IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v15y2000i4d10.1007_pl00022716.html
   My bibliography  Save this article

Weighted Derivative Estimation of Quantal Response Models: Simulations and Applications to Choice of Truck Freight Carrier

Author

Listed:
  • Erik Bergkvist

    (Ume University)

  • Per Johansson

    (IFAU-Office of Labour Market Policy Evaluation)

Abstract

Summary Under the assumption of a single-index model the weighted average density derivative (WAD) estimator, estimates regression parameters up to scale. The small sample performance of ratio estimators are studied. For spherical errors in a latent variable specification the WAD estimator, in terms of bias and mean square error (MSE), demonstrates performance similar to the logit maximum likelihood estimator. Under heteroskedastic errors the WAD estimator performs better. In an empirical application concerning choices of freight transports we find that the WAD estimator evidences improved performance in one of the two sectors studied compared with standard parametric models.

Suggested Citation

  • Erik Bergkvist & Per Johansson, 2000. "Weighted Derivative Estimation of Quantal Response Models: Simulations and Applications to Choice of Truck Freight Carrier," Computational Statistics, Springer, vol. 15(4), pages 485-510, December.
  • Handle: RePEc:spr:compst:v:15:y:2000:i:4:d:10.1007_pl00022716
    DOI: 10.1007/PL00022716
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/PL00022716
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/PL00022716?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Bergkvist, Erik & Westin, Lars, 1998. "Regional valuation of infrastructure improvements. The case of Swedish road freight," Umeå Economic Studies 463, Umeå University, Department of Economics.
    2. W. Michael Hanemann, 1989. "Welfare Evaluations in Contingent Valuation Experiments with Discrete Response Data: Reply," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 71(4), pages 1057-1061.
    3. Chuan-Zhong Li, 1996. "Semiparametric Estimation of the Binary Choice Model for Contingent Valuation," Land Economics, University of Wisconsin Press, vol. 72(4), pages 462-473.
    4. Newey, Whitney K & Powell, James L & Walker, James R, 1990. "Semiparametric Estimation of Selection Models: Some Empirical Results," American Economic Review, American Economic Association, vol. 80(2), pages 324-328, May.
    5. Hardle, Wolfgang & Tsybakov, A. B., 1993. "How sensitive are average derivatives?," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 31-48, July.
    6. Amemiya, Takeshi, 1981. "Qualitative Response Models: A Survey," Journal of Economic Literature, American Economic Association, vol. 19(4), pages 1483-1536, December.
    7. Yatchew, Adonis & Griliches, Zvi, 1985. "Specification Error in Probit Models," The Review of Economics and Statistics, MIT Press, vol. 67(1), pages 134-139, February.
    8. Manski, Charles F., 1985. "Semiparametric analysis of discrete response : Asymptotic properties of the maximum score estimator," Journal of Econometrics, Elsevier, vol. 27(3), pages 313-333, March.
    9. Powell, James L. & Stoker, Thomas M., 1996. "Optimal bandwidth choice for density-weighted averages," Journal of Econometrics, Elsevier, vol. 75(2), pages 291-316, December.
    10. W. Michael Hanemann, 1984. "Welfare Evaluations in Contingent Valuation Experiments with Discrete Responses," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 66(3), pages 332-341.
    11. Powell, James L & Stock, James H & Stoker, Thomas M, 1989. "Semiparametric Estimation of Index Coefficients," Econometrica, Econometric Society, vol. 57(6), pages 1403-1430, November.
    12. Lee, Lung-Fei, 1982. "Specification error in multinomial logit models : Analysis of the omitted variable bias," Journal of Econometrics, Elsevier, vol. 20(2), pages 197-209, November.
    13. Hardle, Wolfgang & Tsybakov, A. B., 1993. "How sensitive are average derivatives?," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 31-48, July.
    14. Ruud, Paul A, 1983. "Sufficient Conditions for the Consistency of Maximum Likelihood Estimation Despite Misspecifications of Distribution in Multinomial Discrete Choice Models," Econometrica, Econometric Society, vol. 51(1), pages 225-228, January.
    Full references (including those not matched with items on IDEAS)

