A UNIFIED MIXED LOGIT FRAMEWORK FOR MODELING REVEALED AND STATED PREFERENCES: FORMULATION AND APPLICATION TO CONGESTION PRICING ANALYSIS IN THE SAN FRANCISCO BAY AREA

By Chandra R. Bhat, Saul Castelar. 2002

300 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Transportation

View Paper: https://www.semanticscholar.org/paper/27d72cc35f6bee60803abffa102ea69d91f96d6f

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Citations in the corpus

The Impact of Demographics, Built Environment Attributes, Vehicle Characteristics, and Gasoline Prices on Household Vehicle Holdings and Use (2009, background): “From a travel demand forecasting perspective, household vehicle holdings has been found to impact almost all aspects of daily activity-travel patterns, including the number of out-of-home activity episodes that individuals participate in, the location of outof-home participations, and the travel mod”
Estimation on stated-preference experiments constructed from revealed-preference choices (2008, methodology): “Examples of this pooled approach include Ben-Akiva and Morikawa (1990), Hensher and Bradley (1993), and Hensher et al. (1999) within a logit specification and Brownstone et al. (2000) and Bhat and Castelar (2002) using mixed logit.”
A simulation evaluation of the maximum approximate composite marginal likelihood (MACML) estimator for mixed multinomial probit models (2011, background): “Finally, in the case of panel data, and when the random coefficients have both an intra-individual and inter-individual random component (see Bhat and Castelar, 2002; Bhat and Sardesai, 2006, Hess and Rose, 2009), the utility structure may be written as: , qitqitU ε+′= qitqt xβ (5) where qtqqt βββ…”
Combining Revealed and Stated Preference Data to Estimate the Nonmarket Value of Ecological Services: An Assessment of the State of the Science (2008, unknown): “In addition to scale, Bhat and Castelar (2002) raise three issues for RP-SP studies: inter-alternative error structure, unobserved heterogeneity, and state-dependence.”
Combining Discrete and Continuous Representations of Preference Heterogeneity: A Latent Class Approach (2010, background): “have found that when heterogeneous preferences are not properly accounted for, valuable information is discarded and inconsistent estimates and biased welfare measures are obtained (Bhat, 1997; Bhat and Castelar, 2002; Hess”
Hybrid Choice Models: Progress and Challenges (2002, background): “More recently, Bhat and Castelar (2002) , Brownstone, Bunch, and Train (2000) and Walker and Ben-Akiva (2001) have integrated revealed and stated preference data while also utilizing the flexibility of mixed logits to obtain more realistic representations of differences across various sources of da”
Efficient stated choice experiments for estimating nested logit models (2009, methodology): “…designs to the nested logit (NL) model form, which has become a popular tool in estimating models based on SC data in the transportation area (e.g., Bhat and Castelar, 2002; Brownstone and Small, 2005; Cherchi and Ortúzar, 2002; Hess and Polak, 2006a,b; Hess et al., 2006; Polydoropoulou and…”
Combining RP and SP data: biases in using the nested logit ‘trick’: contrasts with flexible mixed logit incorporating panel and scale effects (2008, background): “Formally, reference dependence is defined as (Bhat and Castelar 2002): , (1 ) qt RPqϕ δ− (6) where ,qt RPδ = 1 if an RP obs, 0 otherwise, and qϕ is the parameter estimate of reference dependence which can be fixed or random.”
Empirical Identification in the Mixed Logit Model: Analysing the Effect of Data Richness (2008, background): “Several studies (Munizaga and Alvarez 2001, 2005; Hensher and Greene 2003; Bhat and Castelar 2002; Swait and Bernardino 2000; Hensher 1998) have investigated the difficulty of distinguishing different substitution patterns in a ML, referring mainly to the scale problem.”
State dependence and heterogeneity in fishing location choice (2005, background): “A handful of authors have explored heterogeneity in state dependence in other discrete choice settings by interacting state dependence terms with other covariates [8], estimating correlation in state dependence across product categories [31], estimating individual-level regressions [12], and by esti”
A Comprehensive Analysis of Built Environment Characteristics on Household Residential Choice and Auto Ownership Levels (2007, background): “Third, auto ownership has been found to impact almost all aspects of daily activity-travel patterns, including the number of out-of-home activity episodes that individuals participate in, the location of out-of-home participations, and the travel mode and time-of-day of out-of-home activity particip”