Nonlinear Models and Generalized Method of Moments

  • Myoung-jae Lee

Abstract

Consider a nonlinear regression model
$$ y = r\left( {x,\beta } \right) + u,E\left( {u\left| x \right.} \right) = 0, $$
(1.1)
where ß is a k × 1 vector and the form of r(·) is known. In contrast to the linear model, the dimension of x is not necessarily the same as that of ß. Depending on cases, we may omit either x or ß in r(x, ß). A model more general than (1.1) is
$$ \rho (y,x,\beta ) = u,{\text{ }}E(u\left| {x) = 0} \right., $$
(1.2)
which includes (1.1) as a special case when p(y, x, ß) = y − r(x, ß).

Keywords

Nonlinear Model Moment Condition Variance Matrix Nonlinear Little Square Lagrangian Multiplier Test 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1996

Authors and Affiliations

  • Myoung-jae Lee
    • 1
  1. 1.Department of EconometricsTilburg UniversityTilburgThe Netherlands

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