IDEAS home Printed from https://ideas.repec.org/a/eee/transb/v76y2015icp1-26.html
   My bibliography  Save this article

Inference on mode preferences, vehicle purchases, and the energy paradox using a Bayesian structural choice model

Author

Listed:
  • Daziano, Ricardo A.

Abstract

Discrete choice modeling is experiencing a reemergence of research interest in the inclusion of latent variables as explanatory variables of consumer behavior. There are several reasons that motivate the integration of latent attributes, including better-informed modeling of random consumer heterogeneity and treatment of endogeneity. However, current work still is at an early stage and multiple simplifying assumptions are usually imposed. For instance, most previous applications assume all of the following: independence of taste shocks and of latent attributes, exclusion restrictions, linearity of the effect of the latent attributes on the utility function, continuous manifest variables, and an a priori bound for the number of latent constructs. We derive and apply a structural choice model with a multinomial probit kernel and discrete effect indicators to analyze continuous latent segments of travel behavior, including inference on the energy paradox. Our estimator allows for interaction and simultaneity among the latent attributes, residual correlation, nonlinear effects on the utility function, flexible substitution patterns, and temporal correlation within responses of the same individual. Statistical properties of the Bayes estimator that we propose are exact and are not affected by the number of latent attributes.

Suggested Citation

  • Daziano, Ricardo A., 2015. "Inference on mode preferences, vehicle purchases, and the energy paradox using a Bayesian structural choice model," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 1-26.
  • Handle: RePEc:eee:transb:v:76:y:2015:i:c:p:1-26
    DOI: 10.1016/j.trb.2015.02.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0191261515000405
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.trb.2015.02.012?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. Walter McManus, 2007. "The Link Between Gasoline Prices and Vehicle Sales," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 42(1), pages 53-60, January.
    2. Akshay Vij & Joan L. Walker, 2014. "Hybrid choice models: the identification problem," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 22, pages 519-564, Edward Elgar Publishing.
    3. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-1339, November.
    4. Denis Bolduc & Bernard Fortin & Stephen Gordon, 1997. "Multinomial Probit Estimation of Spatially Interdependent Choices: An Empirical Comparison of Two New Techniques," International Regional Science Review, , vol. 20(1-2), pages 77-101, April.
    5. Ricardo Daziano & Denis Bolduc, 2013. "Covariance, identification, and finite-sample performance of the MSL and Bayes estimators of a logit model with latent attributes," Transportation, Springer, vol. 40(3), pages 647-670, May.
    6. Stephane Hess & Nesha Beharry-Borg, 2012. "Accounting for Latent Attitudes in Willingness-to-Pay Studies: The Case of Coastal Water Quality Improvements in Tobago," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 52(1), pages 109-131, May.
    7. Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
    8. Walker, Joan & Ben-Akiva, Moshe, 2002. "Generalized random utility model," Mathematical Social Sciences, Elsevier, vol. 43(3), pages 303-343, July.
    9. Meghan R. Busse & Christopher R. Knittel & Florian Zettelmeyer, 2013. "Are Consumers Myopic? Evidence from New and Used Car Purchases," American Economic Review, American Economic Association, vol. 103(1), pages 220-256, February.
    10. Dansie, B. R., 1985. "Parameter estimability in the multinomial probit model," Transportation Research Part B: Methodological, Elsevier, vol. 19(6), pages 526-528, December.
    11. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    12. Hunt Allcott, 2011. "Consumers' Perceptions and Misperceptions of Energy Costs," American Economic Review, American Economic Association, vol. 101(3), pages 98-104, May.
    13. 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.
    14. Daniel McFadden, 1986. "The Choice Theory Approach to Market Research," Marketing Science, INFORMS, vol. 5(4), pages 275-297.
