Open Access
2015 Efficient estimation for longitudinal data by combining large-dimensional moment conditions
Hyunkeun Cho, Annie Qu
Electron. J. Statist. 9(1): 1315-1334 (2015). DOI: 10.1214/15-EJS1036


The quadratic inference function approach is able to provide a consistent and efficient estimator if valid moment conditions are available. However, the QIF estimator is unstable when the dimension of moment conditions is large compared to the sample size, due to the singularity problem for the estimated weighting matrix. We propose a new estimation procedure which combines all valid moment conditions optimally via the spectral decomposition of the weighting matrix. In theory, we show that the proposed method yields a consistent and efficient estimator which follows an asymptotic normal distribution. In addition, Monte Carlo studies indicate that the proposed method performs well in the sense of reducing bias and improving estimation efficiency. A real data example of Fortune 500 companies is used to compare the performance of the new method with existing methods.


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Hyunkeun Cho. Annie Qu. "Efficient estimation for longitudinal data by combining large-dimensional moment conditions." Electron. J. Statist. 9 (1) 1315 - 1334, 2015.


Received: 1 September 2014; Published: 2015
First available in Project Euclid: 22 June 2015

zbMATH: 1327.62370
MathSciNet: MR3358326
Digital Object Identifier: 10.1214/15-EJS1036

Primary: 62H25

Keywords: generalized method of moments , moment selection , principal components , quadratic inference function , singularity matrix

Rights: Copyright © 2015 The Institute of Mathematical Statistics and the Bernoulli Society

Vol.9 • No. 1 • 2015
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