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Stochastic frontier estimation of budgets for Kuhn–Tucker demand systems: Application to activity time-use analysis

Listed author(s):
  • Pinjari, Abdul Rawoof
  • Augustin, Bertho
  • Sivaraman, Vijayaraghavan
  • Faghih Imani, Ahmadreza
  • Eluru, Naveen
  • Pendyala, Ram M.

We propose a stochastic frontier approach to estimate budgets for the multiple discrete–continuous extreme value (MDCEV) model. The approach is useful when the underlying time and/or money budgets driving a choice situation are unobserved, but the expenditures on the choice alternatives of interest are observed. Several MDCEV applications hitherto used the observed total expenditure on the choice alternatives as the budget to model expenditure allocation among choice alternatives. This does not allow for increases or decreases in the total expenditure due to changes in choice alternative-specific attributes, but only allows a reallocation of the observed total expenditure among different alternatives. The stochastic frontier approach helps address this issue by invoking the notion that consumers operate under latent budgets that can be conceived (and modeled) as the maximum possible expenditure they are willing to incur. The proposed method is applied to analyze the daily out-of-home activity participation and time-use patterns in a survey sample of non-working adults in Florida. First, a stochastic frontier regression is performed on the observed out-of-home activity time expenditure (OH-ATE) to estimate the unobserved out-of-home activity time frontier (OH-ATF). The estimated frontier is interpreted as a subjective limit or maximum possible time individuals can allocate to out-of-home activities and used as the time budget governing out-of-home time-use choices in an MDCEV model. The efficacy of this approach is compared with other approaches for estimating time budgets for the MDCEV model, including: (a) a log-linear regression on the total observed expenditure for out-of-home activities and (b) arbitrarily assumed, constant time budgets for all individuals in the sample. A comparison of predictive accuracy in time-use patterns suggests that the stochastic frontier and log-linear regression approaches perform better than arbitrary assumptions on time budgets. Between the stochastic frontier and log-linear regression approaches, the former results in slightly better predictions of activity participation rates while the latter results in slightly better predictions of activity durations. A comparison of policy simulations demonstrates that the stochastic frontier approach allows for the total out-of-home activity time expenditure to either expand or shrink due to changes in alternative-specific attributes. The log-linear regression approach allows for changes in total time expenditure due to changes in decision-maker attributes, but not due to changes in alternative-specific attributes.

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Article provided by Elsevier in its journal Transportation Research Part A: Policy and Practice.

Volume (Year): 88 (2016)
Issue (Month): C ()
Pages: 117-133

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Handle: RePEc:eee:transa:v:88:y:2016:i:c:p:117-133
DOI: 10.1016/j.tra.2016.03.013
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  1. Farooq, Bilal & Miller, Eric J. & Haider, Murtaza, 2013. "A multidimensional decisions modeling framework for built space supply," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 6(3), pages 61-74.
  2. Wales, T. J. & Woodland, A. D., 1983. "Estimation of consumer demand systems with binding non-negativity constraints," Journal of Econometrics, Elsevier, vol. 21(3), pages 263-285, April.
  3. Pinjari, Abdul Rawoof & Bhat, Chandra R. & Hensher, David A., 2009. "Residential self-selection effects in an activity time-use behavior model," Transportation Research Part B: Methodological, Elsevier, vol. 43(7), pages 729-748, August.
  4. Caleb Van Nostrand & Vijayaraghavan Sivaraman & Abdul Pinjari, 2013. "Analysis of long-distance vacation travel demand in the United States: a multiple discrete–continuous choice framework," Transportation, Springer, vol. 40(1), pages 151-171, January.
  5. Fang, Hao Audrey, 2008. "A discrete-continuous model of households' vehicle choice and usage, with an application to the effects of residential density," Transportation Research Part B: Methodological, Elsevier, vol. 42(9), pages 736-758, November.
  6. Khandker Habib & Eric Miller, 2008. "Modelling daily activity program generation considering within-day and day-to-day dynamics in activity-travel behaviour," Transportation, Springer, vol. 35(4), pages 467-484, July.
  7. Sobhani, Anae & Eluru, Naveen & Faghih-Imani, Ahmadreza, 2013. "A latent segmentation based multiple discrete continuous extreme value model," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 154-169.
  8. von Haefen, Roger H., 2010. "Incomplete Demand Systems, Corner Solutions, and Welfare Measurement," Agricultural and Resource Economics Review, Cambridge University Press, vol. 39(01), pages 22-36, February.
  9. Bhat, Chandra R. & Sen, Sudeshna & Eluru, Naveen, 2009. "The impact of demographics, built environment attributes, vehicle characteristics, and gasoline prices on household vehicle holdings and use," Transportation Research Part B: Methodological, Elsevier, vol. 43(1), pages 1-18, January.
  10. Pinjari, Abdul Rawoof & Bhat, Chandra, 2010. "A multiple discrete-continuous nested extreme value (MDCNEV) model: Formulation and application to non-worker activity time-use and timing behavior on weekdays," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 562-583, May.
  11. Erika Spissu & Abdul Pinjari & Chandra Bhat & Ram Pendyala & Kay Axhausen, 2009. "An analysis of weekly out-of-home discretionary activity participation and time-use behavior," Transportation, Springer, vol. 36(5), pages 483-510, September.
  12. Deaton,Angus & Muellbauer,John, 1980. "Economics and Consumer Behavior," Cambridge Books, Cambridge University Press, number 9780521296762, August.
  13. Chandra Bhat & Konstadinos Goulias & Ram Pendyala & Rajesh Paleti & Raghuprasad Sidharthan & Laura Schmitt & Hsi-Hwa Hu, 2013. "A household-level activity pattern generation model with an application for Southern California," Transportation, Springer, vol. 40(5), pages 1063-1086, September.
  14. Bhat, Chandra R., 2008. "The multiple discrete-continuous extreme value (MDCEV) model: Role of utility function parameters, identification considerations, and model extensions," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 274-303, March.
  15. Bhat, Chandra R., 2005. "A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions," Transportation Research Part B: Methodological, Elsevier, vol. 39(8), pages 679-707, September.
  16. Amlan Banerjee & Xin Ye & Ram Pendyala, 2007. "Understanding Travel Time Expenditures Around the World: Exploring the Notion of a Travel Time Frontier," Transportation, Springer, vol. 34(1), pages 51-65, January.
  17. Pinjari, Abdul Rawoof, 2011. "Generalized extreme value (GEV)-based error structures for multiple discrete-continuous choice models," Transportation Research Part B: Methodological, Elsevier, vol. 45(3), pages 474-489, March.
  18. Ahn, Jiwoon & Jeong, Gicheol & Kim, Yeonbae, 2008. "A forecast of household ownership and use of alternative fuel vehicles: A multiple discrete-continuous choice approach," Energy Economics, Elsevier, vol. 30(5), pages 2091-2104, September.
  19. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
  20. Castro, Marisol & Bhat, Chandra R. & Pendyala, Ram M. & Jara-Díaz, Sergio R., 2012. "Accommodating multiple constraints in the multiple discrete–continuous extreme value (MDCEV) choice model," Transportation Research Part B: Methodological, Elsevier, vol. 46(6), pages 729-743.
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