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Non-linear and Limited Dependent Regression

  • Nigel Da Costa Lewis
Part of the Finance and Capital Markets Series book series (FCMS)

Abstract

There is no guarantee that the relationship between the dependent and independent variables will be linear. On many occasions we may find the relationship to have considerable non-linearity. In such circumstances, we might attempt to use polynomial regression, logarithmic regression, exponential regression, or a more general non-linear model. This chapter introduces these models. It also discusses the use of limited dependent regression models.

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Further Reading

  1. Bates, Douglas M. and Watts, Donald G. (1988) Nonlinear Regression Analysis and Its Applications, John Wiley & Sons, New York.CrossRefGoogle Scholar
  2. Berndt, E., Hall, B., Hall, R., and Hausman, J. (1974) “Estimation and inference in nonlinear structural models,” Annals of Economic and Social Measurement, 3: 653–65.Google Scholar
  3. Cook, R. D. and Weisberg, S. (1990) “Confidence curves in nonlinear regression,” Journal of the American Statistical Association, 85: 544–51.CrossRefGoogle Scholar
  4. Gallant, A. R. (1987) Nonlinear Statistical Models, Wiley, New York.CrossRefGoogle Scholar
  5. Maddala, G. S. (1983) Limited-dependent and qualitative variables in economics, Cambridge University Press, Cambridge.CrossRefGoogle Scholar
  6. White, H. (1981) “Consequences and detection of misspecified nonlinear regression models.” Journal of the American Statistical Association, 76: 419–33.CrossRefGoogle Scholar
  7. Wolak, Frank. (1991) “The local nature of hypothesis tests involving inequality constraints in nonlinear models,” Econometrica, 59: 981–95.CrossRefGoogle Scholar

Copyright information

© Nigel Da Costa Lewis 2005

Authors and Affiliations

  • Nigel Da Costa Lewis

There are no affiliations available

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