    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. Erik Bergkvist, 2001. "The value of time and forecasting of flowsin freight transportation," ERSA conference papers ersa01p271, European Regional Science Association.
    2. Ichimura, Hidehiko & Todd, Petra E., 2007. "Implementing Nonparametric and Semiparametric Estimators," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 74, Elsevier.
    3. Hanemann, W. Michael & Kanninen, Barbara, 1996. "The Statistical Analysis Of Discrete-Response Cv Data," CUDARE Working Papers 25022, University of California, Berkeley, Department of Agricultural and Resource Economics.
    4. Kaido, Hiroaki, 2017. "Asymptotically Efficient Estimation Of Weighted Average Derivatives With An Interval Censored Variable," Econometric Theory, Cambridge University Press, vol. 33(5), pages 1218-1241, October.
    5. Xia, Yingcun & Härdle, Wolfgang Karl & Linton, Oliver, 2009. "Optimal smoothing for a computationally and statistically efficient single index estimator," SFB 649 Discussion Papers 2009-028, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    6. Véronique Flambard & Pierre Lasserre & Pierre Mohnen, 2007. "Snow removal auctions in Montreal: costs, informational rents, and procurement management," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 40(1), pages 245-277, February.
    7. Kyungchul Song, 2009. "Two-Step Extremum Estimation with Estimated Single-Indices," PIER Working Paper Archive 09-012, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    8. Nishiyama, Y., 2004. "Minimum normal approximation error bandwidth selection for averaged derivatives," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(1), pages 53-61.
    9. repec:hum:wpaper:sfb649dp2009-028 is not listed on IDEAS
    10. Lewbel, Arthur, 2000. "Semiparametric qualitative response model estimation with unknown heteroscedasticity or instrumental variables," Journal of Econometrics, Elsevier, vol. 97(1), pages 145-177, July.
    11. Song, Kyungchul, 2014. "Semiparametric models with single-index nuisance parameters," Journal of Econometrics, Elsevier, vol. 178(P3), pages 471-483.
    12. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    13. Joshua Lospinoso & Michael Schweinberger & Tom Snijders & Ruth Ripley, 2011. "Assessing and accounting for time heterogeneity in stochastic actor oriented models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(2), pages 147-176, July.
    14. Linton, Oliver, 2002. "Edgeworth approximations for semiparametric instrumental variable estimators and test statistics," Journal of Econometrics, Elsevier, vol. 106(2), pages 325-368, February.
    15. Coppejans, Mark, 2001. "Estimation of the binary response model using a mixture of distributions estimator (MOD)," Journal of Econometrics, Elsevier, vol. 102(2), pages 231-269, June.
    16. Huybrechts F. Bindele & Ash Abebe & Karlene N. Meyer, 2018. "General rank-based estimation for regression single index models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(5), pages 1115-1146, October.
    17. Powell, James L. & Stoker, Thomas M., 1996. "Optimal bandwidth choice for density-weighted averages," Journal of Econometrics, Elsevier, vol. 75(2), pages 291-316, December.
    18. Yoshihiko Nishiyama & Peter M. Robinson, 2005. "The Bootstrap and the Edgeworth Correction for Semiparametric Averaged Derivatives," Econometrica, Econometric Society, vol. 73(3), pages 903-948, May.
    19. Hidehiko Ichimura & Oliver Linton, 2001. "Asymptotic expansions for some semiparametric program evaluation estimators," CeMMAP working papers 04/01, Institute for Fiscal Studies.
    20. Yixiao Jiang, 2021. "Semiparametric Estimation of a Corporate Bond Rating Model," Econometrics, MDPI, vol. 9(2), pages 1-20, May.
    21. Marian Hristache, 2002. "Are Efficient Estimators in Single-Index Models Really Efficient? A Computational Discussion," Computational Statistics, Springer, vol. 17(4), pages 453-464, December.

    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:spr:compst:v:15:y:2000:i:4:d:10.1007_pl00022716. 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: 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.