IDEAS home Printed from https://ideas.repec.org/a/ags/jlaare/31040.html
   My bibliography  Save this article

Simulated Maximum Likelihood For Double-Bounded Referendum Models

Author

Listed:
  • Riddel, Mary C.

Abstract

Although joint estimation of referendum-type contingent value (CV) survey responses using maximum-likelihood models is preferred to single-equation estimation, it has been largely disregarded because estimation involves evaluating multivariate normal probabilities. New developments in the construction of probability simulators have addressed this problem, and simulated maximum likelihood (SML) for multiple-good models is now possible. This analysis applies SML for a three-good model under a double-bounded questioning format. Results indicate joint estimation substantially improves the variances of the parameters and willingness-to-pay estimates.

Suggested Citation

  • Riddel, Mary C., 2001. "Simulated Maximum Likelihood For Double-Bounded Referendum Models," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 26(2), pages 1-17, December.
  • Handle: RePEc:ags:jlaare:31040
    DOI: 10.22004/ag.econ.31040
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/31040/files/26020491.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.31040?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. Borsch-Supan, Axel & Hajivassiliou, Vassilis A., 1993. "Smooth unbiased multivariate probability simulators for maximum likelihood estimation of limited dependent variable models," Journal of Econometrics, Elsevier, vol. 58(3), pages 347-368, August.
    2. Alberini Anna, 1995. "Efficiency vs Bias of Willingness-to-Pay Estimates: Bivariate and Interval-Data Models," Journal of Environmental Economics and Management, Elsevier, vol. 29(2), pages 169-180, September.
    3. Mary Riddel & John Loomis, 1998. "Joint Estimation of Multiple CVM Scenarios under a Double Bounded Questioning Format," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 12(1), pages 77-98, July.
    4. Cameron Trudy Ann & Quiggin John, 1994. "Estimation Using Contingent Valuation Data from a Dichotomous Choice with Follow-Up Questionnaire," Journal of Environmental Economics and Management, Elsevier, vol. 27(3), pages 218-234, November.
    5. Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
    6. Geweke, John & Keane, Michael P & Runkle, David, 1994. "Alternative Computational Approaches to Inference in the Multinomial Probit Model," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 609-632, November.
    7. Cameron, Trudy Ann, 1988. "A new paradigm for valuing non-market goods using referendum data: Maximum likelihood estimation by censored logistic regression," Journal of Environmental Economics and Management, Elsevier, vol. 15(3), pages 355-379, September.
    8. Geweke, John & Keane, Michael P & Runkle, David, 1994. "Alternative Computational Approaches to Inference in the Multinomial Probit Model," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 609-632, November.
    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. Shiko Maruyama, 2009. "Estimating Sequential-move Games by a Recursive Conditioning Simulator," Discussion Papers 2009-01, School of Economics, The University of New South Wales.
    2. W. Kuiper & Anton Cozijnsen, 2011. "The Performance of German Firms in the Business-Related Service Sectors Revisited: Differential Evolution Markov Chain Estimation of the Multinomial Probit Model," Computational Economics, Springer;Society for Computational Economics, vol. 37(4), pages 331-362, April.
    3. Geweke, John F. & Keane, Michael P. & Runkle, David E., 1997. "Statistical inference in the multinomial multiperiod probit model," Journal of Econometrics, Elsevier, vol. 80(1), pages 125-165, September.
    4. Maruyama, Shiko, 2014. "Estimation of finite sequential games," Journal of Econometrics, Elsevier, vol. 178(2), pages 716-726.
    5. Xuemei Fu & Zhicai Juan, 2017. "Estimation of multinomial probit-kernel integrated choice and latent variable model: comparison on one sequential and two simultaneous approaches," Transportation, Springer, vol. 44(1), pages 91-116, January.
    6. Aßmann, Christian, 2007. "Determinants and Costs of Current Account Reversals under Heterogeneity and Serial Correlation," Economics Working Papers 2007-17, Christian-Albrechts-University of Kiel, Department of Economics.
    7. Lee, Lung-Fei, 1997. "A simulated likelihood estimator for qualitative response models with sufficient statistics," Economics Letters, Elsevier, vol. 57(1), pages 23-32, November.
    8. Ziegler, Andreas & Schröder, Michael, 2010. "What determines the inclusion in a sustainability stock index?: A panel data analysis for european firms," Ecological Economics, Elsevier, vol. 69(4), pages 848-856, February.
    9. Groh, Elke D. & Möllendorff, Charlotte v., 2020. "What shapes the support of renewable energy expansion? Public attitudes between policy goals and risk, time, and social preferences," Energy Policy, Elsevier, vol. 137(C).
    10. Denzil G. Fiebig & Michael P. Keane & Jordan Louviere & Nada Wasi, 2010. "The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity," Marketing Science, INFORMS, vol. 29(3), pages 393-421, 05-06.
    11. Michael Brien & Christopher Swann, 2001. "Does Participation in Multiple Welfare Programs Improve Birth Outcomes?," JCPR Working Papers 212, Northwestern University/University of Chicago Joint Center for Poverty Research.
    12. Andreas Ziegler, 2010. "Individual Characteristics and Stated Preferences for Alternative Energy Sources and Propulsion Technologies in Vehicles: A Discrete Choice Analysis," CER-ETH Economics working paper series 10/125, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    13. Ziegler, Andreas & Schröder, Michael, 2006. "What Determines the Inclusion in a Sustainability Stock Index? A Panel Data Analysis for European Companies," ZEW Discussion Papers 06-041, ZEW - Leibniz Centre for European Economic Research.
    14. Andreas Lange & Andreas Ziegler, 2017. "Offsetting Versus Mitigation Activities to Reduce $$\hbox {CO}_{2}$$ CO 2 Emissions: A Theoretical and Empirical Analysis for the U.S. and Germany," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 66(1), pages 113-133, January.
    15. Rennings, Klaus & Ziegler, Andreas, 2004. "Determinants of Environmental Innovations in Germany: Do Organizational Measures Matter? A Discrete Choice Analysis at the Firm Level," ZEW Discussion Papers 04-30, ZEW - Leibniz Centre for European Economic Research.
    16. Victoria Prowse, 2009. "Estimating labour supply elasticities under rationing: a structural model of time allocation behaviour," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 42(1), pages 90-112, February.
    17. Andreas Ziegler, 2008. "Disentangling Specific Subsets of Innovations : A Micro-Econometric Analysis of their Determinants," CER-ETH Economics working paper series 08/100, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    18. van Dijk, Bram & Paap, Richard, 2008. "Explaining individual response using aggregated data," Journal of Econometrics, Elsevier, vol. 146(1), pages 1-9, September.
    19. Lee, Lung-Fei, 1997. "Simulated maximum likelihood estimation of dynamic discrete choice statistical models some Monte Carlo results," Journal of Econometrics, Elsevier, vol. 82(1), pages 1-35.
    20. Andreas Ziegler, 2007. "Simulated classical tests in multinomial probit models," Statistical Papers, Springer, vol. 48(4), pages 655-681, October.

    More about this item

    Keywords

    Research Methods/ Statistical Methods;

    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:jlaare:31040. 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/waeaaea.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.