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Quasi-random simulation of discrete choice models

Citations

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Cited by:

  1. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
  2. Sarrias, Mauricio, 2016. "Discrete Choice Models with Random Parameters in R: The Rchoice Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i10).
  3. Bliemer, Michiel C.J. & Rose, John M., 2010. "Construction of experimental designs for mixed logit models allowing for correlation across choice observations," Transportation Research Part B: Methodological, Elsevier, vol. 44(6), pages 720-734, July.
  4. Landry, Craig & Remar, Daniel & Twinkle, Roy, 2022. "Economic Value of Restaurant Safety Measures and Propensity to Dine during the COVID-19 Pandemic," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322234, Agricultural and Applied Economics Association.
  5. Mikołaj Czajkowski & Nick Hanley & Jacob LaRiviere, 2016. "Controlling for the Effects of Information in a Public Goods Discrete Choice Model," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 63(3), pages 523-544, March.
  6. Akinc, Deniz & Vandebroek, Martina, 2018. "Bayesian estimation of mixed logit models: Selecting an appropriate prior for the covariance matrix," Journal of choice modelling, Elsevier, vol. 29(C), pages 133-151.
  7. Yannis, George & Antoniou, C., 2007. "A mixed logit model for the sensitivity analysis of Greek drivers' behaviour towards enforcement for road safety," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 37, pages 62-77.
  8. Xiaodong Gong & Robert Breuing, 2011. "Estimating Net Child Care Price Elasticities of Partnered Women With Pre-School Children Using a Discrete Structural Labour Supply-Child Care Model," CEPR Discussion Papers 653, Centre for Economic Policy Research, Research School of Economics, Australian National University.
  9. Junyi Shen, 2009. "Latent class model or mixed logit model? A comparison by transport mode choice data," Applied Economics, Taylor & Francis Journals, vol. 41(22), pages 2915-2924.
  10. Junyi Shen & Yusuke Sakata & Yoshizo Hashimoto, 2006. "A Comparison between Latent Class Model and Mixed Logit Model for Transport Mode Choice: Evidences from Two Datasets of Japan," Discussion Papers in Economics and Business 06-05, Osaka University, Graduate School of Economics.
  11. Munger, D. & L’Ecuyer, P. & Bastin, F. & Cirillo, C. & Tuffin, B., 2012. "Estimation of the mixed logit likelihood function by randomized quasi-Monte Carlo," Transportation Research Part B: Methodological, Elsevier, vol. 46(2), pages 305-320.
  12. Bastin, Fabian & Cirillo, Cinzia & Toint, Philippe L., 2006. "Application of an adaptive Monte Carlo algorithm to mixed logit estimation," Transportation Research Part B: Methodological, Elsevier, vol. 40(7), pages 577-593, August.
  13. Zsolt Sándor, 2019. "Further evidence on sparse grids-based numerical integration in the mixed logit model," Economics Bulletin, AccessEcon, vol. 39(4), pages 2726-2731.
  14. Bhat, Chandra R. & Sidharthan, Raghuprasad, 2011. "A simulation evaluation of the maximum approximate composite marginal likelihood (MACML) estimator for mixed multinomial probit models," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 940-953, August.
  15. Staus, Alexander, 2008. "Standard and Shuffled Halton Sequences in a Mixed Logit Model," Working Papers 93856, Universitaet Hohenheim, Institute of Agricultural Policy and Agricultural Markets.
  16. Wüstenhagen, Rolf & Schleich, Joachim & Rennings, Klaus & Heinzle, Stefanie & Brohmann, Bettina, 2009. "What's Driving Sustainable Energy Consumption? A Survey of the Empirical Literature," ZEW Discussion Papers 09-013, ZEW - Leibniz Centre for European Economic Research.
  17. Yu, Jie & Goos, Peter & Vandebroek, Martina, 2010. "Comparing different sampling schemes for approximating the integrals involved in the efficient design of stated choice experiments," Transportation Research Part B: Methodological, Elsevier, vol. 44(10), pages 1268-1289, December.
  18. Andreas Ziegler, 2007. "Simulated classical tests in multinomial probit models," Statistical Papers, Springer, vol. 48(4), pages 655-681, October.
