Numerical implementation of the QuEST function

Arbeitspapier

This paper deals with certain estimation problems involving the covariance matrix in large dimensions. Due to the breakdown of finite-dimensional asymptotic theory when the dimension is not negligible with respect to the sample size, it is necessary to resort to an alternative framework known as large-dimensional asymptotics. Recently, Ledoit and Wolf (2015) have proposed an estimator of the eigenvalues of the population covariance matrix that is consistent according to a mean-square criterion under large-dimensional asymptotics. It requires numerical inversion of a multivariate nonrandom function which they call the QuEST function. The present paper explains how to numerically implement the QuEST function in practice through a series of six successive steps. It also provides an algorithm to compute the Jacobian analytically, which is necessary for numerical inversion by a nonlinear optimizer. Monte Carlo simulations document the effectiveness of the code.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Working Paper ; No. 215

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Estimation: General ; Optimization Techniques; Programming Models; Dynamic Analysis ; Econometric Software
• Thema: Large-dimensional asymptotics ; numerical optimization ; random matrix theory ; spectrum estimation

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Ledoit, Olivier ; Wolf, Michael
• Ereignis: Veröffentlichung
• (wer): University of Zurich, Department of Economics
• (wo): Zurich
• (wann): 2017

Weitere Informationen

• DOI: doi:10.5167/uzh-120492
• Handle: http://hdl.handle.net/10419/162418
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