Skip to main content
Log in

The exact density and distribution functions of the inequality constrained and pre-test estimators

  • Articles
  • Published:
Statistical Papers Aims and scope Submit manuscript

Abstract

We consider a bivariate normal linear regression model with an inequality restriction imposed on one of the regression coefficients. The exact analytical expressions for the density and distribution functions of the inequality constrained and pre-test estimators are derived and numerically evaluated. The implications of using the inequality constrained and pre-test estimators in confidence interval construction are also discussed and explored.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Bennett, B.M. (1952), “Estimation of means on the basis of preliminary test of significance”,Annals of the Intitute of Statistical Mathematics 4, 31–43.

    Article  Google Scholar 

  • Bennett, B.M. (1956), “On the use of preliminary test in certain statistical procedures”,Annals of the Institute of Statistical Mathematics 8, 45–52.

    MATH  MathSciNet  Google Scholar 

  • Giles, D.E.A. (1992), “The exact distribution of a simple pre-test estimator”, in W.E. Griffiths, Bock, M.E. and Lütkepohl, H. (eds.),Readings in Econometric Theory and Practice: In Honour of George Judge, Amsterdam, North-Holland, 57–74.

    Google Scholar 

  • Giles, D.E.A. and Srivastava, V.K. (1993), “The exact distribution of a least squares regression coefficient estimator after a preliminary t test”,Statistics and Probability Letters 16, 59–64.

    Article  MATH  MathSciNet  Google Scholar 

  • Giles, J.A. and Giles, D.E.A. (1993), “Pre-test estimation and testing in Econometrics: recent developments”,Journal of Economic Surveys 7, 145–197.

    Article  Google Scholar 

  • Judge, G.G. and Bock, M.E. (1978),The Statistical Implications of Pre-test and Stein-Rule Estimators in Econometrics, Amsterdam, North Holland.

    MATH  Google Scholar 

  • Judge, G.G. and Bock, M.E. (1983), “Biased estimation,” in Griliches, Z. and Intrillgator, M.D. (eds),Handbook of Econometrics, Amsterdam, North-Holland, 599–649.

    Google Scholar 

  • Judge, G.G. and Yancey, T.A. (1981), “Sampling properties of an inequality restricted estimator”,Economics Letters 7, 327–333.

    Article  MathSciNet  Google Scholar 

  • Judge, G.G. and Yancey, T.A. (1986),Improved Methods of Inference in Econometrics, Amsterdam, North Holland.

    MATH  Google Scholar 

  • Kurumai, H. and Ohtani, K. (1994), “The exact density and distribution functions of a pre-test estimator of the error variance in a linear regression model with proxy variables”, mimeo., Faculty of Economics, Kobe University.

  • Lovell, M.C. and Prescott, E. (1970), “Multiple regression with inequality constraints: pre-testing bias, hypothesis testing and efficiency”,Journal of the American Statistical Association 65, 913–925.

    Article  Google Scholar 

  • NAG (1991), Numerical Algorithm Group Ltd.,NAG Fortran Library Manual, Mark 15, Vol 1, New York: NAG Inc.

    Google Scholar 

  • Ohtani, K. (1991), “Small sample properties of the interval constrained least squares estimator when error terms have a multivariate t distribution”,Journal of the Japan Statistical Society 21, 197–204.

    MathSciNet  Google Scholar 

  • Ohtani, K. and Giles, J.A. (1996), “The density function and MSE dominance of the pre-test estimator in a heteroscedastic linear regression model with omitted variables”,Statistical Papers, forthcoming.

  • Press, W.H., S.A. Teukolsky W.T. Vetterling and B.P. Flannery, (1992).Numerical Recipes: The Art of Scientific Computing. New York: Cambridge University Press.

    Google Scholar 

  • Thomson, M. (1982), “Some results on the statistical properties of an inequality constrained least squares estimator in a linear regression model with two regressors”,Journal of Econometrics 19, 215–231.

    Article  MathSciNet  Google Scholar 

  • Thomson, M. and Schmidt, P. (1982), “A note on the comparison of the mean square error of inequality constrained least squares and other related estimators”,Review of Economics and Statistics 64, 174–176.

    Article  Google Scholar 

  • Wan, A.T.K. (1994a), “The sampling performance of inequality restricted and pre-test estimators in a mis-specified linear model”,Australian Journal of Statistics 36, 313–325.

    Article  MATH  MathSciNet  Google Scholar 

  • Wan, A.T.K. (1994b), “Risk comparison of the inequality constrained least squares and other related estimators under balanced loss”,Economics Letters 46, 203–210.

    Article  MathSciNet  Google Scholar 

  • Wan, A.T.K. (1995), “The optimal critical value of a pre-test for an inequality restriction in a mis-specified regression model”,Australian Journal of Statistics 37, 73–82.

    Article  MATH  Google Scholar 

  • Yancey, T.A. and Bohrer, R. (1992), “Risk and power for inequality pre-test estimators: general case in two dimensions”, in Griffiths, W.E. Bock, M.E. and Lütkepohl, H. (eds.),Readings in Econometric Theory and Practice: In Honour of George Judge, Amsterdam, North-Holland, 33–55.

    Google Scholar 

  • Yancey, T.A., and Judge, G.G. and Bohrer, R. (1989), “Sampling performance of some joint one-sided preliminary test estimators under squared error loss”,Econometrica 57, 1221–1228.

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wan, A.T.K. The exact density and distribution functions of the inequality constrained and pre-test estimators. Statistical Papers 38, 329–341 (1997). https://doi.org/10.1007/BF02925272

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02925272

Keywords

Navigation