HIGHER ORDER ASYMPTOTIC THEORY FOR MINIMUM CONTRAST ESTIMATORS OF SPECTRAL PARAMETERS OF STATIONARY PROCESSES

Masanobu Taniguchi, Kees Jan van Garderen () and Madan L. Puri

Econometric Theory, 2003, vol. 19, issue 06, pages 984-1007

Abstract: Let g( ) be the spectral density of a stationary process and let f ( ), , be a fitted spectral model for g( ). A minimum contrast estimator of is defined that minimizes a distance between , where is a nonparametric spectral density estimator based on n observations. It is known that is asymptotically Gaussian efficient if g( ) = f ( ). Because there are infinitely many candidates for the distance function , this paper discusses higher order asymptotic theory for in relation to the choice of D. First, the second-order Edgeworth expansion for is derived. Then it is shown that the bias-adjusted version of is not second-order asymptotically efficient in general. This is in sharp contrast with regular parametric estimation, where it is known that if an estimator is first-order asymptotically efficient, then it is automatically second-order asymptotically efficient after a suitable bias adjustment (e.g., Ghosh, 1994, Higher Order Asymptotics, p. 57). The paper establishes therefore that for semiparametric estimation it does not hold in general that first-order efficiency implies second-order efficiency. The paper develops verifiable conditions on D that imply second-order efficiency.This paper was written while the first author was visiting the University of Bristol as a Benjamin Meaker Professor. The second author was previously at Bristol and is now supported by a fellowship of the Royal Netherlands Academy of Arts and Sciences. We are grateful to the co-editor Pentti Saikkonen and two anonymous referees for their valuable comments, which significantly improved the paper.

Date: 2003

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