IDEAS home Printed from https://ideas.repec.org/p/ags/aaea15/205649.html
   My bibliography  Save this paper

Neural Network Estimators of Binary Choice Processes: Estimation, Marginal Effects and WTP

Author

Listed:
  • Bergtold, Jason S.
  • Ramsey, Steven M.

Abstract

Estimation of binary choice models typically require that the econometric model satisfy the utility maximization hypothesis. The most widely used models for this purpose are the binary logit and probit models. To satisfy the utility maximization hypothesis the logit and probit models must make a priori assumptions regarding the underlying functional form of a representative utility function. Such a theoretical restriction on a statistical model without considering the underlying probabilistic structure of the observed data can leave the postulated estimable model statistically misspecified. Feed-forward back-propagation artificial neural networks (FFBANN) provide a potentially powerful semi-nonparametric method to avoid misspecifications. This paper shows that a single-hidden layer FFBANN can be interpreted as a logistic regression with a flexible index function. An empirical application is conducted using FFBANNs to model a contingent valuation study and estimate marginal effects and willingness-to-pay. Results are used for comparison with more traditional methods such as the binary logit and probit models.

Suggested Citation

  • Bergtold, Jason S. & Ramsey, Steven M., 2015. "Neural Network Estimators of Binary Choice Processes: Estimation, Marginal Effects and WTP," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205649, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea15:205649
    DOI: 10.22004/ag.econ.205649
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/205649/files/AAEA%20ANN%20Paper%20Bergtold%20and%20Ramsey%202015.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.205649?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
    ---><---

    References listed on IDEAS

    as
    1. Cooper, Joseph C., 2002. "Flexible Functional Form Estimation of Willingness to Pay Using Dichotomous Choice Data," Journal of Environmental Economics and Management, Elsevier, vol. 43(2), pages 267-279, March.
    2. Spanos,Aris, 1999. "Probability Theory and Statistical Inference," Cambridge Books, Cambridge University Press, number 9780521424080.
    3. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    4. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    5. Gabler, Siegfried & Laisney, Francois & Lechner, Michael, 1993. "Seminonparametric Estimation of Binary-Choice Models with an Application to Labor-Force Participation," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 61-80, January.
    6. 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.
    7. Patricia M. West & Patrick L. Brockett & Linda L. Golden, 1997. "A Comparative Analysis of Neural Networks and Statistical Methods for Predicting Consumer Choice," Marketing Science, INFORMS, vol. 16(4), pages 370-391.
    8. Giuseppe De Luca, 2008. "SNP and SML estimation of univariate and bivariate binary-choice models," Stata Journal, StataCorp LP, vol. 8(2), pages 190-220, June.
    9. Eberechukwu Onukwugha & Jason Bergtold & Rahul Jain, 2015. "A Primer on Marginal Effects—Part I: Theory and Formulae," PharmacoEconomics, Springer, vol. 33(1), pages 25-30, January.
    10. Klein, Roger W & Spady, Richard H, 1993. "An Efficient Semiparametric Estimator for Binary Response Models," Econometrica, Econometric Society, vol. 61(2), pages 387-421, March.
    11. Gallant, A Ronald & Nychka, Douglas W, 1987. "Semi-nonparametric Maximum Likelihood Estimation," Econometrica, Econometric Society, vol. 55(2), pages 363-390, March.
    12. Terry L. Kastens & Allen M. Featherstone, 1996. "Feedforward Backpropagation Neural Networks in Prediction of Farmer Risk Preferences," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(2), pages 400-415.
    13. Hanemann, W Michael, 1991. "Willingness to Pay and Willingness to Accept: How Much Can They Differ?," American Economic Review, American Economic Association, vol. 81(3), pages 635-647, June.
    14. Mark Yuying An, 2000. "A Semiparametric Distribution for Willingness to Pay and Statistical Inference with Dichotomous Choice Contingent Valuation Data," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 82(3), pages 487-500.
    15. Amemiya, Takeshi, 1981. "Qualitative Response Models: A Survey," Journal of Economic Literature, American Economic Association, vol. 19(4), pages 1483-1536, December.
    16. Cosslett, Stephen R, 1983. "Distribution-Free Maximum Likelihood Estimator of the Binary Choice Model," Econometrica, Econometric Society, vol. 51(3), pages 765-782, May.
    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. Jikhan Jeong, 2020. "Identifying Consumer Preferences from User- and Crowd-Generated Digital Footprints on Amazon.com by Leveraging Machine Learning and Natural Language Processing," 2020 Papers pje208, Job Market Papers.
    2. Doremus, Jacqueline, 2020. "How does eco-label competition affect environmental benefits? The case of Central Africa's forests," Journal of Environmental Economics and Management, Elsevier, vol. 103(C).

