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A flexible spatially dependent discrete choice model: Formulation and application to teenagers' weekday recreational activity participation

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  • Bhat, Chandra R.
  • Sener, Ipek N.
  • Eluru, Naveen

Abstract

This study proposes a simple and practical Composite Marginal Likelihood (CML) inference approach to estimate ordered-response discrete choice models with flexible copula-based spatial dependence structures across observational units. The approach is applicable to data sets of any size, provides standard error estimates for all parameters, and does not require any simulation machinery. The combined copula-CML approach proposed here should be appealing for general multivariate modeling contexts because it is simple and flexible, and is easy to implement The ability of the CML approach to recover the parameters of a spatially ordered process is evaluated using a simulation study, which clearly points to the effectiveness of the approach. In addition, the combined copula-CML approach is applied to study the daily episode frequency of teenagers' physically active and physically inactive recreational activity participation, a subject of considerable interest in the transportation, sociology, and adolescence development fields. The data for the analysis are drawn from the 2000 San Francisco Bay Area Survey. The results highlight the value of the copula approach that separates the univariate marginal distribution form from the multivariate dependence structure, as well as underscore the need to consider spatial effects in recreational activity participation. The variable effects indicate that parents' physical activity participation constitutes the most important factor influencing teenagers' physical activity participation levels. Thus, an effective way to increase active recreation among teenagers may be to direct physical activity benefit-related information and education campaigns toward parents, perhaps at special physical education sessions at the schools of teenagers.

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Bibliographic Info

Article provided by Elsevier in its journal Transportation Research Part B: Methodological.

Volume (Year): 44 (2010)
Issue (Month): 8-9 (September)
Pages: 903-921

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Handle: RePEc:eee:transb:v:44:y::i:8-9:p:903-921

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Related research

Keywords: Spatial econometrics Copula Composite marginal likelihood (CML) inference approach Children's activity Public health Physical activity;

References

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  1. Bhat, Chandra R. & Eluru, Naveen, 2009. "A copula-based approach to accommodate residential self-selection effects in travel behavior modeling," Transportation Research Part B: Methodological, Elsevier, vol. 43(7), pages 749-765, August.
  2. John J. Hanfelt, 2004. "Composite conditional likelihood for sparse clustered data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 259-273.
  3. 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.
  4. Antonio Páez, 2007. "Spatial perspectives on urban systems: developments and directions," Journal of Geographical Systems, Springer, vol. 9(1), pages 1-6, April.
  5. Rachel Copperman & Chandra Bhat, 2007. "An analysis of the determinants of children’s weekend physical activity participation," Transportation, Springer, vol. 34(1), pages 67-87, January.
  6. Ipek Sener & Rachel Copperman & Ram Pendyala & Chandra Bhat, 2008. "An analysis of children’s leisure activity engagement: examining the day of week, location, physical activity level, and fixity dimensions," Transportation, Springer, vol. 35(5), pages 673-696, August.
  7. Bhat, Chandra R. & Guo, Jessica Y., 2007. "A comprehensive analysis of built environment characteristics on household residential choice and auto ownership levels," Transportation Research Part B: Methodological, Elsevier, vol. 41(5), pages 506-526, June.
  8. Guan, Yongtao, 2006. "A Composite Likelihood Approach in Fitting Spatial Point Process Models," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1502-1512, December.
  9. Nils Lid Hjort & Cristiano Varin, 2008. "ML, PL, QL in Markov Chain Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics & Finnish Statistical Society & Norwegian Statistical Association & Swedish Statistical Association, vol. 35(1), pages 64-82.
  10. de Leon, A.R., 2005. "Pairwise likelihood approach to grouped continuous model and its extension," Statistics & Probability Letters, Elsevier, vol. 75(1), pages 49-57, November.
  11. 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.
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  18. Caragea, Petruta C. & Smith, Richard L., 2007. "Asymptotic properties of computationally efficient alternative estimators for a class of multivariate normal models," Journal of Multivariate Analysis, Elsevier, vol. 98(7), pages 1417-1440, August.
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  20. Bhat, Chandra R. & Srinivasan, Sivaramakrishnan & Sen, Sudeshna, 2006. "A joint model for the perfect and imperfect substitute goods case: Application to activity time-use decisions," Transportation Research Part B: Methodological, Elsevier, vol. 40(10), pages 827-850, December.
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Full references (including those not matched with items on IDEAS)

Citations

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Cited by:
  1. Ipek Sener & Chandra Bhat, 2012. "Flexible spatial dependence structures for unordered multinomial choice models: formulation and application to teenagers’ activity participation," Transportation, Springer, vol. 39(3), pages 657-683, May.
  2. Paleti, Rajesh & Bhat, Chandra R., 2013. "The composite marginal likelihood (CML) estimation of panel ordered-response models," Journal of choice modelling, Elsevier, vol. 7(C), pages 24-43.
  3. Páez, Antonio & López, Fernando A. & Ruiz, Manuel & Morency, Catherine, 2013. "Development of an indicator to assess the spatial fit of discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 56(C), pages 217-233.
  4. Jacobs, Jan & Samarina, Anna & Heijnen, Pim & Elhorst, Paul, 2013. "State transfers at different moments in time: A spatial probit approach," Research Report 13006-EEF, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
  5. 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.
  6. Paudel, Krishna P. & Caffey, Rex H. & Devkota, Nirmala, 2011. "An Evaluation of Factors Affecting the Choice of Coastal Recreational Activities," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 43(02), May.
  7. Ferdous, Nazneen & Eluru, Naveen & Bhat, Chandra R. & Meloni, Italo, 2010. "A multivariate ordered-response model system for adults' weekday activity episode generation by activity purpose and social context," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 922-943, September.
  8. Elias, Wafa & Katoshevski-Cavari, Rachel, 2014. "The role of socio-economic and environmental characteristics in school-commuting behavior: A comparative study of Jewish and Arab children in Israel," Transport Policy, Elsevier, vol. 32(C), pages 79-87.
  9. Qu, Xi & Lee, Lung-fei, 2012. "LM tests for spatial correlation in spatial models with limited dependent variables," Regional Science and Urban Economics, Elsevier, vol. 42(3), pages 430-445.

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