IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v35y1997i2p155-164.html
   My bibliography  Save this article

Bias correction for a class of multivariate nonlinear regression models

Author

Listed:
  • Cordeiro, Gauss M.
  • Vasconcellos, Klaus L. P.

Abstract

In this paper, we derive general formulae for second-order biases of maximum likelihood estimates which can be applied to a wide class of multivariate nonlinear regression models. The class of models we consider is very rich and includes a number of commonly used models in econometrics and statistics as special cases, such as the univariate nonlinear model and the multivariate linear model. Our formulae are easy to compute and give bias-corrected maximum likelihood estimates to order n-1, where n is the sample size, by means of supplementary weighted linear regressions. They are also simple enough to be used algebraically in order to obtain closed-form expressions in special cases.

Suggested Citation

  • Cordeiro, Gauss M. & Vasconcellos, Klaus L. P., 1997. "Bias correction for a class of multivariate nonlinear regression models," Statistics & Probability Letters, Elsevier, vol. 35(2), pages 155-164, September.
    • Handle: RePEc:eee:stapro:v:35:y:1997:i:2:p:155-164
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(97)00009-6
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

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

    References listed on IDEAS

    as
    1. Gallant, A. Ronald, 1975. "Seemingly unrelated nonlinear regressions," Journal of Econometrics, Elsevier, vol. 3(1), pages 35-50, February.
    2. Cordeiro, Gauss M. & Klein, Ruben, 1994. "Bias correction in ARMA models," Statistics & Probability Letters, Elsevier, vol. 19(3), pages 169-176, February.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Alexandre Patriota & Artur Lemonte & Heleno Bolfarine, 2011. "Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model," Statistical Papers, Springer, vol. 52(2), pages 455-467, May.
    2. Di Caterina, Claudia & Kosmidis, Ioannis, 2019. "Location-adjusted Wald statistics for scalar parameters," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 126-142.
    3. Barreto-Souza, Wagner & Vasconcellos, Klaus L.P., 2011. "Bias and skewness in a general extreme-value regression model," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1379-1393, March.
    4. Artur J. Lemonte & Germán Moreno–Arenas, 2020. "Improved Estimation for a New Class of Parametric Link Functions in Binary Regression," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(1), pages 84-110, May.
    5. Ospina, Raydonal & Cribari-Neto, Francisco & Vasconcellos, Klaus L.P., 2006. "Improved point and interval estimation for a beta regression model," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 960-981, November.
    6. Zhang, Rui & Shonkwiler, J. Scott, 2017. "Bias Correction of Welfare measures in Non-Market Valuation: Comparison of the Delta Method, Jackknife and Bootstrap," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258099, Agricultural and Applied Economics Association.
    7. Patriota, Alexandre G. & Lemonte, Artur J., 2009. "Bias correction in a multivariate normal regression model with general parameterization," Statistics & Probability Letters, Elsevier, vol. 79(15), pages 1655-1662, August.
    8. Gauss Cordeiro & Lúcia Barroso, 2007. "A third-order bias corrected estimate in generalized linear models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(1), pages 76-89, May.
    9. Cordeiro, Gauss M. & Ferrari, Silvia L. P. & Uribe-Opazo, Miguel A. & Vasconcellos, Klaus L. P., 2000. "Corrected maximum-likelihood estimation in a class of symmetric nonlinear regression models," Statistics & Probability Letters, Elsevier, vol. 46(4), pages 317-328, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aslanidis, Nektarios, 2007. "Business Cycle Regimes in CEECs Production: A Threshold SURE Approach," Working Papers 2072/5318, Universitat Rovira i Virgili, Department of Economics.
