Annesha Enam – Association between Moods and Activity Engagement Choices: An Application of Hybrid Multiple Discrete Continuous Choice (HMDC) Model

  • Date: Thursday 21 June 2018
  • Location: Business School Maurice Keyworth SR (1.31), University of Leeds
  • Time: Thursday 21 June 2018, 11:00 to 12:00

Annesha Enam, Assistant Professor at Bangladesh University of Engineering and Technology (BUET).

Association between Moods and Activity Engagement Choices: An Application of Hybrid Multiple Discrete Continuous Choice (HMDC) Model.


In the recent years, multiple discrete continuous (MDC) models have emerged as a popular framework to simultaneously model the choice of multiple goods (that are imperfect substitutes to one another) and the associated consumption quantities. The research presents a new integrated choice and latent variable (ICLV) model implementation called the Hybrid Multiple Discrete Continuous (HMDC) model that can incorporate the influence of psychological factors (modeled as latent constructs) on MDC choice behaviors. Estimation of ICLV models (with single discrete choice kernels and MDC kernels) has been a challenge owing to the high dimensional integrals involved in the likelihood function. The typically used maximum simulated likelihood estimation (MSLE) approach becomes cumbersome when the dimensionality of integration increases. In this research, a composite marginal likelihood (CML) based estimation approach is proposed for parameter estimation of the HMDC framework.

Unlike the ICLV model implementations with single discrete choice kernel, the dimension of the integral to be decomposed in the HMDC varies across observations. This necessitated the use of weights when decomposing the likelihood function using the CML approach. A simulation study was conducted using synthetic datasets to demonstrate the superiority of the weighted CML approach over its unweighted counterpart in the presence of MDC choice kernel. Simulation results of parameter estimates point to the validity and usability of the estimation technique in terms of recovering the consistent and efficient estimates of the true parameters with an average absolute percentage bias (APB) value of 0.636% and an asymptotic relative efficiency of 1.099. The applicability of the proposed model formulation and associated estimation routine was demonstrated using an empirical case study with data from the 2013 American Time Use Survey (ATUS). In the case study, the association between individuals’ day level moods and their discretionary activity participation and time allocation decisions are explored. The empirical study was followed by a validation study with hold out sample to demonstrate the forecasting ability of the ICLV framework with MDC kernel. The empirical study identifies interesting association between day level moods and discretionary activity participation decisions. Studying such associations allow us to unravel unobserved heterogeneity in the activity participation and time allocation behavior due to moods. The endogenous treatment of moods also allows us to capture non-linear influence of different exogenous variables on the choice i.e. their direct influence on the choice outcome and their indirect influence through their correlations with the moods variables.

About Annesha: Annesha has recently completed her doctoral studies from the University of Connecticut, USA on activity and travel behaviour modelling and has received the Charley V. Wootan Memorial Award from the Council of University Transportation Centers in the USA for the Best Dissertation in Transport Policy and Planning. Prior to that, she has completed her master’s degree from BUET in 2010 where she had worked with me on willingness-to-pay for mass paid transit in Dhaka.

Annesha’s research interests include econometric modelling of choice behaviour, activity time use and well-being, transportation planning, use of data mining and machine learning methodologies for understanding choice behaviour as well as understanding transportation safety issues.