Generalized kernel regularized least squares estimator with parametric error covariance

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

A chapter in Advances in Applied Econometrics, 2024, pp 603-632 from Springer

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: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:adschp:978-3-031-48385-1_22

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DOI: 10.1007/978-3-031-48385-1_22

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