Electronic Journal of Statistics

Efficient estimation for longitudinal data by combining large-dimensional moment conditions

Hyunkeun Cho and Annie Qu

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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.

Article information

Electron. J. Statist., Volume 9, Number 1 (2015), 1315-1334.

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

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62H25: Factor analysis and principal components; correspondence analysis

Generalized method of moments moment selection principal components quadratic inference function singularity matrix


Cho, Hyunkeun; Qu, Annie. Efficient estimation for longitudinal data by combining large-dimensional moment conditions. Electron. J. Statist. 9 (2015), no. 1, 1315--1334. doi:10.1214/15-EJS1036. https://projecteuclid.org/euclid.ejs/1434988475

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