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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. repec:spr:epolit:v:34:y:2017:i:3:d:10.1007_s40888-017-0076-0 is not listed on IDEAS
    2. 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.
    3. Juodis, Arturas & Sarafidis, Vasilis, 2014. "Fixed T Dynamic Panel Data Estimators with Multi-Factor Errors," MPRA Paper 57659, University Library of Munich, Germany.
    4. Jan F. Kiviet, 2005. "Judging Contending Estimators by Simulation: Tournaments in Dynamic Panel Data Models," Tinbergen Institute Discussion Papers 05-112/4, Tinbergen Institute.
    5. repec:wly:japmet:v:32:y:2017:i:1:p:37-55 is not listed on IDEAS
    6. John Strauss & Nayoung Lee & Geert Ridder, 2010. "Estimation of Poverty Transition Matrices with Noisy Data," Working Papers id:2796, eSocialSciences.

    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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