The heterogeneous effects of guardian supervision on adolescent driver-injury severities: A finite-mixture random-parameters approach
One of the key aspects of graduated driver licensing programs is the new-driver experience gained in the presence of a guardian (a person providing mandatory supervision from the passenger seat). However, the effect that this guardian-supervising practice has on adolescent drivers’ crash-injury severity (should a crash occur) is not well understood. This paper seeks to provide insights into the injury-prevention effectiveness of guardian supervision by developing an appropriate econometric structure to account for the complex interactions that are likely to occur in the study of the heterogeneous effects of guardian supervision on crash-injury severities. As opposed to conventional heterogeneity models with standard distributional assumptions, this paper deals with the heterogeneous effects by accounting for the possible multivariate characteristics of parameter distributions in addition to allowing for multimodality, skewness and kurtosis. A Markov Chain Monte Carlo (MCMC) algorithm is developed for estimation and the permutation sampler proposed by Frühwirth-Schnatter (2001) is extended for model identification. The econometric analysis shows the presence of two distinct driving environments (defined by roadway geometric and traffic conditions). Model estimation results show that, in both of these driving environments, the presence of guardian supervision reduces the crash-injury severity, but in interestingly different ways. Based on the findings of this research, a case could easily be made for extending the time-requirement for guardian supervision in current graduated driver license programs.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 49 (2013)
Issue (Month): C ()
|Contact details of provider:|| Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/548/description#description|
|Order Information:|| Postal: http://www.elsevier.com/wps/find/supportfaq.cws_home/regional|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Joel Huber and Kenneth Train., 2000.
"On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths,"
Economics Working Papers
E00-289, University of California at Berkeley.
- Huber, Joel & Train, Kenneth, 2000. "On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths," Department of Economics, Working Paper Series qt7zm4f51b, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
- Joel Huber & Kenneth Train, 2001. "On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths," Econometrics 0012003, EconWPA.
- Park, Byung-Jung & Zhang, Yunlong & Lord, Dominique, 2010. "Bayesian mixture modeling approach to account for heterogeneity in speed data," Transportation Research Part B: Methodological, Elsevier, vol. 44(5), pages 662-673, June.
- McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
- Daniel McFadden, 1987. "A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration," Working papers 464, Massachusetts Institute of Technology (MIT), Department of Economics.
- 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.
- Clifford Winston & Vikram Maheshri & Fred Mannering, 2006. "An exploration of the offset hypothesis using disaggregate data: The case of airbags and antilock brakes," Journal of Risk and Uncertainty, Springer, vol. 32(2), pages 83-99, March.
- Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-1057, September.
- 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.
- Vassilis A. Hajivassiliou & Axel Borsch-Supan, 1990. "Smooth Unbiased Multivariate Probability Simulators for Maximum Likelihood Estimation of Limited Dependent Variable Models," Cowles Foundation Discussion Papers 960, Cowles Foundation for Research in Economics, Yale University.
- Fruhwirth-Schnatter S., 2001. "Markov Chain Monte Carlo Estimation of Classical and Dynamic Switching and Mixture Models," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 194-209, March.
- Geweke, John & Keane, Michael, 2007. "Smoothly mixing regressions," Journal of Econometrics, Elsevier, vol. 138(1), pages 252-290, May.
- William H. Greene & David A. Hensher, 2013. "Revealing additional dimensions of preference heterogeneity in a latent class mixed multinomial logit model," Applied Economics, Taylor & Francis Journals, vol. 45(14), pages 1897-1902, May.
- William Greene & David Hensher, 2010. "Revealing Additional Dimensions of Preference Heterogeneity in a Latent Class Mixed Multinomial Logit Model," Working Papers 10-17, New York University, Leonard N. Stern School of Business, Department of Economics.
- Yau, Kelvin K. W. & Lee, Andy H. & Ng, Angus S. K., 2003. "Finite mixture regression model with random effects: application to neonatal hospital length of stay," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 359-366, January.
- 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.
- Wong, K.I. & Wong, S.C. & Yang, Hai & Wu, J.H., 2008. "Modeling urban taxi services with multiple user classes and vehicle modes," Transportation Research Part B: Methodological, Elsevier, vol. 42(10), pages 985-1007, December.
- Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2007. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9780521671736, January.
- Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2007. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9780521855716, January.
- Koop, Gary M & Poirier, Dale J & Tobias, Justin, 2007. "Bayesian Econometric Methods," Staff General Research Papers Archive 12722, Iowa State University, Department of Economics.
- Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August.
- Greene, William H. & Hensher, David A., 2003. "A latent class model for discrete choice analysis: contrasts with mixed logit," Transportation Research Part B: Methodological, Elsevier, vol. 37(8), pages 681-698, September.
- Bhat, Chandra R., 2011. "The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 923-939, August.
- Olaru, Doina & Smith, Brett & Taplin, John H.E., 2011. "Residential location and transit-oriented development in a new rail corridor," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(3), pages 219-237, March.
- McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
- Bhat, Chandra R., 2003. "Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences," Transportation Research Part B: Methodological, Elsevier, vol. 37(9), pages 837-855, November.
- Angel Bujosa & Antoni Riera & Robert Hicks, 2010. "Combining Discrete and Continuous Representations of Preference Heterogeneity: A Latent Class Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 47(4), pages 477-493, December.
- Angel Bujosa Bestard & Antoni Riera Font & Robert L. Hicks, 2009. "Combining discrete and continuous representations of preference heterogeneity: a latent class approach," CRE Working Papers (Documents de treball del CRE) 2009/2, Centre de Recerca Econòmica (UIB ·"Sa Nostra").
- Chib, Siddhartha, 1992. "Bayes inference in the Tobit censored regression model," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 79-99.
- Fruhwirth-Schnatter, Sylvia & Tuchler, Regina & Otter, Thomas, 2004. "Bayesian Analysis of the Heterogeneity Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 2-15, January. Full references (including those not matched with items on IDEAS)