IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v10y1997i3p251-66.html
   My bibliography  Save this article

Constrained Maximum Likelihood

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://journals.kluweronline.com/issn/0927-7099/contents
    Download Restriction: Access to the full text of the articles in this series is restricted.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. P. S. Sephton, 2000. "Financial analysis package for GAUSS," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(4), pages 433-438.
    5. Peter Andre, 2021. "Shallow Meritocracy: An Experiment on Fairness Views," CRC TR 224 Discussion Paper Series crctr224_2021_318v1, University of Bonn and University of Mannheim, Germany.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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).
    11. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:10:y:1997:i:3:p:251-66. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.