Generalized kernel regularized least squares estimator with parametric error covariance

Justin Dang () and Aman Ullah ()
Additional contact information
Justin Dang: University of San Diego
Aman Ullah: University of California

Empirical Economics, 2023, vol. 64, issue 6, No 22, 3059-3088

Abstract: Abstract A two-step estimator of a nonparametric regression function via Kernel regularized least squares (KRLS) with parametric error covariance is proposed. The KRLS, not considering any information in the error covariance, is improved by incorporating a parametric error covariance, allowing for both heteroskedasticity and autocorrelation, in estimating the regression function. A two step procedure is used, where in the first step, a parametric error covariance is estimated by using KRLS residuals and in the second step, a transformed model using the error covariance is estimated by KRLS. Theoretical results including bias, variance, and asymptotics are derived. Simulation results show that the proposed estimator outperforms the KRLS in both heteroskedastic errors and autocorrelated errors cases. An empirical example is illustrated with estimating an airline cost function under a random effects model with heteroskedastic and correlated errors. The derivatives are evaluated, and the average partial effects of the inputs are determined in the application.

Keywords: Nonparametric estimator; Semiparametric models; Machine learning; Kernel regularized least squares; Covariance; Heteroskedasticity; Serial correlation; C; C01; C1; C13; C14; C5; C51; C52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00181-023-02411-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:empeco:v:64:y:2023:i:6:d:10.1007_s00181-023-02411-z

Ordering information: This journal article can be ordered from
http://www.springer. ... rics/journal/181/PS2

DOI: 10.1007/s00181-023-02411-z

Access Statistics for this article

Empirical Economics is currently edited by Robert M. Kunst, Arthur H.O. van Soest, Bertrand Candelon, Subal C. Kumbhakar and Joakim Westerlund

More articles in Empirical Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().