'The University of Texas at Austin, Bureau of Economic Geology'
Abstract
This paper formulates a generalized heterogeneous data model (GHDM) that jointly handles
mixed types of dependent variables—including multiple nominal outcomes, multiple ordinal
variables, and multiple count variables, as well as multiple continuous variables—by
representing the covariance relationships among them through a reduced number of latent
factors. Sufficiency conditions for identification of the GHDM parameters are presented. The
maximum approximate composite marginal likelihood (MACML) method is proposed to
estimate this jointly mixed model system. This estimation method provides computational time
advantages since the dimensionality of integration in the likelihood function is independent of
the number of latent factors. The study undertakes a simulation experiment within the virtual
context of integrating residential location choice and travel behavior to evaluate the ability of the
MACML approach to recover parameters. The simulation results show that the MACML
approach effectively recovers underlying parameters, and also that ignoring the multidimensional
nature of the relationship among mixed types of dependent variables can lead not
only to inconsistent parameter estimation, but also have important implications for policy
analysis.Civil, Architectural, and Environmental Engineerin