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Cited by:
  1. Hallin, Marc & Mathias, Charles & Pirotte, Hugues & Veredas, David, 2011. "Market liquidity as dynamic factors," Journal of Econometrics, Elsevier, vol. 163(1), pages 42-50, July.
  2. Luciani, Matteo, 2014. "Forecasting with approximate dynamic factor models: The role of non-pervasive shocks," International Journal of Forecasting, Elsevier, vol. 30(1), pages 20-29.
  3. Heaton, Chris & Solo, Victor, 2012. "Estimation of high-dimensional linear factor models with grouped variables," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 348-367.
  4. Helmut Lütkepohl, 2014. "Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey," SFB 649 Discussion Papers SFB649DP2014-004, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  5. Jörg Breitung & Sandra Eickmeier, 2014. "Analyzing business and financial cycles using multi-level factor models," CAMA Working Papers 2014-43, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  6. In Choi & Jorg Breitung, 2011. "Factor models," Working Papers 1121, Research Institute for Market Economy, Sogang University, revised Dec 2011.

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