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Generalized Partial Linear Models

  • Wolfgang Härdle
  • Axel Werwatz
  • Marlene Müller
  • Stefan Sperlich
Part of the Springer Series in Statistics book series (SSS)

Abstract

As indicated in the overview in Chapter 5, a partial linear model (PLM) consists of two additive components, a linear and a nonparametric part:
$$ E(Y|U,T) = U^ \top \beta + m(T) $$
where β=(β1,...,β p )is a finite dimensional parameter and m(●) a smooth function. Here, we assume again a decomposition of the explanatory variables into two vectors, U and T. The vector U denotes a p-variate random vector which typically covers categorical explanatory variables or variables that are known to influence the index in a linear way. The vector T is a q-variate random vector of continuous explanatory variables which is to be modeled in a nonparametric way. Economic theory or intuition should guide you as to which regressors should be included in U or T, respectively.

Keywords

Estimation Algorithm Nonparametric Function Semiparametric Estimate Partial Linear Model Parametric Part 
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-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Wolfgang Härdle
    • 1
  • Axel Werwatz
    • 2
  • Marlene Müller
    • 3
  • Stefan Sperlich
    • 4
  1. 1.CASE — Center for Applied Statistics and Economics Wirtschaftswissenschaftliche FakultätHumboldt-Universität zu BerlinBerlinGermany
  2. 2.DIW BerlinBerlinGermany
  3. 3.Fraunhofer ITWMKaiserslauternGermany
  4. 4.Departamento de EconomíaUniversidad Carlos III de MadridGetafe (Madrid)Spain

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