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Penalized Quantile Regression with Semiparametric Correlated Effects: Applications with Heterogeneous Preferences

Author

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  • Harding, Matthew

    (University of California, Irvine)

  • Lamarche, Carlos

    (University of Kentucky)

Abstract

This paper proposes new ?1-penalized quantile regression estimators for panel data, which explicitly allows for individual heterogeneity associated with covariates. We conduct Monte Carlo simulations to assess the small sample performance of the new estimators and provide comparisons of new and existing penalized estimators in terms of quadratic loss. We apply the techniques to two empirical studies. First, the new method is applied to the estimation of labor supply elasticities and we find evidence that positive substitution effects dominate negative wealth effects at the middle of the conditional distribution of hours. The overall effect tends to be larger at the lower tail, which suggests that changes in taxes have different effects across the response distribution. Second, we estimate consumer preferences for nutrients from a demand model using a large scanner dataset of household food purchases. We show that preferences for nutrients vary across the conditional distribution of expenditure and across genders, and emphasize the importance of fully capturing consumer heterogeneity in demand modeling. Both applications highlight the importance of estimating individual heterogeneity when designing economic policy.

Suggested Citation

  • Harding, Matthew & Lamarche, Carlos, 2013. "Penalized Quantile Regression with Semiparametric Correlated Effects: Applications with Heterogeneous Preferences," IZA Discussion Papers 7741, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp7741
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    Cited by:

    1. Lamarche, Carlos & Parker, Thomas, 2023. "Wild bootstrap inference for penalized quantile regression for longitudinal data," Journal of Econometrics, Elsevier, vol. 235(2), pages 1799-1826.
    2. Hartley, Robert Paul & Lamarche, Carlos, 2018. "Behavioral responses and welfare reform: Evidence from a randomized experiment," Labour Economics, Elsevier, vol. 54(C), pages 135-151.
    3. Jiaying Gu & Stanislav Volgushev, 2018. "Panel Data Quantile Regression with Grouped Fixed Effects," Papers 1801.05041, arXiv.org, revised Aug 2018.
    4. Galvao, Antonio F. & Gu, Jiaying & Volgushev, Stanislav, 2020. "On the unbiased asymptotic normality of quantile regression with fixed effects," Journal of Econometrics, Elsevier, vol. 218(1), pages 178-215.
    5. Harding, Matthew & Lamarche, Carlos, 2019. "A panel quantile approach to attrition bias in Big Data: Evidence from a randomized experiment," Journal of Econometrics, Elsevier, vol. 211(1), pages 61-82.
    6. Zongwu Cai & Meng Shi & Yue Zhao & Wuqing Wu, 2020. "Testing Financial Hierarchy Based on A PDQ-CRE Model," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202011, University of Kansas, Department of Economics, revised Jul 2020.
    7. Al Rababa'a, Abdel Razzaq & Alomari, Mohammad & Mensi, Walid & Matar, Ali & Saidat, Zaid, 2021. "Does tracking the infectious diseases impact the gold, oil and US dollar returns and correlation? A quantile regression approach," Resources Policy, Elsevier, vol. 74(C).
    8. Battagliola, Maria Laura & Sørensen, Helle & Tolver, Anders & Staicu, Ana-Maria, 2022. "A bias-adjusted estimator in quantile regression for clustered data," Econometrics and Statistics, Elsevier, vol. 23(C), pages 165-186.
    9. Alomari, Mohammad & Al Rababa'a, Abdel Razzaq & Ur Rehman, Mobeen & Power, David M., 2022. "Infectious diseases tracking and sectoral stock market returns: A quantile regression analysis," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    10. Panayiotis Tzeremes, 2022. "The Asymmetric Effects of Regional House Prices in the UK: New Evidence from Panel Quantile Regression Framework," Studies in Microeconomics, , vol. 10(1), pages 7-22, June.
    11. Gu, Jiaying & Volgushev, Stanislav, 2019. "Panel data quantile regression with grouped fixed effects," Journal of Econometrics, Elsevier, vol. 213(1), pages 68-91.

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    More about this item

    Keywords

    labor supply; quantile regression; panel data; shrinkage; scanner data;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply

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