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Identification and Estimation of a Large Factor Model with Structural Instability


  • Badi H. Baltagi

    (Syracuse University)

  • Chihwa Kao

    (University of Connecticut)

  • Fa Wang

    (Shanghai University of Finance and Economics)


This paper tackles the identi cation and estimation of a high dimensional factor model with unknown number of latent factors and a single break in the number of factors and/or factor loadings occurring at unknown common date. First, we propose a least squares estimator of the change point based on the second moments of estimated pseudo factors and show that the estimation error of the proposed estimator is Op(1). We also show that the proposed estimator has some degree of robustness to misspeci cation of the number of pseudo fac-tors. With the estimated change point plugged in, consistency of the estimated number of pre and post-break factors and convergence rate of the estimated pre and post-break factor space are then established under fairly general assump-tions. The nite sample performance of our estimators is investigated using Monte Carlo experiments. JEL Classification: C13; C33 Key words: high dimensional factor model, structural change, rate of con-vergence, number of factors, model selection, factor space, panel data

Suggested Citation

  • Badi H. Baltagi & Chihwa Kao & Fa Wang, 2016. "Identification and Estimation of a Large Factor Model with Structural Instability," Working papers 2016-34, University of Connecticut, Department of Economics.
  • Handle: RePEc:uct:uconnp:2016-34

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    References listed on IDEAS

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    Cited by:

    1. Matteo Barigozzi & Lorenzo Trapani, 2017. "Sequential testing for structural stability in approximate factor models," Papers 1708.02786,
    2. repec:eee:ecolet:v:161:y:2017:i:c:p:141-145 is not listed on IDEAS

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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