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Common Correlated Effects Estimation of Heterogeneous Dynamic Panel Quantile Regression Models

Author

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  • Matthew Harding
  • Carlos Lamarche
  • M. Hashem Pesaran

Abstract

This paper proposes a quantile regression estimator for a heterogeneous panel model with lagged dependent variables and interactive effects. The paper adopts the Common Correlated Effects (CCE) approach proposed by Pesaran (2006) and Chudik and Pesaran (2015) and demonstrates that the extension to the estimation of dynamic quantile regression models is feasible under similar conditions to the ones used in the literature. We establish consistency and derive the asymptotic distribution of the new quantile regression estimator. Monte Carlo studies are carried out to study the small sample behavior of the proposed approach. The evidence shows that the estimator can significantly improve on the performance of existing estimators as long as the time series dimension of the panel is large. We present an application to the evaluation of Time-of-Use pricing using a large randomized control trial.

Suggested Citation

  • Matthew Harding & Carlos Lamarche & M. Hashem Pesaran, 2018. "Common Correlated Effects Estimation of Heterogeneous Dynamic Panel Quantile Regression Models," CESifo Working Paper Series 7211, CESifo.
    • Handle: RePEc:ces:ceswps:_7211
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    References listed on IDEAS

    as
    1. Galvao Jr., Antonio F., 2011. "Quantile regression for dynamic panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 164(1), pages 142-157, September.
    2. Chudik, Alexander & Pesaran, M. Hashem, 2015. "Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors," Journal of Econometrics, Elsevier, vol. 188(2), pages 393-420.
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    Cited by:

    1. Paulo M.M. Rodrigues & Matei Demetrescu, 2022. "Cross-Sectional Error Dependence in Panel Quantile Regressions," Working Papers w202213, Banco de Portugal, Economics and Research Department.
    2. Demetrescu, Matei & Hosseinkouchack, Mehdi & Rodrigues, Paulo M. M., 2023. "Tests of no cross-sectional error dependence in panel quantile regressions," Ruhr Economic Papers 1041, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    3. Artūras Juodis, 2022. "A regularization approach to common correlated effects estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 788-810, June.
    4. Lee, Yoonseok & Sul, Donggyu, 2023. "Depth-weighted means of noisy data: An application to estimating the average effect in heterogeneous panels," Journal of Multivariate Analysis, Elsevier, vol. 196(C).
    5. Ignace De Vos & Gerdie Everaert & Vasilis Sarafidis, 2021. "A method for evaluating the rank condition for CCE estimators," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 21/1013, Ghent University, Faculty of Economics and Business Administration.
    6. Bataka, Hodabalo, 2021. "Globalization and Environmental Pollution in Sub-Saharan Africa," African Journal of Economic Review, African Journal of Economic Review, vol. 9(1), January.
    7. Gkillas, Konstantinos & Konstantatos, Christoforos & Papathanasiou, Spyros & Wohar, Mark, 2023. "Estimation of value at risk for copper," Journal of Commodity Markets, Elsevier, vol. 32(C).
    8. Giacomo Novelli, 2022. "Energy Dependency and Long-Run Growth," Working Papers 2022.42, Fondazione Eni Enrico Mattei.
    9. Novelli, Giacomo, 2022. "Energy Dependency and Long-Run Growth," FEEM Working Papers 329650, Fondazione Eni Enrico Mattei (FEEM).
    10. Ando, Tomohiro & Li, Kunpeng & Lu, Lina, 2023. "A spatial panel quantile model with unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 232(1), pages 191-213.
    11. Cepoi, Cosmin-Octavian, 2020. "Asymmetric dependence between stock market returns and news during COVID-19 financial turmoil," Finance Research Letters, Elsevier, vol. 36(C).
    12. Jia Chen Author-Name-First: Jia & Yongcheol Shin & Chaowen Zheng, 2023. "Dynamic Quantile Panel Data Models with Interactive Effects," Economics Discussion Papers em-dp2023-06, Department of Economics, University of Reading.
    13. David Powell, 2022. "Quantile regression with nonadditive fixed effects," Empirical Economics, Springer, vol. 63(5), pages 2675-2691, November.
    14. Jose E. Gomez-Gonzalez & Jorge M. Uribe & Oscar M. Valencia, 2024. "Asymmetric Sovereign Risk: Implications for Climate Change Preparation," IREA Working Papers 202401, University of Barcelona, Research Institute of Applied Economics, revised Jan 2024.
    15. Christis Katsouris, 2023. "Optimal Estimation Methodologies for Panel Data Regression Models," Papers 2311.03471, arXiv.org, revised Nov 2023.
    16. Yang, Jisheng & Wei, Jinbao & Cai, Biqing, 2022. "Quantile unit root inference for panel data with common shocks," Economics Letters, Elsevier, vol. 219(C).
    17. Abdullah Emre Caglar & Bulent Guloglu & Ayfer Gedikli, 2022. "Moving towards sustainable environmental development for BRICS: Investigating the asymmetric effect of natural resources on CO2," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(5), pages 1313-1325, October.
    18. Razzaq, Asif & Ajaz, Tahseen & Li, Jing Claire & Irfan, Muhammad & Suksatan, Wanich, 2021. "Investigating the asymmetric linkages between infrastructure development, green innovation, and consumption-based material footprint: Novel empirical estimations from highly resource-consuming economi," Resources Policy, Elsevier, vol. 74(C).

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    More about this item

    Keywords

    common correlated effects; dynamic panel; quantile regression; smart meter; randomized experiment;
    All these keywords.

    JEL classification:

    • 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
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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