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Functional Data Inference in a Parametric Quantile Model applied to Lifetime Income Curves

Author

Listed:
  • JIN SEO CHO

    (Yonsei University)

  • PETER C. B. PHILLIPS

    (Yale University)

  • JUWON SEO

    (National University of Singapore)

Abstract

A parametric quantile function estimation procedure is developed for functional data. The approach involves minimizing the sum of integrated functional distances that measure the functional gap between each functional observation and the quantile curve in terms of the check function. The procedure is validated under both correctly specified and misspecified models by allowing for the presence of nuisance parameter estimation effects. Testing methodology is developed using Wald, Lagrange multiplier, and quasi-likelihood ratio procedures in this functional data setting. Finite sample performance is assessed using simulations and the methodology is applied to study how lifetime income paths differ between genders and among different education levels using continuous work history samples. The methodology enables the analysis of full career income paths with temporal and possibly persistent dependence structures embodied in the observations.The results capture both gender and education effects but these empirical differences are shown to be mitigated upon rescaling to take account of lifetime experience and job mobility.

Suggested Citation

  • Jin Seo Cho & Peter C. B. Phillips & Juwon Seo, 2023. "Functional Data Inference in a Parametric Quantile Model applied to Lifetime Income Curves," Working papers 2023rwp-211, Yonsei University, Yonsei Economics Research Institute.
  • Handle: RePEc:yon:wpaper:2023rwp-211
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Functional data; quantile function; nuisance effects; quantile inference; lifetime income path; gender and education effects.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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