Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/247587 
Year of Publication: 
2020
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 8 [Issue:] 4 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-25
Publisher: 
MDPI, Basel
Abstract: 
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.
Subjects: 
generalized method of moments framework
instrumental variables
regularization
ridge regression
training and test samples
JEL: 
C13
C18
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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