    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. Steven M. Ramsey & Jason S. Bergtold, 2021. "Examining Inferences from Neural Network Estimators of Binary Choice Processes: Marginal Effects, and Willingness-to-Pay," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1137-1165, December.
    2. Bergtold, Jason S. & Taylor, Daniel B. & Bosch, Darrell J., 2003. "Networking Your Way to a Better Prediction: Effectively Modeling Contingent Valuation Survey Data," 2003 Annual meeting, July 27-30, Montreal, Canada 22152, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    3. Mittelhammer, Ron C. & Judge, George, 2011. "A family of empirical likelihood functions and estimators for the binary response model," Journal of Econometrics, Elsevier, vol. 164(2), pages 207-217, October.
    4. Mamine, Fateh & Fares, M'hand & Minviel, Jean Joseph, 2020. "Contract Design for Adoption of Agrienvironmental Practices: A Meta-analysis of Discrete Choice Experiments," Ecological Economics, Elsevier, vol. 176(C).
    5. Ron Mittelhammer & George Judge, 2009. "A Minimum Power Divergence Class of CDFs and Estimators for the Binary Choice Model," International Econometric Review (IER), Econometric Research Association, vol. 1(1), pages 33-49, April.
    6. Malmendier, Ulrike M. & Botsch, Matthew J., 2020. "The Long Shadows of the Great Inflation: Evidence from Residential Mortgages," CEPR Discussion Papers 14934, C.E.P.R. Discussion Papers.
    7. Chen, Heng Z. & Randall, Alan, 1997. "Semi-nonparametric estimation of binary response models with an application to natural resource valuation," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 323-340.
    8. Huang, Ju-Chin & Nychka, Douglas W. & Smith, V. Kerry, 2008. "Semi-parametric discrete choice measures of willingness to pay," Economics Letters, Elsevier, vol. 101(1), pages 91-94, October.
    9. Arana, Jorge E. & Leon, Carmelo J., 2005. "Flexible mixture distribution modeling of dichotomous choice contingent valuation with heterogenity," Journal of Environmental Economics and Management, Elsevier, vol. 50(1), pages 170-188, July.
    10. Pigini Claudia, 2015. "Bivariate Non-Normality in the Sample Selection Model," Journal of Econometric Methods, De Gruyter, vol. 4(1), pages 123-144, January.
    11. Giuseppe De Luca, 2008. "SNP and SML estimation of univariate and bivariate binary-choice models," Stata Journal, StataCorp LP, vol. 8(2), pages 190-220, June.
    12. William H. Greene & David A. Hensher, 2008. "Modeling Ordered Choices: A Primer and Recent Developments," Working Papers 08-26, New York University, Leonard N. Stern School of Business, Department of Economics.
    13. Jeff Racine, 2002. "Generalized Semiparametric Binary Prediction," Annals of Economics and Finance, Society for AEF, vol. 3(1), pages 117-134, May.
    14. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    15. Cooper, Joseph C., 2002. "Flexible Functional Form Estimation of Willingness to Pay Using Dichotomous Choice Data," Journal of Environmental Economics and Management, Elsevier, vol. 43(2), pages 267-279, March.
    16. Fosgerau, Mogens & Bierlaire, Michel, 2007. "A practical test for the choice of mixing distribution in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 41(7), pages 784-794, August.
    17. Pere Riera & Raúl Brey & Guillermo Gándara, 2008. "Bid design for non-parametric contingent valuation with a single bounded dichotomous choice format," Hacienda Pública Española / Review of Public Economics, IEF, vol. 186(3), pages 43-60, October.
    18. van den Berg, Gerard J. & van Vuuren, Aico, 2010. "The effect of search frictions on wages," Labour Economics, Elsevier, vol. 17(6), pages 875-885, December.
    19. Ye, Xin & Garikapati, Venu M. & You, Daehyun & Pendyala, Ram M., 2017. "A practical method to test the validity of the standard Gumbel distribution in logit-based multinomial choice models of travel behavior," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 173-192.
    20. 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.

    More about this item

    Keywords

    Environmental Economics and Policy; Research Methods/ Statistical Methods; Resource /Energy Economics and Policy;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ags:aaea15:205649. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.html .

    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.