01660919

Component

It is common practice to build Stated Preference (SP) attributes and alternatives from observed Revealed Preference (RP) choices. While many surveys pivot all alternatives around an observed choice, others use more adaptive approaches in which changes are made depending on what alternative was chosen in the RP setting. For example, in SP-off-RP data, the RP chosen alternative is worsened and other alternatives are improved to induce a choice change. This facilitates the creating of meaningful trade-offs or tipping points but introduces endogeneity. This source of endogeneity was largely ignored until Train and Wilson (T&W) proposed a full information maximum likelihood (FIML) solution that can be implemented with simulation. In this article the authors propose a limited information maximum likelihood (LIML) approach to address the SP-off-RP problem using a method that does not need simulation, can be applied with standard software and uses data that is already available for the stated problem. The proposed method can be seen as an application of the Control-Function (CF) method to correct for endogeneity in discrete choice models, using the RP attributes as instrumental variables. The authors discuss the theoretical and practical advantages and disadvantages of the CF and T&W methods and illustrate them using Monte Carlo and real data. The authors also suggest that the T&W results are driven by accounting for correlation across SP choices.

This paper was sponsored by TRB committee ADB40 Standing Committee on Transportation Demand Forecasting.

18-06710

English

15p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; References; Tables

Data analysis; Econometrics; Monte Carlo method; Revealed preferences; Stated preferences

Discrete choice models; Endogeneity

Data and Information Technology; Planning and Forecasting; Transportation (General)

Transportation Research Board Annual Meeting 2018 Paper #18-06710

TRIS, TRB, ATRI

Jan 8 2018 11:44AM