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Structural FECM: Cointegration in large-scale structural FAVAR models

Author

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  • Banerjee, Anindya
  • Marcellino, Massimiliano
  • Masten, Igor

Abstract

Starting from the dynamic factor model for non-stationary data we derive the factor-augmented error correction model (FECM) and, by generalizing the Granger representation theorem, its moving-average representation. The latter is used for the identification of structural shocks and their propagation mechanism. Besides discussing contemporaneous restrictions along the lines of Bernanke et al. (2005), we show how to implement classical identification schemes based on long-run restrictions in the case of large panels. The importance of the error-correction mechanism for impulse response analysis is analysed by means of both empirical examples and simulation experiments. Our results show that the bias in estimated impulse responses in a FAVAR model is positively related to the strength of the error-correction mechanism and the cross-section dimension of the panel. We observe empirically in a large panel of US data that these features have a substantial effect on the responses of several variables to the identified real shock.

Suggested Citation

  • Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2014. "Structural FECM: Cointegration in large-scale structural FAVAR models," CEPR Discussion Papers 9858, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:9858
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    Cited by:

    1. Guglielmo Maria Caporale & Luis Alberiko Gil-Alana & Pedro Jose Piqueras Martinez, 2024. "Dynamic Factor Models and Fractional Integration—With an Application to US Real Economic Activity," Econometrics, MDPI, vol. 12(4), pages 1-14, December.
    2. Barigozzi, Matteo & Lippi, Marco & Luciani, Matteo, 2021. "Large-dimensional Dynamic Factor Models: Estimation of Impulse–Response Functions with I(1) cointegrated factors," Journal of Econometrics, Elsevier, vol. 221(2), pages 455-482.
    3. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2016. "Non-Stationary Dynamic Factor Models for Large Datasets," Finance and Economics Discussion Series 2016-024, Board of Governors of the Federal Reserve System (U.S.).
    4. Onatski, Alexei & Wang, Chen, 2019. "Extreme canonical correlations and high-dimensional cointegration analysis," Journal of Econometrics, Elsevier, vol. 212(1), pages 307-322.
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    6. Kurz-Kim, Jeong-Ryeol, 2018. "A note on the predictive power of survey data in nowcasting euro area GDP," Discussion Papers 10/2018, Deutsche Bundesbank.
    7. Favero, Carlo A. & Melone, Alessandro, 2020. "Asset Pricing vs Asset Expected Returning in Factor-Portfolio Models," CEPR Discussion Papers 14417, C.E.P.R. Discussion Papers.
    8. Carlo A. Favero & Alessandro Melone, 2019. "Asset Pricing vs Asset Expected Returning in Factor Models," Working Papers 651, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    9. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2020. "Cointegration and Error Correction Mechanisms for Singular Stochastic Vectors," Econometrics, MDPI, vol. 8(1), pages 1-23, February.
    10. Stoupos, Nikolaos & Nikas, Christos & Kiohos, Apostolos, 2023. "Turkey: From a thriving economic past towards a rugged future? - An empirical analysis on the Turkish financial markets," Emerging Markets Review, Elsevier, vol. 54(C).

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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