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Estimation and model identification of longitudinal data time-varying nonparametric models

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  • Liu, Shu
  • You, Jinhong
  • Lian, Heng

Abstract

In this paper, we consider nonparametric regression modeling for longitudinal data. An important modeling choice is that the covariate effect may change dynamically with time by using a bivariate link function. Comparing with Jiang and Wang (2010, 2011), and Zhang et al. (2013) we make two distinct contributions to this important class of models. First, we show theoretically and empirically that taking the within-subject correlation into account can improve the estimation efficiency for the bivariate link function. Second, we propose a novel method involving a shrinkage estimation technique to identify consistently whether the effect of covariates is time-varying. Simulation studies are conducted to assess the finite-sample performance and a real data example is analyzed to illustrate the proposed methods.

Suggested Citation

  • Liu, Shu & You, Jinhong & Lian, Heng, 2017. "Estimation and model identification of longitudinal data time-varying nonparametric models," Journal of Multivariate Analysis, Elsevier, vol. 156(C), pages 116-136.
    • Handle: RePEc:eee:jmvana:v:156:y:2017:i:c:p:116-136
    DOI: 10.1016/j.jmva.2017.02.003
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    References listed on IDEAS

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    Cited by:

    1. Apergis, Nicholas & Polemis, Michael, 2018. "Electricity supply shocks and economic growth across the US states: evidence from a time-varying Bayesian panel VAR model, aggregate and disaggregate energy sources," MPRA Paper 84954, University Library of Munich, Germany.

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