Počet záznamů: 1  

Model averaging with ridge regularization

  1. 1.
    0576123 - NHU-C 2024 CZ eng V - Výzkumná zpráva
    Skolkova, Alena
    Model averaging with ridge regularization.
    Prague: CERGE-EI, 2023. 31 s. CERGE-EI Working Paper Series, 758. ISSN 2788-0443
    Institucionální podpora: Cooperatio-COOP
    Klíčová slova: linear regression * shrinkage * model averaging
    Obor OECD: Applied Economics, Econometrics
    https://www.cerge-ei.cz/pdf/wp/Wp758.pdf

    Model averaging is an increasingly popular alternative to model selection. Ridge regression serves a similar purpose as model averaging, i.e. the minimization of mean squared error through shrinkage, though in different ways. In this paper, we propose the ridge-regularized modifications of Mallows model averaging (Hansen, 2007, Econometrica, 75) and heteroskedasticity-robust Mallows model averaging (Liu & Okui, 2013, The Econometrics Journal, 16) to leverage the capabilities of averaging and ridge regularization simultaneously. Via a simulation study, we examine the finite-sample improvements obtained by replacing least-squares with a ridge regression. Ridge-based model averaging is especially useful when one deals with sets of moderately to highly correlated predictors because the underlying ridge regression accommodates correlated predictors without blowing up estimation variance. A toy theoretical example shows that the relative reduction of mean squared error is increasing with the strength of the correlation. We also demonstrate the superiority of the ridge-regularized modifications via empirical examples focused on wages and economic growth.
    Trvalý link: https://hdl.handle.net/11104/0345726

     
     
Počet záznamů: 1  

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