    2. Yuquan Chen & Xiaohua Yu, 2022. "Estimating market power for the Chinese fluid milk market with imported products," Agribusiness, John Wiley & Sons, Ltd., vol. 38(2), pages 386-401, April.
    3. Marcela Munizaga & Sergio Jara-Díaz & Paulina Greeven & Chandra Bhat, 2008. "Econometric Calibration of the Joint Time Assignment--Mode Choice Model," Transportation Science, INFORMS, vol. 42(2), pages 208-219, May.
    4. Ferrari, Silvia L. P. & Cribari-Neto, Francisco, 1998. "On bootstrap and analytical bias corrections," Economics Letters, Elsevier, vol. 58(1), pages 7-15, January.
    5. Reinsel, Gregory C. & Cheang, Wai-Kwong, 2003. "Approximate ML and REML estimation for regression models with spatial or time series AR(1) noise," Statistics & Probability Letters, Elsevier, vol. 62(2), pages 123-135, April.
    6. Mahdi Teimouri, 2022. "bccp: an R package for life-testing and survival analysis," Computational Statistics, Springer, vol. 37(1), pages 469-489, March.
    7. Yong Bao, 2015. "Should We Demean the Data?," Annals of Economics and Finance, Society for AEF, vol. 16(1), pages 163-171, May.
    8. von Auer, Ludwig & Weinand, Sebastian, 2022. "A nonlinear generalization of the country-product-dummy method," Discussion Papers 45/2022, Deutsche Bundesbank.
    9. Paul Cashin, 1991. "A Model Of The Disaggregated Demand For Meat In Australia," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 35(3), pages 263-283, December.
    10. Stelios Arvanitis & Antonis Demos, 2015. "A class of indirect inference estimators: higher‐order asymptotics and approximate bias correction," Econometrics Journal, Royal Economic Society, vol. 18(2), pages 200-241, June.
    11. Hanrahan, Kevin F. & Westhoff, Patrick C. & Young, Robert E., II, 2001. "Trade Allocation Modeling: Comparing The Results From Armington And Locally Regular Ai Demand System Specifications Of A Uk Beef Import Demand Allocation Model," 2001 Annual meeting, August 5-8, Chicago, IL 20510, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    12. Zellner, Arnold & Ando, Tomohiro, 2010. "A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model," Journal of Econometrics, Elsevier, vol. 159(1), pages 33-45, November.
    13. W. Viscusi & William Evans, 2006. "Behavioral Probabilities," Journal of Risk and Uncertainty, Springer, vol. 32(1), pages 5-15, January.
    14. Rulon D. Pope & Richard E. Just, 1977. "On The Competitive Firm Under Production Uncertainty," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 21(2), pages 111-118, August.
    15. Julie Bertrand & Emmanuelle Comets & Marylore Chenel & France Mentré, 2012. "Some Alternatives to Asymptotic Tests for the Analysis of Pharmacogenetic Data Using Nonlinear Mixed Effects Models," Biometrics, The International Biometric Society, vol. 68(1), pages 146-155, March.
    16. Ana Rodríguez-Álvarez & Ignacio Rosal & José Baños-Pino, 2007. "The cost of strikes in the Spanish mining sector: modelling an undesirable input with a distance function," Journal of Productivity Analysis, Springer, vol. 27(1), pages 73-83, February.
    17. David E. Giles, 2012. "A Note on Improved Estimation for the Topp-Leone Distribution," Econometrics Working Papers 1203, Department of Economics, University of Victoria.
    18. F. Cribari-Neto & G.M. Cordeiro, 1995. "On Bartlett and Bartlett-Type Corrections," Econometrics 9507001, University Library of Munich, Germany.
    19. J.‐P. Chavas & A. F. Kraus, 1990. "Population Dynamics And Milk Supply Response In The Us Lake States," Journal of Agricultural Economics, Wiley Blackwell, vol. 41(1), pages 75-84, January.
    20. Christian Koziol & Philipp Koziol & Thomas Schön, 2015. "Do correlated defaults matter for CDS premia? An empirical analysis," Review of Derivatives Research, Springer, vol. 18(3), pages 191-224, October.

    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:eee:stapro:v:35:y:1997:i:2:p:155-164. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.