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Variable Selection for a Categorical Varying-Coefficient Model with Identifications for Determinants of Body Mass Index

Author

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  • Jiti Gao
  • Bin Peng
  • Zhao Ren
  • Xiaohui Zhang

Abstract

In this paper, we propose a variable selection procedure based on the shrinkage estimation technique for a categorical varying-coefficient model. We apply the method to identify the relevant determinants for body mass index (BMI) from a large amount of potential factors proposed in the multidisciplinary literature, using data from the 2013 National Health Interview Survey in the United States. We quantify the varying impacts of the relevant determinants of BMI across demographic groups.

Suggested Citation

  • Jiti Gao & Bin Peng & Zhao Ren & Xiaohui Zhang, 2015. "Variable Selection for a Categorical Varying-Coefficient Model with Identifications for Determinants of Body Mass Index," Monash Econometrics and Business Statistics Working Papers 21/15, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2015-21
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    File URL: https://www.monash.edu/__data/assets/pdf_file/0010/925948/wp21-15-1.pdf
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    References listed on IDEAS

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    1. John Cawley & Stephanie von Hinke Kessler Scholder, 2013. "The Demand for Cigarettes as Derived from the Demand for Weight Control," NBER Working Papers 18805, National Bureau of Economic Research, Inc.
    2. QI Li & Desheng Ouyang & Jeffrey S. Racine, 2013. "Categorical semiparametric varying‐coefficient models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(4), pages 551-579, June.
    3. Dardanoni, Valentino & Modica, Salvatore & Peracchi, Franco, 2011. "Regression with imputed covariates: A generalized missing-indicator approach," Journal of Econometrics, Elsevier, vol. 162(2), pages 362-368, June.
    4. Daniel L. Millimet & Rusty Tchernis, 2013. "Estimation Of Treatment Effects Without An Exclusion Restriction: With An Application To The Analysis Of The School Breakfast Program," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(6), pages 982-1017, September.
    5. Jiti Gao & Peter C.B. Phillips, 2013. "Functional Coefficient Nonstationary Regression," Cowles Foundation Discussion Papers 1911, Cowles Foundation for Research in Economics, Yale University.
    6. Heather Antecol & Kelly Bedard, 2006. "Unhealthy assimilation: Why do immigrants converge to American health status levels?," Demography, Springer;Population Association of America (PAA), vol. 43(2), pages 337-360, May.
    7. Wang, Lifeng & Li, Hongzhe & Huang, Jianhua Z., 2008. "Variable Selection in Nonparametric Varying-Coefficient Models for Analysis of Repeated Measurements," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1556-1569.
    8. Cawley, John (ed.), 2011. "The Oxford Handbook of the Social Science of Obesity," OUP Catalogue, Oxford University Press, number 9780199736362.
    9. Joshua C. C. Chan & Justin L. Tobias, 2015. "Priors and Posterior Computation in Linear Endogenous Variable Models with Imperfect Instruments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 650-674, June.
    10. Hu, Yingyao & Sasaki, Yuya, 2015. "Closed-form estimation of nonparametric models with non-classical measurement errors," Journal of Econometrics, Elsevier, vol. 185(2), pages 392-408.
    11. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    12. Chen, Jia & Gao, Jiti & Li, Degui, 2012. "Semiparametric trending panel data models with cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 171(1), pages 71-85.
    13. Brendan Kline & Justin L. Tobias, 2008. "The wages of BMI: Bayesian analysis of a skewed treatment-response model with nonparametric endogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(6), pages 767-793.
    14. Zhang, Qi & Wang, Youfa, 2004. "Socioeconomic inequality of obesity in the United States: do gender, age, and ethnicity matter?," Social Science & Medicine, Elsevier, vol. 58(6), pages 1171-1180, March.
    15. Li, Qi & Racine, Jeffrey S., 2010. "Smooth Varying-Coefficient Estimation And Inference For Qualitative And Quantitative Data," Econometric Theory, Cambridge University Press, vol. 26(6), pages 1607-1637, December.
    16. Michael Delgado, 2013. "A smooth coefficient model of carbon emissions," Empirical Economics, Springer, vol. 45(3), pages 1049-1071, December.
    17. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
    18. Nathan P. Hendricks & Aaron Smith, 2015. "Grouped coefficients to reduce bias in heterogeneous dynamic panel models with small T," Applied Economics, Taylor & Francis Journals, vol. 47(40), pages 4335-4348, August.
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    Cited by:

    1. Fatemeh Tajik & Mingzheng Wang & Xiaohui Zhang & Jie Han, 2020. "Evaluation of the impact of body mass index on venous thromboembolism risk factors," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-17, July.

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    More about this item

    Keywords

    ody Mass Index; Obesity; Varying-Coefficient; Variable Selection;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • I15 - Health, Education, and Welfare - - Health - - - Health and Economic Development

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