Electronic Journal of Statistics

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

Hyunkeun Cho and Annie Qu

Full-text: Open access

Abstract

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

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

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

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1434988475

Digital Object Identifier
doi:10.1214/15-EJS1036

Mathematical Reviews number (MathSciNet)
MR3358326

Zentralblatt MATH identifier
1327.62370

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

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

Citation

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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