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Partially linear functional-coefficient dynamic panel data models: sieve estimation and specification testing

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  • Yonghui Zhang
  • Qiankun Zhou

Abstract

We study the nonparametric estimation and specification testing for partially linear functional-coefficient dynamic panel data models, where the effects of some covariates on the dependent variable vary nonparametrically according to a set of low-dimensional variables. Based on the sieve approximation of unknown slope functions, we propose a sieve 2SLS procedure to estimate the model. The asymptotic properties of the estimators of both parametric and nonparametric components are established when sample size N and T tend to infinity jointly. A nonparametric specification test for the constancy of slopes is also proposed. We show that after being appropriately standardized, the test is asymptotically normally distributed under the null hypothesis. The asymptotic properties of the test is also studied under a sequence of local Pitman alternatives and global alternatives. A set of Monte Carlo simulations show that our sieve 2SLS estimators and specification test perform remarkably well in finite samples. We apply our method to study the impact of income on democracy, and find strong evidence of nonlinear/nonconstant effect of income on democracy.

Suggested Citation

  • Yonghui Zhang & Qiankun Zhou, 2021. "Partially linear functional-coefficient dynamic panel data models: sieve estimation and specification testing," Econometric Reviews, Taylor & Francis Journals, vol. 40(10), pages 983-1006, November.
    • Handle: RePEc:taf:emetrv:v:40:y:2021:i:10:p:983-1006
    DOI: 10.1080/07474938.2021.1889175
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    Cited by:

    1. Lee, Chien-Chiang & Yuan, Zihao, 2024. "Impact of energy poverty on public health: A non-linear study from an international perspective," World Development, Elsevier, vol. 174(C).
    2. Xu, Xin & Huang, Shupei & An, Haizhong, 2022. "The dynamic moderating function of the exchange rate market on the oil-stock nexus," International Review of Financial Analysis, Elsevier, vol. 81(C).
    3. Tan, Ruipeng & Xu, Mengmeng & Qiao, Gang & Wu, Huaqing, 2023. "FDI, financial market development and nonlinearities of energy and environmental efficiency in China: Evidence from both parametric and nonparametric models," Energy Economics, Elsevier, vol. 119(C).
    4. Tan, Ruipeng & Pan, Lulu & Xu, Mengmeng & He, Xinju, 2022. "Transportation infrastructure, economic agglomeration and non-linearities of green total factor productivity growth in China: Evidence from partially linear functional coefficient model," Transport Policy, Elsevier, vol. 129(C), pages 1-13.
    5. Wang, Wei & Xiao, Weiwei & Bai, Caiquan, 2022. "Can renewable energy technology innovation alleviate energy poverty? Perspective from the marketization level," Technology in Society, Elsevier, vol. 68(C).
    6. Luojia Wang & Kerui Du & Bin Fang & Rob Law, 2023. "Escape from air pollution: How does air quality in the place of residence shape tourism consumption?," Tourism Economics, , vol. 29(4), pages 1074-1099, June.
    7. Ting Xu & Zhike Lv, 2023. "The effect of tourism on the shadow economy: The level of economic development is key," Tourism Economics, , vol. 29(8), pages 2129-2142, December.
    8. Cai, Hechang & Wang, Zilong & Zhang, Zhiwen & Xu, Liuyang, 2023. "Does environmental regulation promote technology transfer? Evidence from a partially linear functional-coefficient panel model," Economic Modelling, Elsevier, vol. 124(C).
    9. Xu, Yujie & Wang, Yinan, 2023. "Has financial development made income more equal? – From the perspective of regional development imbalance," International Review of Financial Analysis, Elsevier, vol. 90(C).
    10. Lee, Chien-Chiang & He, Zhi-Wen & Yuan, Zihao, 2023. "A pathway to sustainable development: Digitization and green productivity," Energy Economics, Elsevier, vol. 124(C).

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