Masking identification of discrete choice models under simulation methods

86 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Identification of parameters in normal error component logit-mixture (NECLM) models (2007, methodology): “Within the class of continuous mixtures is the special case of random parameters, which can be applied to non-dummy as well as to dummy variables. The latter are often referred to as error component models. A variety of distributional assumptions can be used for random parameters, including normal, ”
Latent lifestyle preferences and household location decisions (2007, unknown): “varies across classes, the correlation parameters r are assumed to be the same across classes; this imposes a parsimonious specification of the error structure that eases identification (see Chiou and Walker 2006, for more discussion).”
Spatial Heterogeneity of Willingness to Pay for Forest Management (2017, background): “10 In order to make sure the maximum simulated log-likelihood approach avoids masking identification problems (Chiou and Walker 2007),”
Sampling of alternatives in Logit Mixture models (2013, methodology): “This methodology is an extension of McFadden’s (1978) result for Logit, and it builds on the methodology proposed by Guevara and Ben-Akiva (2013) for MEV models. This extension for Logit Mixture models is relevant because Logit Mixture is fully flexible, since it can approximate any random utility m”