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Selection inconsistency for factor-augmented regressions

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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 set. However, few studies have considered the selection of latent factors and observed covariates simultaneously in FAR. We discover that the class of information criteria suggested by Groen and Kapetanios (2013) to determine the factors and covariates fail to provide consistent model selection, especially when regressors are correlated and the cross-section dimension is small relative to the sample size. This theoretical discovery is corroborated with simulation findings that the criteria in Groen and Kapetanios (2013) tend to underestimate the factor number but overestimate the covariate number.

Suggested Citation

  • Tu, Yundong & Wang, Siwei, 2024. "Selection inconsistency for factor-augmented regressions," Economics Letters, Elsevier, vol. 241(C).
    • Handle: RePEc:eee:ecolet:v:241:y:2024:i:c:s0165176524003240
    DOI: 10.1016/j.econlet.2024.111840
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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. 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.
    4. 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.
    5. Bai, Jushan & Ng, Serena, 2013. "Principal components estimation and identification of static factors," Journal of Econometrics, Elsevier, vol. 176(1), pages 18-29.
    6. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    7. Jushan Bai & Serena Ng, 2009. "Boosting diffusion indices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 607-629.
    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.
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    Cited by:

    1. Tu, Yundong & Wang, Siwei, 2025. "Quantile prediction with factor-augmented regression: Structural instability and model uncertainty," Journal of Econometrics, Elsevier, vol. 249(PB).
    2. Tu, Yundong & Wang, Siwei, 2025. "Consistent model selection for factor-augmented regressions," Economics Letters, Elsevier, vol. 253(C).

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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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