Transformation Models

  • Joel L. Horowitz
Part of the Lecture Notes in Statistics book series (LNS, volume 131)


This chapter is concerned with estimating models of the form
$$ T\left(Y\right) = X\beta+U, $$
where T is a strictly increasing function, Y is an observed dependent variable, X is an observed random vector, β is a vector of constant parameters that is conformable with X,and U is an unobserved random variable that is independent of X. T is assumed to be strictly increasing to insure that (5.1) uniquely determines Y as a function of X and U. In applied econometrics, models of the form (5.1) are used frequently for the analysis of duration data and estimation of hedonic price functions. Familiar versions of (5.1) include the proportional hazards model, the accelerated failure time model, and the Box-Cox (1964) regression model.


Maximum Likelihood Estimator Transformation Model Unobserved Heterogeneity Kernel Estimator Duration Data 
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 1998

Authors and Affiliations

  • Joel L. Horowitz
    • 1
  1. 1.Department of EconomicsUniversity of IowaIowa CityUSA

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