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Constrained Maximum Likelihood

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  • Schoenberg, Ronald

Abstract

Constrained Maximum Likelihood (CML), developed at Aptech Systems, generates maximum likelihood estimates with general parametric constraints (linear or nonlinear, equality or inequality), using the sequential quadratic programming method. CML computes two classes of confidence intervals by inversion of the Wald and likelihood ratio statistics, and by simulation. The inversion techniques can produce misleading test sizes, but Monte Carlo evidence suggests this problem can be corrected under certain circumstances. Citation Copyright 1997 by Kluwer Academic Publishers.

Suggested Citation

  • Schoenberg, Ronald, 1997. "Constrained Maximum Likelihood," Computational Economics, Springer;Society for Computational Economics, vol. 10(3), pages 251-266, August.
    • Handle: RePEc:kap:compec:v:10:y:1997:i:3:p:251-66
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    Cited by:

    1. Feng, Guohua & Serletis, Apostolos, 2010. "Efficiency, technical change, and returns to scale in large US banks: Panel data evidence from an output distance function satisfying theoretical regularity," Journal of Banking & Finance, Elsevier, vol. 34(1), pages 127-138, January.
    2. Valéria Perim da Cunha & Glenda Michele Botelho & Ary Henrique Morais de Oliveira & Lorena Dias Monteiro & David Gabriel de Barros Franco & Rafael da Costa Silva, 2021. "Application of the ARIMA Model to Predict Under-Reporting of New Cases of Hansen’s Disease during the COVID-19 Pandemic in a Municipality of the Amazon Region," IJERPH, MDPI, vol. 19(1), pages 1-12, December.
    3. P. S. Sephton, 2000. "Financial analysis package for GAUSS," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(4), pages 433-438.
    4. Robert F. Engle & Aaron D. Smith, 1999. "Stochastic Permanent Breaks," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 553-574, November.
    5. Peter Andre, 2021. "Shallow Meritocracy: An Experiment on Fairness Views," ECONtribute Discussion Papers Series 115, University of Bonn and University of Cologne, Germany.
    6. Ortelli, Nicola & Hillel, Tim & Pereira, Francisco C. & de Lapparent, Matthieu & Bierlaire, Michel, 2021. "Assisted specification of discrete choice models," Journal of choice modelling, Elsevier, vol. 39(C).
    7. Tianshun Yan & Yanyong Zhao & Wentao Wang, 2020. "Likelihood-based estimation of a semiparametric time-dependent jump diffusion model of the short-term interest rate," Computational Statistics, Springer, vol. 35(2), pages 539-557, June.
    8. Ooms, M., 2008. "Trends in Applied Econometrics Software Development 1985-2008, an analysis of Journal of Applied Econometrics research articles, software reviews, data and code," Serie Research Memoranda 0021, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    9. Jolynn Pek & Hao Wu, 2015. "Profile Likelihood-Based Confidence Intervals and Regions for Structural Equation Models," Psychometrika, Springer;The Psychometric Society, vol. 80(4), pages 1123-1145, December.
    10. George Economides & Jim Malley & Apostolis Philippopoulos & Ulrich Woitek, 2003. "Electoral Uncertainty, Fiscal Policies & Growth: Theory and Evidence from Germany, the UK and the US," CESifo Working Paper Series 1072, CESifo.
    11. Nurudeen A. Adegoke & Andrew Punnett & Marti J. Anderson, 2022. "Estimation of Multivariate Dependence Structures via Constrained Maximum Likelihood," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(2), pages 240-260, June.

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