    15. Hausman, Jerry A & Joskow, Paul L, 1982. "Evaluating the Costs and Benefits of Appliance Efficiency Standards," American Economic Review, American Economic Association, vol. 72(2), pages 220-225, May.
    16. McCulloch, Robert E. & Polson, Nicholas G. & Rossi, Peter E., 2000. "A Bayesian analysis of the multinomial probit model with fully identified parameters," Journal of Econometrics, Elsevier, vol. 99(1), pages 173-193, November.
    17. Dreyfus, Mark K & Viscusi, W Kip, 1995. "Rates of Time Preference and Consumer Valuations of Automobile Safety and Fuel Efficiency," Journal of Law and Economics, University of Chicago Press, vol. 38(1), pages 79-105, April.
    18. Jerry A. Hausman, 1979. "Individual Discount Rates and the Purchase and Utilization of Energy-Using Durables," Bell Journal of Economics, The RAND Corporation, vol. 10(1), pages 33-54, Spring.
    19. Parry, Ian W.H. & Evans, David & Oates, Wallace E., 2014. "Are energy efficiency standards justified?," Journal of Environmental Economics and Management, Elsevier, vol. 67(2), pages 104-125.
    20. 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.
    21. Jaffe, Adam B. & Stavins, Robert N., 1994. "The energy paradox and the diffusion of conservation technology," Resource and Energy Economics, Elsevier, vol. 16(2), pages 91-122, May.
    22. Jean-Marie Dufour, 1997. "Some Impossibility Theorems in Econometrics with Applications to Structural and Dynamic Models," Econometrica, Econometric Society, vol. 65(6), pages 1365-1388, November.
    23. Ben-Akiva, Moshe & McFadden, Daniel & Train, Kenneth & Börsch-Supan, Axel, 2002. "Hybrid Choice Models: Progress and Challenges," Sonderforschungsbereich 504 Publications 02-29, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    24. Nobile, Agostino, 2000. "Comment: Bayesian multinomial probit models with a normalization constraint," Journal of Econometrics, Elsevier, vol. 99(2), pages 335-345, December.
    25. Martin Burda & Matthew Harding, 2013. "Panel Probit With Flexible Correlated Effects: Quantifying Technology Spillovers In The Presence Of Latent Heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(6), pages 956-981, September.
    26. Vassilis A. Hajivassiliou & Daniel L. McFadden, 1998. "The Method of Simulated Scores for the Estimation of LDV Models," Econometrica, Econometric Society, vol. 66(4), pages 863-896, July.
    27. Horne, Matt & Jaccard, Mark & Tiedemann, Ken, 2005. "Improving behavioral realism in hybrid energy-economy models using discrete choice studies of personal transportation decisions," Energy Economics, Elsevier, vol. 27(1), pages 59-77, January.
    28. Palma, David & Dios Ortuzar, Juan de & Casaubon, Gerard & Rizzi, Luis I. & Agosin, Eduardo, 2013. "Measuring consumer preferences using hybrid discrete choice models," Working Papers 164855, American Association of Wine Economists.
    29. Daziano, Ricardo A. & Achtnicht, Martin, 2014. "Accounting for uncertainty in willingness to pay for environmental benefits," Energy Economics, Elsevier, vol. 44(C), pages 166-177.
    30. Lutz Hildebrandt & Dirk Temme & Marcel Paulssen, 2012. "Choice Modeling and SEM," Springer Books, in: Adamantios Diamantopoulos & Wolfgang Fritz & Lutz Hildebrandt (ed.), Quantitative Marketing and Marketing Management, edition 127, chapter 3, pages 63-80, Springer.
    31. Hunt Allcott & Nathan Wozny, 2014. "Gasoline Prices, Fuel Economy, and the Energy Paradox," The Review of Economics and Statistics, MIT Press, vol. 96(5), pages 779-795, December.
    32. Train, Kenneth, 1985. "Discount rates in consumers' energy-related decisions: A review of the literature," Energy, Elsevier, vol. 10(12), pages 1243-1253.
    33. Bento, Antonio M. & Li, Shanjun & Roth, Kevin, 2012. "Is there an energy paradox in fuel economy? A note on the role of consumer heterogeneity and sorting bias," Economics Letters, Elsevier, vol. 115(1), pages 44-48.
    34. Elrod, Terry & Keane, Michael, 1995. "A Factor-Analytic Probit Model for Representing the Market Structure in Panel Data," MPRA Paper 52434, University Library of Munich, Germany.
    35. Imai, Kosuke & van Dyk, David A., 2005. "A Bayesian analysis of the multinomial probit model using marginal data augmentation," Journal of Econometrics, Elsevier, vol. 124(2), pages 311-334, February.
