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Consistent model selection for factor-augmented regression within hierarchical factor structures

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  • Tu, Yundong
  • Zheng, Jinsha

Abstract

This paper addresses the underexplored challenge of model selection in factor-augmented regression for distributed high-dimensional data, where information is structured across hierarchical layers of global common factors and group-specific components. We propose a unified framework integrating two-layer hierarchical factor estimation with novel information criteria, designed to account for the slower convergence rates and diverging factor dimensions inherent in distributed architectures. Theoretical analysis establishes selection consistency, while simulations and macroeconomic applications unequivocally demonstrate that the proposed criteria outperform traditional criteria in forecasting accuracy and computational efficiency, underscoring its practical utility in distributed high-dimensional data modeling.

Suggested Citation

  • Tu, Yundong & Zheng, Jinsha, 2025. "Consistent model selection for factor-augmented regression within hierarchical factor structures," Economics Letters, Elsevier, vol. 257(C).
    • Handle: RePEc:eee:ecolet:v:257:y:2025:i:c:s0165176525005348
    DOI: 10.1016/j.econlet.2025.112697
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    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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