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Misspecification Testing

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

Abstract

Arguably, one of the most important issues in regression modeling applied to risk management is the correct specification of the regression equation. How can we assess the validity of the pre-specified regression model, which will provide the basis of statistical inference and practical decisions? It turns out that statistical inference concerning the linear regression model depends crucially on the “validity” of the underlying statistical assumptions of the model. If the assumed underlying statistical assumptions are invalid the inference based on it will be unreliable. The primary objective of this chapter is to outline the key assumptions of the linear regression model and provide some elementary techniques for validating or refuting these assumptions given a specific data set.

Keywords

Regression Model Linear Regression Model Standardize Residual Large Standard Error Best Linear Unbiased Estimator 
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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Further Reading

  1. Doran, H. E. (1989) Applied Regression Analysis in Econometrics, Marcel Dekker, Inc., New York.Google Scholar
  2. Neter, J., Kutner, M. H., Nachtsheim, C. J., and Wasserman, W. (1996) Applied Linear Regression Models (3rd edn), Richard D. Irwin, Inc., Chicago, IL.Google Scholar
  3. Weisberg, S. (1985) Applied Linear Regression, John Wiley and Sons., New York.Google Scholar

Copyright information

© Nigel Da Costa Lewis 2005

Authors and Affiliations

  • Nigel Da Costa Lewis

There are no affiliations available

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