  19. Dapeng Cui & David Curry, 2005. "Prediction in Marketing Using the Support Vector Machine," Marketing Science, INFORMS, vol. 24(4), pages 595-615, January.
  20. Prateek Bansal & Vahid Keshavarzzadeh & Angelo Guevara & Shanjun Li & Ricardo A Daziano, 2022. "Designed quadrature to approximate integrals in maximum simulated likelihood estimation [Evaluating simulation-based approaches and multivariate quadrature on sparse grids in estimating multivariat," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 301-321.
  21. Liu, Henry X. & He, Xiaozheng & Recker, Will, 2007. "Estimation of the time-dependency of values of travel time and its reliability from loop detector data," Transportation Research Part B: Methodological, Elsevier, vol. 41(4), pages 448-461, May.
  22. Czajkowski, Mikołaj & Budziński, Wiktor, 2019. "Simulation error in maximum likelihood estimation of discrete choice models," Journal of choice modelling, Elsevier, vol. 31(C), pages 73-85.
  23. Ernan Haruvy & Peter T. L. Popkowski Leszczyc, 2010. "Search and Choice in Online Consumer Auctions," Marketing Science, INFORMS, vol. 29(6), pages 1152-1164, 11-12.
  24. Tong Zeng, 2024. "Frequentist model averaging in the generalized multinomial logit model," Computational Statistics, Springer, vol. 39(2), pages 605-627, April.
  25. Kunwar, Samrat B. & Bohara, Alok K. & Thacher, Jennifer, 2020. "Public preference for river restoration in the Danda Basin, Nepal: A choice experiment study," Ecological Economics, Elsevier, vol. 175(C).
  26. Xiaodong Gong & Robert Breunig, 2017. "Childcare Assistance: Are Subsidies or Tax Credits Better?," Fiscal Studies, Institute for Fiscal Studies, vol. 38, pages 7-48, March.
  27. repec:sss:wpaper:201404 is not listed on IDEAS
  28. Bliemer, Michiel C.J. & Rose, John M., 2013. "Confidence intervals of willingness-to-pay for random coefficient logit models," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 199-214.
  29. Kawasaki, Tomoya & Hanaoka, Shinya & Nguyen, Long Xuan, 2014. "The valuation of shipment time variability in Greater Mekong Subregion," Transport Policy, Elsevier, vol. 32(C), pages 25-33.
  30. Xiaodong Gong, 2017. "The dynamics of study-work choice and its effect on intended and actual university attainment," Education Economics, Taylor & Francis Journals, vol. 25(6), pages 619-639, November.
  31. Daziano, Ricardo A., 2013. "Conditional-logit Bayes estimators for consumer valuation of electric vehicle driving range," Resource and Energy Economics, Elsevier, vol. 35(3), pages 429-450.
  32. Train, Kenneth & Wilson, Wesley W., 2008. "Estimation on stated-preference experiments constructed from revealed-preference choices," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 191-203, March.
  33. Pál, László & Sándor, Zsolt, 2023. "Comparing procedures for estimating random coefficient logit demand models with a special focus on obtaining global optima," International Journal of Industrial Organization, Elsevier, vol. 88(C).
  34. Cherchi, Elisabetta & Guevara, Cristian Angelo, 2012. "A Monte Carlo experiment to analyze the curse of dimensionality in estimating random coefficients models with a full variance–covariance matrix," Transportation Research Part B: Methodological, Elsevier, vol. 46(2), pages 321-332.
  35. Ziegler Andreas, 2010. "Z-Tests in Multinomial Probit Models under Simulated Maximum Likelihood Estimation: Some Small Sample Properties," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 230(5), pages 630-652, October.
  36. Chandra Bhat, 2015. "A new spatial (social) interaction discrete choice model accommodating for unobserved effects due to endogenous network formation," Transportation, Springer, vol. 42(5), pages 879-914, September.
  37. Hess, Stephane & Train, Kenneth E. & Polak, John W., 2006. "On the use of a Modified Latin Hypercube Sampling (MLHS) method in the estimation of a Mixed Logit Model for vehicle choice," Transportation Research Part B: Methodological, Elsevier, vol. 40(2), pages 147-163, February.
  38. Sándor, Z. & Franses, Ph.H.B.F., 2004. "Experimental investigation of consumer price evaluations," Econometric Institute Research Papers EI 2004-12, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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