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Consistent model selection for factor-augmented regressions

Author

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  • Tu, Yundong
  • Wang, Siwei

Abstract

Factor-augmented regression (FAR) is an effective tool in forming predictions in the presence of big data sets. However, few studies have considered the selection of latent factors and observed covariates simultaneously in FAR. This paper addresses this issue and introduces a new set of information criteria for factor selection and covariate selection jointly. In particular, we demonstrate that the factor estimation error will not only influence the factor selection, but also the covariate selection in FAR. As a result, the penalty used to ensure consistent model selection should depend on both the cross-sectional dimension and the time length, to account for the effect of factor estimation error. Selection consistency is then proved under standard regularity conditions. The simulation results demonstrate the nice performance of the proposed criteria.

Suggested Citation

  • Tu, Yundong & Wang, Siwei, 2025. "Consistent model selection for factor-augmented regressions," Economics Letters, Elsevier, vol. 253(C).
    • Handle: RePEc:eee:ecolet:v:253:y:2025:i:c:s0165176525001685
    DOI: 10.1016/j.econlet.2025.112331
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    4. Jushan Bai & Serena Ng, 2009. "Boosting diffusion indices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 607-629.
    5. Tu, Yundong & Wang, Siwei, 2024. "Selection inconsistency for factor-augmented regressions," Economics Letters, Elsevier, vol. 241(C).
    6. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    7. 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.
    8. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    9. 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. Tingting Cheng & Jiachen Cong & Fei Liu & Xuanbin Yang, 2025. "Binary Response Forecasting under a Factor-Augmented Framework," Papers 2507.16462, arXiv.org.

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    More about this item

    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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