System of Equations

  • Hubert Gatignon


In this chapter we consider the case where several dependent variables are explained by linear relationships with other variables. Independent analysis of each relationship by ordinary least squares could result in incorrect statistical inferences either because the estimation is not efficient (a simultaneous consideration of all the explained variables may lead to more efficient estimators for the parameters) or may be biased in cases where the dependent variables influence each other.


Covariance Matrix Ordinary Little Square Exogenous Variable Endogenous Variable Rank Condition 
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Basic Technical Readings

  1. Dhrymes, P. J. (1978). Introductory econometrics. New York, NY: Springer [Chap. 6].CrossRefGoogle Scholar
  2. Judge, G. G., Griffiths, W. E., Carter Hill, R., Lutkepohl, H., & Lee, T.-C. (1985). The theory and practice of econometrics. New York, NY: John Wiley and Sons [Chap. 14 and Chap. 15].Google Scholar
  3. Morrison, D. F. (1976). Multivariate statistical methods. New York, NY: McGraw-Hill Book Company.Google Scholar
  4. Parsons, L. J., & Schultz, R. L. (1976). Marketing models and econometric research. New York, NY: North Holland.Google Scholar
  5. Theil, H. (1971). Principles of econometrics. New York, NY: John Wiley and Sons [Chap. 9 and Chap. 10].Google Scholar

Application Readings

  1. Bass, F. M. (1969). A simultaneous equation regression study of advertising and sales of cigarettes. Journal of Marketing Research, 6(3), 291–300.CrossRefGoogle Scholar
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  13. Wildt, A. (1974). Multifirm analysis of competitive decision variables. Journal of Marketing Research, 11(1), 50–62.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Hubert Gatignon
    • 1
  1. 1.INSEADFontainebleau CedexFrance

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