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Evaluating Restricted Common Factor models for non-stationary data

Author

Listed:
  • Francesca Di Iorio

    (University of Naples Federico II)

  • Stefano Fachin

    ("Sapienza" University of Rome)

Abstract

We propose to evaluate restrictions on the loadings of approximate Factor models comparing the estimated number of factors of the unconstrained and constrained models. A difference between the two estimates is evidence against the constraints, which should thus be rejected. To take into account possible finite sample bias of the model selection procedure, we develop a bootstrap algorithm for the estimation of the probability of rejecting cor- rect constraints. For non-stationary factor models we show analytically that the algorithm is asymptotically valid, and by simulation that the evaluation procedure has good small sample properties.

Suggested Citation

  • Francesca Di Iorio & Stefano Fachin, 2017. "Evaluating Restricted Common Factor models for non-stationary data," DSS Empirical Economics and Econometrics Working Papers Series 2017/2, Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome.
    • Handle: RePEc:sas:wpaper:20172
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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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