    36. Richard G. Newell & Juha Siikamäki, 2014. "Nudging Energy Efficiency Behavior: The Role of Information Labels," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 1(4), pages 555-598.
    37. Maribeth Coller & Melonie Williams, 1999. "Eliciting Individual Discount Rates," Experimental Economics, Springer;Economic Science Association, vol. 2(2), pages 107-127, December.
    38. Bolduc, Denis, 1992. "Generalized autoregressive errors in the multinomial probit model," Transportation Research Part B: Methodological, Elsevier, vol. 26(2), pages 155-170, April.
    39. Bhat, Chandra R. & Dubey, Subodh K., 2014. "A new estimation approach to integrate latent psychological constructs in choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 68-85.
    40. Rungie, Cam M. & Coote, Leonard V. & Louviere, Jordan J., 2012. "Latent variables in discrete choice experiments," Journal of choice modelling, Elsevier, vol. 5(3), pages 145-156.
    41. Rosenberger, Randall S. & Needham, Mark D. & Morzillo, Anita T. & Moehrke, Caitlin, 2012. "Attitudes, willingness to pay, and stated values for recreation use fees at an urban proximate forest," Journal of Forest Economics, Elsevier, vol. 18(4), pages 271-281.
    42. Helfand, Gloria & Wolverton, Ann, 2011. "Evaluating the Consumer Response to Fuel Economy: A Review of the Literature," International Review of Environmental and Resource Economics, now publishers, vol. 5(2), pages 103-146, May.
    43. Bunch, David S., 1991. "Estimability in the Multinomial Probit Model," University of California Transportation Center, Working Papers qt1gf1t128, University of California Transportation Center.
    44. Bunch, David S., 1991. "Estimability in the multinomial probit model," Transportation Research Part B: Methodological, Elsevier, vol. 25(1), pages 1-12, February.
    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. Vij, Akshay & Walker, Joan L., 2016. "How, when and why integrated choice and latent variable models are latently useful," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 192-217.
    2. Kamargianni, Maria & Dubey, Subodh & Polydoropoulou, Amalia & Bhat, Chandra, 2015. "Investigating the subjective and objective factors influencing teenagers’ school travel mode choice – An integrated choice and latent variable model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 473-488.
    3. Tsionas, Efthymios & Assaf, A. George & Gillen, David & Mattila, Anna S., 2017. "Modeling technical and service efficiency," Transportation Research Part B: Methodological, Elsevier, vol. 96(C), pages 113-125.
    4. Blake, Miranda R. & Dubey, Subodh & Swait, Joffre & Lancsar, Emily & Ghijben, Peter, 2020. "An integrated modelling approach examining the influence of goals, habit and learning on choice using visual attention data," Journal of Business Research, Elsevier, vol. 117(C), pages 44-57.
    5. Enam, Annesha & Konduri, Karthik C. & Pinjari, Abdul R. & Eluru, Naveen, 2018. "An integrated choice and latent variable model for multiple discrete continuous choice kernels: Application exploring the association between day level moods and discretionary activity engagement choi," Journal of choice modelling, Elsevier, vol. 26(C), pages 80-100.
    6. Bouscasse, H., 2018. "Integrated choice and latent variable models: A literature review on mode choice," Working Papers 2018-07, Grenoble Applied Economics Laboratory (GAEL).
    7. Dubey, Subodh & Sharma, Ishant & Mishra, Sabyasachee & Cats, Oded & Bansal, Prateek, 2022. "A General Framework to Forecast the Adoption of Novel Products: A Case of Autonomous Vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 165(C), pages 63-95.
    8. Gyeongjae Lee & Sujae Kim & Jahun Koo & Sangho Choo, 2024. "Exploring Psychological Factors Influencing the Adoption of Sustainable Public Transit Considering Preference Heterogeneity," Sustainability, MDPI, vol. 16(18), pages 1-23, September.
    9. Hélène Bouscasse, 2018. "Integrated choice and latent variable models: A literature review on mode choice," Working Papers hal-01795630, HAL.
    10. Hess, Stephane & Spitz, Greg & Bradley, Mark & Coogan, Matt, 2018. "Analysis of mode choice for intercity travel: Application of a hybrid choice model to two distinct US corridors," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 547-567.

