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Models with Endogenous Regressors

  • Laszlo Balazsi
  • Maurice J. G. Bun
  • Felix Chan
  • Mark N. Harris
Chapter
Part of the Advanced Studies in Theoretical and Applied Econometrics book series (ASTA, volume 50)

Abstract

This chapter examines various estimation and testing issues concerning models with endogenous regressors. The complexity of these issues increases as the number of potential unobserved heterogeneities increases with the dimension of the data. The chapter examines the properties of least squares type estimators, including theWithin estimator, under different specifications of the error components and different correlation assumptions with the regressors. The latter induces different types of endogeneity not studied previously. In terms of estimation, the chapter includes an extension to the well-known Hausman-Taylor estimator for models with multiple dimensions. It also proposes a set of valid orthogonality conditions for purposes of implementing Generalised Method of Moments (GMM) estimators under these different specifications and endogeneity assumptions. The theoretical results in this chapter identify consistent and efficient estimators for different specifications. These results allow an extension of the Hausman specification test to detect endogeneity in multi-dimensional panel data models. Other issues, such as mixed effects models, self-flow, incomplete data and higher dimensional models, will also be discussed.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Laszlo Balazsi
    • 1
  • Maurice J. G. Bun
    • 2
  • Felix Chan
    • 3
  • Mark N. Harris
    • 3
  1. 1.Central European UniversityBudapestHungary
  2. 2.University of AmsterdamAmsterdamthe Netherlands
  3. 3.Curtin UniversityPerthAustralia

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