A MIXED SPATIALLY CORRELATED LOGIT MODEL: FORMULATION AND APPLICATION TO RESIDENTIAL CHOICE MODELING

By Chandra R. Bhat, Jessica Y. Guo. 2004

370 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Transportation

View Paper: https://www.semanticscholar.org/paper/53e10592fa8ffeb10c26881a8e2f612e3c223137

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Citations in the corpus

A Comprehensive Analysis of Built Environment Characteristics on Household Residential Choice and Auto Ownership Levels (2007, result): “The income dissimilarity measure, captured by the absolute difference between zonal median income and household income, confirms the income segregation phenomenon observed in earlier studies (Waddell, 1993; Bhat and Guo, 2004).”
Endogeneity in Residential Location Choice Models (2006, background): “Variables a, b, c, and d were considered iid uniform (1, 2) for each household and alternative.”
Correcting for endogeneity due to omitted attributes in discrete-choice models: the multiple indicator solution (2016, result): “…in this type of models, the estimated coefficient of price may be less negative than its population value, or even positive, a finding that has been profusely reported (Bhat and Guo 2004; Guevara and Ben-Akiva 2006, 2012; Levine 1998; Quigley 1976; Sermons and Koppelman 2001; Waddell 1992).”
The Composite Marginal Likelihood (CML) Inference Approach with Applications to Discrete and Mixed Dependent Variable Models (2014, background): “…effect, it is also likely that there is heterogeneity (i.e., differences in relationships between the dependent variable of interest and the independent variables across decision-makers or spatial units (see, Fotheringham and Brunsdon, 1999, Bhat and Zhao, 2002, Bhat and Guo, 2004).”
Numerical Analysis of Effect of Sampling of Alternatives in Discrete Choice Models (2004, background): “analysis literature for capturing such spatial correlation is to allow contiguous alternatives to be correlated (4).”
Accommodating Spatial Correlation Across Choice Alternatives in Discrete Choice Models: Application to Modeling Residential Location Choice Behavior (2011, methodology): “Discrete choice modeling has a unique place in this literature since it helps understand the trade-offs of various sociodemographic and locational variables, and allows the identification of the sensitivity of different demographic segments to the residential choice attributes (see Sermons and Koppe”
Modeling Elicitation effects in contingent valuation studies: a Monte Carlo Analysis of the bivariate approach (2005, methodology): “In this article, we illustrate the potential of an alternative approach, based on the integration of GEV-style choice probabilities over the distribution of taste coefficients, leading to a Mixed GEV model (c.f. Chernew et al., 2003; Bhat and Guo, 2004 ).”
Spatial statistics for urban analysis: A review of techniques with examples (2004, methodology): “Also, the MAUP has received attention in the context of modeling the residential location choices of households (Guo and Bhat, 2004).”
The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models (2011, background): “Spatial correlation may exist across discrete choice alternatives (see, for example, Bolduc et al., 1996; Bhat and Guo, 2004; Miyamoto et al., 2004; Sener et al., 2011) or across decisionmakers (see, for example, Anselin, 2003; Fleming, 2004; Franzese and Hays, 2008; Bhat and Sener, 2009).”