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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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    References listed on IDEAS

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    1. Bai, Jushan & Ng, Serena, 2013. "Principal components estimation and identification of static factors," Journal of Econometrics, Elsevier, vol. 176(1), pages 18-29.
    2. Jack Fosten, 2017. "Model selection with estimated factors and idiosyncratic components," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(6), pages 1087-1106, September.
    3. Jan J. J. Groen & George Kapetanios, 2013. "Model Selection Criteria for Factor-Augmented Regressions-super-," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(1), pages 37-63, February.
    4. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    5. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    6. Seung C. Ahn & Alex R. Horenstein, 2013. "Eigenvalue Ratio Test for the Number of Factors," Econometrica, Econometric Society, vol. 81(3), pages 1203-1227, May.
    7. Tu, Yundong & Wang, Siwei, 2025. "Consistent model selection for factor-augmented regressions," Economics Letters, Elsevier, vol. 253(C).
    8. Zhaoxing Gao & Ruey S. Tsay, 2023. "Divide-and-Conquer: A Distributed Hierarchical Factor Approach to Modeling Large-Scale Time Series Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(544), pages 2698-2711, October.
    9. Antoine A. Djogbenou, 2021. "Model selection in factor-augmented regressions with estimated factors," Econometric Reviews, Taylor & Francis Journals, vol. 40(5), pages 470-503, April.
    10. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
    11. Jushan Bai & Serena Ng, 2006. "Confidence Intervals for Diffusion Index Forecasts and Inference for Factor-Augmented Regressions," Econometrica, Econometric Society, vol. 74(4), pages 1133-1150, July.
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    Cited by:

    1. Bellocca, Gian Pietro Enzo & Garrón Vedia, Ignacio & Rodríguez Caballero, Carlos Vladimir & Ruiz Ortega, Esther, 2026. "The empirical distribution of sequential LS factors in Multi-level Dynamic Factor Models," DES - Working Papers. Statistics and Econometrics. WS 49336, Universidad Carlos III de Madrid. Departamento de Estadística.

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    Keywords

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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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