Efficient Estimation Using the Characteristic Function
47 Pages Posted: 19 Oct 2013
Date Written: July 18, 2013
Abstract
The method of moments proposed by Carrasco and Florens (2000) permits to fully exploit the information contained in the characteristic function and yields an estimator which is asymptotically as efficient as the maximum likelihood estimator. However, this estimation procedure depends on a regularization or tuning parameter ∝ that needs to be selected. The aim of the present paper is to provide a way to optimally choose ∝ by minimizing the approximate mean square error (AMSE) of the estimator. Following an approach similar to that of Newey and Smith (2004), we derive a higher-order expansion of the estimator from which we characterize the finite sample dependence of the AMSE on ∝. We provide a data-driven procedure for selecting the regularization parameter that relies on parametric bootstrap. We show that this procedure delivers a root T consistent estimator of ∝. Moreover, the data-driven selection of the regularization parameter preserves the consistency, asymptotic normality and efficiency of the CGMM estimator. Simulation experiments based on a CIR model show the relevance of the proposed approach.
Keywords: Conditional moment restriction, continuum of moment conditions, generalized method of moments, mean square error, stochastic expansion, Tikhonov regularization
JEL Classification: C00, C13, C15
Suggested Citation: Suggested Citation
Do you have negative results from your research you’d like to share?
Recommended Papers
-
Estimation of Continuous-Time Processes via the Empirical Characteristic Function
By George J. Jiang and John Knight
-
Empirical Characteristic Function Estimation and its Applications
By Jun Yu
-
Empirical Characteristic Function in Time Series Estimation
By John Knight and Jun Yu
-
By Marine Carrasco, Mikhail Chernov, ...
-
Estimation of Stable Distributions by Indirect Inference
By René Garcia, Eric Renault, ...
-
Cross Validated Snp Density Estimates
By Mark Coppejans and A. Ronald Gallant
-
The Method of Simulated Quantiles
By Yves Dominicy and David Veredas
-
Indirect Estimation of Elliptical Stable Distributions
By Marco J. Lombardi and David Veredas