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Statistical inference for panel dynamic simultaneous equations models

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  • Hsiao, Cheng
  • Zhou, Qiankun

Abstract

We study the identification and estimation of panel dynamic simultaneous equations models. We show that the presence of time-persistent individual-specific effects does not lead to changes in the identification conditions of traditional Cowles Commission dynamic simultaneous equations models. However, the limiting properties of the estimators depend on the way the cross-section dimension, N, or the time series dimension, T, goes to infinity. We propose three limited information estimator: panel simple instrumental variables (PIV), panel generalized two stage least squares (PG2SLS), and panel limited information maximum likelihood estimation (PLIML). We show that they are all asymptotically unbiased independent of the way of how N or T tends to infinity. Monte Carlo studies are conducted to compare the performance of the PLIML, PIV, PG2SLS, the Arellano–Bond type generalized method of moments and the Akashi–Kunitomo least variance ratio estimator. We demonstrate that the reliability of statistical inference depends critically on whether an estimator is asymptotically unbiased or not.

Suggested Citation

  • Hsiao, Cheng & Zhou, Qiankun, 2015. "Statistical inference for panel dynamic simultaneous equations models," Journal of Econometrics, Elsevier, vol. 189(2), pages 383-396.
    • Handle: RePEc:eee:econom:v:189:y:2015:i:2:p:383-396
    DOI: 10.1016/j.jeconom.2015.03.031
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    Cited by:

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    2. Chihhao Fan & Chun-Yueh Lin & Ming-Che Hu, 2019. "Empirical Framework for a Relative Sustainability Evaluation of Urbanization on the Water–Energy–Food Nexus Using Simultaneous Equation Analysis," IJERPH, MDPI, vol. 16(6), pages 1-18, March.
    3. Olatunji Abdul Shobande, 2021. "Decomposing the Persistent and Transitory Effect of Information and Communication Technology on Environmental Impacts Assessment in Africa: Evidence from Mundlak Specification," Sustainability, MDPI, vol. 13(9), pages 1-12, April.
    4. Zhang, Yonghui & Zhou, Qiankun, 2019. "Estimation for time-invariant effects in dynamic panel data models with application to income dynamics," Econometrics and Statistics, Elsevier, vol. 9(C), pages 62-77.
    5. Ling, Shiqing & McAleer, Michael & Tong, Howell, 2015. "Frontiers in Time Series and Financial Econometrics: An overview," Journal of Econometrics, Elsevier, vol. 189(2), pages 245-250.
    6. Hsiao, Cheng, 2018. "Panel models with interactive effects," Journal of Econometrics, Elsevier, vol. 206(2), pages 645-673.
    7. Arturas Juodis, 2015. "Iterative Bias Correction Procedures Revisited: A Small Scale Monte Carlo Study," UvA-Econometrics Working Papers 15-02, Universiteit van Amsterdam, Dept. of Econometrics.
    8. Dennis Gaus & Heike Link, 2020. "Economic Effects of Transportation Infrastructure Quantity and Quality: A Study of German Counties," Discussion Papers of DIW Berlin 1848, DIW Berlin, German Institute for Economic Research.
    9. Hsiao, Cheng & Zhou, Qiankun, 2018. "Incidental parameters, initial conditions and sample size in statistical inference for dynamic panel data models," Journal of Econometrics, Elsevier, vol. 207(1), pages 114-128.
    10. Cobo-Reyes, Ramón & Katz, Gabriel & Meraglia, Simone, 2019. "Endogenous sanctioning institutions and migration patterns: Experimental evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 158(C), pages 575-606.
    11. Shiyun Cao & Yonghui Zhang & Qiankun Zhou, 2021. "2SLS and IV Estimation of Dynamic Panel Models with Heterogeneous Trend," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1408-1431, December.
    12. Ling, S. & McAleer, M.J. & Tong, H., 2015. "Frontiers in Time Series and Financial Econometrics," Econometric Institute Research Papers EI 2015-07, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

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    More about this item

    Keywords

    Panel dynamic simultaneous equations; Maximum likelihood; Instrumental variable; Generalized method of moments; Multi-dimensional asymptotics;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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