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Projection estimators for autoregressive panel data models

Author

Listed:
  • Stephen Bond

    (Institute for Fiscal Studies and Nuffield College, Oxford)

  • Frank Windmeijer

    (Institute for Fiscal Studies and University of Bristol)

Abstract

In this paper we explore a new approach to estimation for autoregressive panel data models, based on projecting the unobserved individual effects on the vector of observations on the lagged dependent variable. This approach yields estimators which coincide with known generalised method of moments (GMM) estimators for models where stationarity is not imposed on the initial conditions and for models which satisfy mean stationarity. Our approach allows us to obtain a simple linear estimator for models which satisfy covariance stationarity, which although not fully efficient performs very well in simulations.

Suggested Citation

  • Stephen Bond & Frank Windmeijer, 2001. "Projection estimators for autoregressive panel data models," CeMMAP working papers CWP06/01, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:06/01
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    File URL: http://cemmap.ifs.org.uk/wps/cwp0106.pdf
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    Cited by:

    1. Wang, Xue & Fan, Li-Wei & Zhang, Hongyan, 2023. "Policies for enhancing patent quality: Evidence from renewable energy technology in China," Energy Policy, Elsevier, vol. 180(C).
    2. Po-Chin Wu & Chia-Jui Chang, 2017. "Nonlinear impacts of debt ratio and term spread on inward FDI performance persistence," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 34(3), pages 369-388, December.
    3. Artūras Juodis & Vasilis Sarafidis, 2018. "Fixed T dynamic panel data estimators with multifactor errors," Econometric Reviews, Taylor & Francis Journals, vol. 37(8), pages 893-929, September.
    4. Lima, Rita, 2016. "Capitale umano, innovazione tecnologica e divari economici nell’era post-knowledge? Un’analisi econometrica a livello sub nazionale [Human capital, technological innovation and economic gaps in the," MPRA Paper 70539, University Library of Munich, Germany.
    5. Jan F. Kiviet, 2005. "Judging Contending Estimators by Simulation: Tournaments in Dynamic Panel Data Models," Tinbergen Institute Discussion Papers 05-112/4, Tinbergen Institute.
    6. Nayoung Lee & Geert Ridder & John Strauss, 2017. "Estimation of Poverty Transition Matrices with Noisy Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 37-55, January.
    7. Wali Ullah, 2017. "Evolving corporate governance and firms performance: evidence from Japanese firms," Economics of Governance, Springer, vol. 18(1), pages 1-33, February.
    8. Christis Katsouris, 2023. "Optimal Estimation Methodologies for Panel Data Regression Models," Papers 2311.03471, arXiv.org, revised Nov 2023.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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