    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. Ricardo A. Daziano & Martin Achtnicht, 2014. "Forecasting Adoption of Ultra-Low-Emission Vehicles Using Bayes Estimates of a Multinomial Probit Model and the GHK Simulator," Transportation Science, INFORMS, vol. 48(4), pages 671-683, November.
    2. 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.
    3. Daziano, Ricardo A. & Achtnicht, Martin, 2012. "Forecasting adoption of ultra-low-emission vehicles using the GHK simulator and Bayes estimates of a multinomial probit model," ZEW Discussion Papers 12-017, ZEW - Leibniz Centre for European Economic Research.
    4. Todd D. Gerarden & Richard G. Newell & Robert N. Stavins, 2017. "Assessing the Energy-Efficiency Gap," Journal of Economic Literature, American Economic Association, vol. 55(4), pages 1486-1525, December.
    5. Ziegler, Andreas, 2001. "Simulated z-tests in multinomial probit models," ZEW Discussion Papers 01-53, ZEW - Leibniz Centre for European Economic Research.
    6. Kenneth Gillingham & Karen Palmer, 2014. "Bridging the Energy Efficiency Gap: Policy Insights from Economic Theory and Empirical Evidence," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 8(1), pages 18-38, January.
    7. Leard, Benjamin, 2018. "Consumer inattention and the demand for vehicle fuel cost savings," Journal of choice modelling, Elsevier, vol. 29(C), pages 1-16.
    8. Shigeru Matsumoto, 2018. "Consumer valuation of energy-saving features of residential air conditioners with hedonic and choice models," Empirical Economics, Springer, vol. 55(4), pages 1779-1806, December.
    9. Chen Wang & Ricardo Daziano, 2015. "On the problem of measuring discount rates in intertemporal transportation choices," Transportation, Springer, vol. 42(6), pages 1019-1038, November.
    10. Sallee, James M. & West, Sarah E. & Fan, Wei, 2016. "Do consumers recognize the value of fuel economy? Evidence from used car prices and gasoline price fluctuations," Journal of Public Economics, Elsevier, vol. 135(C), pages 61-73.
    11. Allcott, Hunt & Mullainathan, Sendhil & Taubinsky, Dmitry, 2014. "Energy policy with externalities and internalities," Journal of Public Economics, Elsevier, vol. 112(C), pages 72-88.
    12. David Roodman, 2009. "Estimating Fully Observed Recursive Mixed-Process Models with cmp," Working Papers 168, Center for Global Development.
    13. Gerarden, Todd D. & Newell, Richard G. & Stavins, Robert N. & Stowe, Robert C., 2015. "An Assessment of the Energy-Efficiency Gap and Its Implications for Climate Change Policy," Climate Change and Sustainable Development 202116, Fondazione Eni Enrico Mattei (FEEM).
    14. Tobias Müller & Stefan Boes, 2020. "Disability insurance benefits and labor supply decisions: evidence from a discontinuity in benefit awards," Empirical Economics, Springer, vol. 58(5), pages 2513-2544, May.
    15. 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.
    16. Ziegler, Andreas, 2012. "Individual characteristics and stated preferences for alternative energy sources and propulsion technologies in vehicles: A discrete choice analysis for Germany," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(8), pages 1372-1385.
    17. Piatek, Rémi & Gensowski, Miriam, 2017. "A Multinomial Probit Model with Latent Factors: Identification and Interpretation without a Measurement System," IZA Discussion Papers 11042, Institute of Labor Economics (IZA).
    18. Richard G. Newell & Juha Siikamäki, 2014. "Nudging Energy Efficiency Behavior: The Role of Information Labels," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 1(4), pages 555-598.
    19. Schmid, Basil & Axhausen, Kay W., 2019. "In-store or online shopping of search and experience goods: A hybrid choice approach," Journal of choice modelling, Elsevier, vol. 31(C), pages 156-180.
    20. Andor, Mark & Gerster, Andreas & Sommer, Stephan, 2016. "Consumer Inattention and Decision Heuristics: The Causal Effects of Energy Label Elements," VfS Annual Conference 2016 (Augsburg): Demographic Change 145778, Verein für Socialpolitik / German Economic Association.

    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:eee:transb:v:76:y:2015:i:c:p:1-26. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/548/description#description .

    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.