Many phenomena are correlated and spatially dependent. In addition, some of them ordinal discrete responses. Thus, a model is needed to capture the interactions multivariate outcomes spatial dependences. Following Smith LeSage (2004) Jeliazkov et al. (2008), this study proposes new algorithm for ordered probit (MSOP) address need. applying model, parameters calculated using Bayesian inference b...

Spatial econometrics has few studies on multivariate ordinal responses. This study proposes a dynamic spatial ordered probit (DMSOP) model, which is the first attempt to capture temporal and dependencies simultaneously for The parameters are calculated using Bayesian inference based Markov chain Monte Carlo sampling. DMSOP model performs effectively with simulated data. Furthermore, applied two...

The groundnut plant has the ability to survive in areas of low rainfall because it is a legume and it increases soil fertility by fixing nitrogen in the soil. The study area is known by erratic and uneven rainfall while groundnut is the main cash crop in the area. The study identified the groundnut market outlets, factor affecting groundnut market outlet choice and identifies farm level women r...

In this paper it is shown that using the maximum likelihood ML prin ciple for the estimation of multivariate probit models leads to consistent and normally distributed pseudo maximum likelihood regression parame ter estimators PML estimators even if the true correlation structure of the responses is misspeci ed As a consequence e g the PML estimator of the random e ects probit model may be used...

In the current paper, we propose a new utility-consistent modeling framework to explicitly link a count data model with an event type multinomial choice model. The proposed framework uses a multinomial probit kernel for the event type choice model and introduces unobserved heterogeneity in both the count and discrete choice components. Additionally, this paper establishes important new results ...

We propose a multivariate probit model that is defined by a confirmatory factor analysis model with covariates for analyzing dichotomous data in medical research. Our proposal is a generalization of several useful multivariate probit models, and provides a flexible framework for practical applications. We implement a Monte Carlo EM algorithm for maximum likelihood estimation of the model, and d...

Electronic voting could increase citizens’ electoral participation and trust in countries characterized by fragile democratic institutions and public discredit of the political system such as those in Latin America. This paper examines attitudes towards e-voting among participants in a large scale pilot project conducted in Colombia in 2007, focusing on the perceived reliability and usability o...

This study explores the relationships between adoption and consideration of three travel-related strategy bundles (travel maintaining/increasing, travel reducing, and major location/lifestyle change), linking them to a variety of explanatory variables. The data for this study are the responses to a fourteen-page survey returned by nearly 1,300 commuting workers living in three distinct San Fran...

In this article we present the Bayesian estimation of spatial probit models in R and provide an implementation in the package spatialprobit. We show that large probit models can be estimated with sparse matrix representations and Gibbs sampling of a truncated multivariate normal distribution with the precision matrix. We present three examples and point to ways to achieve further performance ga...