 Minimum normal approximation error bandwidth selection for averaged derivativesMathematics and Computers in Simulation (MATCOM), 2004, vol. 64, issue 1, 53-61 Abstract: Density-weighted averaged derivative estimator gives a computationally convenient consistent and asymptotically normally (CAN) distributed estimate of the parametric component of a semiparametric single index model. This model includes some important parametric models as special cases such as linear regression, Logit/Probit, Tobit and Box–Cox and other transformation models. This estimator involves a nonparametric kernel density estimate and thus it faces the problem of bandwidth selection. A reasonable way of bandwidth selection for point estimation is one minimizing the mean squared error. Alternatively, for the purposes of hypothesis testing and confidence interval estimation, we may like to choose it such that it minimizes the normal approximation error. The purpose of this paper is to propose a new bandwidth suitable for these purposes by minimizing the normal approximation error in the tail of exact distribution of the statistics using higher order asymptotic theory of Edgeworth expansion or bootstrap method. Keywords: Semiparametric averaged derivatives; Higher order asymptotic theory; Minimum normal approximation error; Bandwidth selection (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:64:y:2004:i:1:p:53-61 DOI: 10.1016/S0378-4754(03)00120-4 Access Statistics for this article Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier |
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