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...

We develop a Bayesian approach for the selection of skew in multivariate skew t distributions constructed through hidden conditioning in the manners suggested by either Azzalini and Capitanio (2003) or Sahu, Dey and Branco (2003). We show that the skew coefficients for each margin are the same for the standardized versions of both distributions. We introduce binary indicators to denote whether ...

Skewness is often present in a wide range of spatial prediction problems, and modeling it in the spatial context remains a challenging problem. In this study a skew-Gaussian random field is considered. The skew-Gaussian random field is constructed by using the multivariate closed skew-normal distribution, which is a generalization of the traditional normal distribution. We present an Metropolis...

In this article we introduce a new three parameter skewed distribution of which normal distribution is a special case. This distribution is obtained by using geometric sum of independent identically distributed normal random variables. We call this distribution as the geometric skew normal distribution. Different properties of this new distribution have been investigated. The probability densit...

Web Appendix A. Multivariate Skew Distributions Different versions of the multivariate skew–normal(SN) and skew-t (ST) distributions have been considered and used in the literature (Arellano-Valle and Genton, 2005; Arellano-Valle et al, 2007; Azzalini and Capitanio, 2003; Sahu et al, 2003 and among others). A new class of distributions by introducing skewness in multivariate elliptically distri...