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

Hyunkeun Cho, Annie Qu

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)1315-1334
Number of pages20
JournalElectronic Journal of Statistics
Volume9
DOIs
StatePublished - Jan 1 2015

Keywords

  • Generalized method of moments
  • Moment selection
  • Principal components
  • Quadratic inference function
  • Singularity matrix

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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