On the Asymptotic Distribution of Ridge Regression Estimators Using Training and Test Samples
Nandana Sengupta () and
Fallaw Sowell ()
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Nandana Sengupta: School of Public Policy, Indian Institute of Technology Delhi, Delhi 110016, India
Econometrics, 2020, vol. 8, issue 4, 1-25
The asymptotic distribution of the linear instrumental variables (IV) estimator with empirically selected ridge regression penalty is characterized. The regularization tuning parameter is selected by splitting the observed data into training and test samples and becomes an estimated parameter that jointly converges with the parameters of interest. The asymptotic distribution is a nonstandard mixture distribution. Monte Carlo simulations show the asymptotic distribution captures the characteristics of the sampling distributions and when this ridge estimator performs better than two-stage least squares. An empirical application on returns to education data is presented.
Keywords: ridge regression; instrumental variables; regularization; training and test samples; generalized method of moments framework (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:8:y:2020:i:4:p:39-:d:422323
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