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Partial Factor Modeling: Predictor-Dependent Shrinkage for Linear Regression

Author

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  • P. Richard Hahn
  • Carlos M. Carvalho
  • Sayan Mukherjee

Abstract

We develop a modified Gaussian factor model for the purpose of inducing predictor-dependent shrinkage for linear regression. The new model predicts well across a wide range of covariance structures, on real and simulated data. Furthermore, the new model facilitates variable selection in the case of correlated predictor variables, which often stymies other methods.

Suggested Citation

  • P. Richard Hahn & Carlos M. Carvalho & Sayan Mukherjee, 2013. "Partial Factor Modeling: Predictor-Dependent Shrinkage for Linear Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 999-1008, September.
    • Handle: RePEc:taf:jnlasa:v:108:y:2013:i:503:p:999-1008
    DOI: 10.1080/01621459.2013.779843
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    Cited by:

    1. Philippe Goulet Coulombe, 2020. "The Macroeconomy as a Random Forest," Papers 2006.12724, arXiv.org, revised Mar 2021.
    2. Hansen, Christian & Liao, Yuan, 2019. "The Factor-Lasso And K-Step Bootstrap Approach For Inference In High-Dimensional Economic Applications," Econometric Theory, Cambridge University Press, vol. 35(3), pages 465-509, June.
    3. Matthew F. Dixon & Nicholas G. Polson & Kemen Goicoechea, 2022. "Deep Partial Least Squares for Empirical Asset Pricing," Papers 2206.10014, arXiv.org.
    4. Philippe Goulet Coulombe, 2021. "The Macroeconomy as a Random Forest," Working Papers 21-05, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    5. Korobilis, Dimitris, 2018. "Machine Learning Macroeconometrics A Primer," Essex Finance Centre Working Papers 22666, University of Essex, Essex Business School.

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