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A moving blocks empirical likelihood method for longitudinal data

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  • Jin Qiu
  • Lang Wu

Abstract

In the analysis of longitudinal or panel data, neglecting the serial correlations among the repeated measurements within subjects may lead to inefficient inference. In particular, when the number of repeated measurements is large, it may be desirable to model the serial correlations more generally. An appealing approach is to accommodate the serial correlations nonparametrically. In this article, we propose a moving blocks empirical likelihood method for general estimating equations. Asymptotic results are derived under sequential limits. Simulation studies are conducted to investigate the finite sample performances of the proposed methods and compare them with the elementwise and subject‐wise empirical likelihood methods of Wang et al. (2010, Biometrika 97, 79–93) and the block empirical likelihood method of You et al. (2006, Can. J. Statist. 34, 79–96). An application to an AIDS longitudinal study is presented.

Suggested Citation

  • Jin Qiu & Lang Wu, 2015. "A moving blocks empirical likelihood method for longitudinal data," Biometrics, The International Biometric Society, vol. 71(3), pages 616-624, September.
    • Handle: RePEc:bla:biomet:v:71:y:2015:i:3:p:616-624
    DOI: 10.1111/biom.12317
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    References listed on IDEAS

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    2. Suojin Wang & Lianfen Qian & Raymond J. Carroll, 2010. "Generalized empirical likelihood methods for analyzing longitudinal data," Biometrika, Biometrika Trust, vol. 97(1), pages 79-93.
    3. Robert C. Feenstra & Robert Inklaar & Marcel P. Timmer, 2015. "The Next Generation of the Penn World Table," American Economic Review, American Economic Association, vol. 105(10), pages 3150-3182, October.
    4. Wei Pan, 2001. "Akaike's Information Criterion in Generalized Estimating Equations," Biometrics, The International Biometric Society, vol. 57(1), pages 120-125, March.
    5. Smith, Richard J, 1997. "Alternative Semi-parametric Likelihood Approaches to Generalised Method of Moments Estimation," Economic Journal, Royal Economic Society, vol. 107(441), pages 503-519, March.
    6. Andrews, Donald W.K., 1988. "Laws of Large Numbers for Dependent Non-Identically Distributed Random Variables," Econometric Theory, Cambridge University Press, vol. 4(3), pages 458-467, December.
    7. Peng Wang & Guei-feng Tsai & Annie Qu, 2012. "Conditional Inference Functions for Mixed-Effects Models With Unspecified Random-Effects Distribution," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 725-736, June.
    8. Hansen, Lars Peter & Singleton, Kenneth J, 1982. "Generalized Instrumental Variables Estimation of Nonlinear Rational Expectations Models," Econometrica, Econometric Society, vol. 50(5), pages 1269-1286, September.
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    Cited by:

    1. Wang, Kangning & Li, Shaomin & Sun, Xiaofei & Lin, Lu, 2019. "Modal regression statistical inference for longitudinal data semivarying coefficient models: Generalized estimating equations, empirical likelihood and variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 257-276.
    2. Zhang, Yuexia & Qin, Guoyou & Zhu, Zhongyi & Zhang, Jiajia, 2022. "Empirical likelihood inference for longitudinal data with covariate measurement errors: An application to the LEAN study," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
    3. Qiu, Jin & Ma, Qing & Wu, Lang, 2019. "A moving blocks empirical likelihood method for panel linear fixed effects models with serial correlations and cross-sectional dependences," Economic Modelling, Elsevier, vol. 83(C), pages 394-405.

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