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Time-varying Factor-augmented Forecasting Models with Variable Selection

Author

Listed:
  • Xiyuan Liu

    (School of Economics and Management, Tshinghua University, Beijing, Beijing 100084, China)

  • Zongwu Cai

    (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA)

  • Liangjun Su

    (School of Economics and Management, Tshinghua University, Beijing, Beijing 100084, China)

Abstract

We study a novel time-varying (TV) factor-augmented (FA) forecasting model, where the forecast target is driven by a strict subset of all the latent factors driving the predictors. To consistently select the target-related factors and estimate the TV parameters simultaneously, we first obtain the unobserved common factors via the local principal component analysis. Next, we conduct a variable selection procedure via a time-varying weighted group least absolute shrinkage and selection operator to select relevant factors. The identification restrictions used in this paper permit asymptotically rotation-free estimation of both factors and loadings. The asymptotic properties, such as consistency, sparsity and the oracle property of these two-step estimators are established. Simulation studies demonstrate the excellent finite sample performance of the proposed estimators. In an empirical application to the U.S. macroeconomic dataset, we show that the penalized TV-FA forecasting model outperforms the conventional TV-FAVAR model in predicting certain key macroeconomic series

Suggested Citation

  • Xiyuan Liu & Zongwu Cai & Liangjun Su, 2025. "Time-varying Factor-augmented Forecasting Models with Variable Selection," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202515, University of Kansas, Department of Economics, revised Sep 2025.
    • Handle: RePEc:kan:wpaper:202515
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    File URL: https://kuwpaper.ku.edu/2025Papers/202515.pdf
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    References listed on IDEAS

    as
    1. Hansen, Bruce E., 2008. "Uniform Convergence Rates For Kernel Estimation With Dependent Data," Econometric Theory, Cambridge University Press, vol. 24(3), pages 726-748, June.
    2. Bai, Jushan & Ng, Serena, 2013. "Principal components estimation and identification of static factors," Journal of Econometrics, Elsevier, vol. 176(1), pages 18-29.
    3. Su, Liangjun & Wang, Xia, 2017. "On time-varying factor models: Estimation and testing," Journal of Econometrics, Elsevier, vol. 198(1), pages 84-101.
    4. Liangjun Su & Xia Wang, 2020. "Corrigendum to “On Time-varying Factor Models: Estimation and Testing” [J. Econometrics 198 (2017) 84-101]," Economics and Statistics Working Papers 8-2020, Singapore Management University, School of Economics.
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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