Skip to main content
SpringerLink
Log in

Detection of Spurious Maxima through Random Draw Tests and Specification Tests

  • Published:
Computational Economics Aims and scope Submit manuscript

Abstract

Consistent estimation requires finding the global maximum of some specifiedobjective function. Local maxima are often easy to find, but do notnecessarily yield consistent estimates. In many nonlinear applications, theresearcher can rarely be certain that a found local maximum is global. Thispaper examines the ability of random draw tests and specification tests todetect spurious maxima. For random draw tests we analyze Veall (1990) and atest introduced here based on a generalized beta distribution. Specificationtests routinely used by researchers are also examined as methods for detectingspurious maxima. Monte Carlo results are reported on various test functions.The results suggest that specification testa are more useful than the randomdraw tests for detecting spurious maxima.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Amemiya, T. (1994). Introduction to Statistics and Econometrics. Harvard University Press, Cambridge, MA.

    Google Scholar 

  • Amemiya, T. (1985). Advanced Econometrics. Harvard University Press, Cambridge, MA.

    Google Scholar 

  • Bierens, H.J. (1990). A consistent conditional moment test of functional form. Econometrica, 58, 1443-1458.

    Google Scholar 

  • de Haan, L. (1981). Estimation of the minimum of a function using order statistics. Journal of the American Statistical Association, 76, 467-469.

    Google Scholar 

  • Dorsey, R.E. and Mayer, W.J. (1995). Genetic algorithms for estimation problems with multiple optima, nondifferentiability, and other irregular features. Journal of Business and Economic Statistics, 13, 53-66.

    Google Scholar 

  • Godfrey, L.G. (1988). Misspecification Tests in Econometrics. Cambridge University Press, Cambridge, U.K.

    Google Scholar 

  • Goffe, W.L., Ferrier, G.D. and Rogers, J. (1994). Global optimization of statistical functions with simulated annealing. Journal of Econometrics, 60, 65-99.

    Google Scholar 

  • Hall, P. and Horowitz, J.L. (1996). Bootstrap critical values for tests based on generalized methods of moments estimators. Econometrica, 64, 891-916.

    Google Scholar 

  • Hausman, J.A. (1978). Specification tests in econometrics. Econometrica, 46, 1251-1272.

    Google Scholar 

  • Judge, G.G., Griffiths, W.E., Hill, R.C., Lütkepohl, H. and Lee, T.C. (1985). The Theory and Practice of Econometrics, 2nd edn. Wiley, New York.

    Google Scholar 

  • Laroque, G. and Salanié, B. (1994). Estimating the canonical disequilibrium model. Journal of Econometrics, 62, 165-210.

    Google Scholar 

  • Lee, T., White, H. and Granger, C.W.J. (1993). Testing for neglected nonlinearity in time series models. Journal of Econometrics, 56, 269-290.

    Google Scholar 

  • MacKinnon, J.G. (1992). Model specification tests and artificial regressions. Journal of Economic Literature, XXX, 102-147.

    Google Scholar 

  • Maddala, G.S. (1983). Limited-Dependent and Qualitative Variables in Econometrics. Cambridge University Press, Cambridge, U.K.

    Google Scholar 

  • Mayer, W.J. (1989). Estimating disequilibrium models with limited a priori price-adjustment information, Journal of Econometrics, 41, 303-317.

    Google Scholar 

  • McDonald, J.B. and Xu, Y.J. (1995). A generalization of the beta distribution with applications. Journal of Econometrics, 66, 133-152.

    Google Scholar 

  • Newey, W.K. (1985). Maximum likelihood specification testing and conditional moment tests. Econometrica, 53, 1047-1070.

    Google Scholar 

  • Pagan, A. and Vella, F. (1989). Diagnostic tests for models based on individual data: a survey. Journal of Applied Econometrics, 4, s29-s59.

    Google Scholar 

  • Quandt, R.E. (1988). The Econometrics of Disequilibrium. Basil Blackwell, New York.

    Google Scholar 

  • Ramsey, J.B. (1969). Tests for specification errors in classical linear regression analysis. Journal of the Royal Statistical Society, B31, 350-371.

    Google Scholar 

  • Robinson, P.M. (1988). The stochastic difference between econometric statistics. Econometrica, 56(3), 531-548.

    Google Scholar 

  • Ruud, P. (1984). Tests of specification in econometrics. Econometric Reviews, 3, 211-242.

    Google Scholar 

  • Tauchen, G. (1985). Diagnostic testing and evaluation of maximum likelihood models. Journal of Econometrics, 30, 415-444.

    Google Scholar 

  • Veall, M.R. (1990). Testing for a global maximum in an econometric context. Econometrica, 58, 1459-1465.

    Google Scholar 

  • White, H. (1981). Consequences and detection of misspecified nonlinear regression models. Journal of American Statistical Association, 76, 419-433.

    Google Scholar 

  • White, H. (1994). Estimation, Inference and Specification Analysis. Cambridge University Press, Cambridge, U.K.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Economics and Finance, University of Mississippi, University, MS, 38677, U.S.A.

    Robert E. Dorsey & Walter J. Mayer

Authors
  1. Robert E. Dorsey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Walter J. Mayer
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dorsey, R.E., Mayer, W.J. Detection of Spurious Maxima through Random Draw Tests and Specification Tests. Computational Economics 16, 237–256 (2000). https://doi.org/10.1023/A:1008777102696

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008777102696

Keywords

Search

Navigation