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Projection estimators for autoregressive panel data models

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  • Stephen Bond
  • Frank Windmeijer

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 generalized method of moments 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. Copyright Royal Economic Society, 2002

Suggested Citation

  • Stephen Bond & Frank Windmeijer, 2002. "Projection estimators for autoregressive panel data models," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 457-479, June.
  • Handle: RePEc:ect:emjrnl:v:5:y:2002:i:2:p:457-479
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    Cited by:

    1. Arturas Juodis & Sarafidis, V., 2014. "Fixed T Dynamic Panel Data Estimators with Multi-Factor Errors," UvA-Econometrics Working Papers 14-07, Universiteit van Amsterdam, Dept. of Econometrics.
    2. Jan F. Kiviet, 2005. "Judging Contending Estimators by Simulation: Tournaments in Dynamic Panel Data Models," Tinbergen Institute Discussion Papers 05-112/4, Tinbergen Institute.
    3. repec:wly:japmet:v:32:y:2017:i:1:p:37-55 is not listed on IDEAS
    4. 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.
    5. repec:spr:epolit:v:34:y:2017:i:3:d:10.1007_s40888-017-0076-0 is not listed on IDEAS
    6. 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.

    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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