Techniques for Estimating Spatially Dependent Discrete Choice Models

By Mark M. Fleming. 2004

183 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a9c4bc30391c32ae25a507384c2568a40bc5e701

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In Economics and Econometrics

Citations in the corpus

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).”
Accommodating Spatial Correlation Across Choice Alternatives in Discrete Choice Models: Application to Modeling Residential Location Choice Behavior (2011, background): “2 Overview of Earlier Relevant Research It has been long recognized that, while the multinomial logit (MNL) model offers computational tractability even in the presence of large choices sets, it suffers from potential violations of the IIA property arising from correlated choice alternatives that ca”
Estimation of Sample Selection Models with Spatial Dependence (2012, background): “Department of Economics, Eller College of Management, University of Arizona.”
Under the hood (2002, background): “12 A review of estimation issues in spatial latent variable models is contained in Fleming (2002). Testing for spatial autocorrelation”
A two-step estimator for a spatial lag model of counts: Theory, small sample performance and an application☆ (2010, methodology): “Fleming (2004) and LeSage and Pace (2009, Chapter 10) provide comprehensive reviews of these methods.”
Habitat and Open Space at Risk of Land-Use Conversion: Targeting Strategies for Land Conservation (2006, methodology): “Full spatial error correction for discrete-choice models using Gibbs sampling or EM algorithm are too computationally intensive for data sets larger than several hundred observations (Fleming, 2004).”
A latent variable representation of count data models to accommodate spatial and temporal dependence: application to predicting crash frequency at intersections (2011, background): “ Several studies in the past decade have considered the spatial lag error structure for binary choice models (for example, see Fleming, 2004, Franzese and Hays, 2008, Franzese et al., 2010, and LeSage and Pace, 2009 for good reviews).”
Estimators of Binary Spatial Autoregressive Models: A Monte Carlo Study (2013, unknown): “odels. On the one hand, this goal is achieved by analysing the theoretical characteristics of the main estimators for spatial models for binary response data. This topic has been in part developed by Fleming (2004) but we consider also the recent literature. Moreover, our paper is focused only on bi”
Network Effects in Public Transit Use: Evidence from a Spatially Autoregressive Mode Choice Model for New York (2008, background): “On the other hand, very few spatial discrete choice models have been estimated because of their computational complexity, but none of them deals with social interaction and spatial spill-over effects in disaggregate transport behaviour (Anselin, 1988; Fleming, 2004).”
The Composite Marginal Likelihood (CML) Inference Approach with Applications to Discrete and Mixed Dependent Variable Models (2014, background): “…of continuous dependent variables to the linear (latent) propensity variables underlying discrete choice dependent variables (see reviews of this literature in Fleming, 2004, Franzese and Hays, 2008, LeSage and Pace, 2009, Hays et al. 2010, Brady and Irwin, 2011, and Sidharthan and Bhat, 2012).”
Thirty years of spatial econometrics (2010, methodology): “Similarly, in spatial econometrics, a number of new textbooks have appeared, including Arbia (2006) and LeSage and Pace (2009), as well as multiple edited volumes, such as Anselin et al. (2004a); Getis et al. (2004); LeSage and Pace (2004); and Arbia and Baltagi (2009).”