Marginal Effects in Multivariate Probit Models. [PDF]
Empir Econ, 2017 Estimation of marginal or partial effects of covariates x on various conditional parameters or functionals is often a main target of applied microeconometric analysis. In the specific context of probit models, estimation of partial effects involving outcome probabilities will often be of interest. Such estimation is straightforward in univariate models,
Mullahy J.
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Sequential Monte Carlo EM for multivariate probit models [PDF]
Computational Statistics & Data Analysis, 2013 Multivariate probit models (MPM) have the appealing feature of capturing some of the dependence structure between the components of multidimensional binary responses.
Kuipers, Jack, Moffa, Giusi
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Estimation of Multivariate Probit Models via Bivariate Probit. [PDF]
Stata J, 2016 In this article, I suggest the utility of fitting multivariate probit models using a chain of bivariate probit estimators. This approach is based on Stata's biprobit and suest commands and is driven by a Mata function, bvpmvp(). I discuss two potential advantages of the approach over the mvprobit command (Cappellari and Jenkins, 2003, Stata Journal 3:
Mullahy J.
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Simulated multivariate random-effects probit models for unbalanced panels [PDF]
The Stata Journal: Promoting communications on statistics and Stata, 2014 This article develops a method for implementing a simulated multivariate random-effects probit model for unbalanced panels (with gaps) and illustrates the model by using artificial data. Halton draws generated by mdraws are used to simulate multivariate normal probabilities with the mvnp() egen function.
Alexander Plum, Plum, Alexander
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Multivariate Probit Regression using Simulated Maximum Likelihood [PDF]
The Stata Journal, 2003 We discuss the application of the GHK simulation method for maximum likelihood estimation of the multivariate probit regression model and describe and illustrate a Stata program mvprobit for this purpose.
Stephen P Jenkins
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Prediction of Patient-Reported Outcome Measures Via Multivariate Ordered Probit Models [PDF]
Journal of the Royal Statistical Society Series A: Statistics in Society, 2014 SummaryThe assessment of patient-reported outcome measures (PROMs) is of central importance in many areas of research and public policy. Unfortunately, it is quite common for clinical studies to employ different PROMs, thus limiting the comparability of the evidence base that they contribute to.
Caterina Conigliani +2 more
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Probit Model on Multivariate Binary Response Using Simulated Maximum Likelihood Estimator
Jurnal Ilmu Dasar, 2010 In this paper, we discuss probit model on multivariate binary response. We assume that each of n individuals is observed in T responses. Yit is tth response on ith individual/subject and each response is binary.
Jaka Nugraha +2 more
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Determinants of market outlet choices by smallholder teff farmers in Dera district, South Gondar Zone, Amhara National Regional State, Ethiopia: a multivariate probit approach [PDF]
Journal of Economic Structures, 2019 In Ethiopia, teff is an important cereal crop, particularly in Dera district. It is a source of food and provides cash income for majority of smallholder farmers.
Tadie Mirie Abate +2 more
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Dynamics of multiple sustainable agricultural intensification practices adoption: Application of the intertemporal multivariate probit model. [PDF]
PLoS ONEApplying an intertemporal multivariate probit model, we reveal complex complementarity and substitution effects as well as new insights on the drivers of adopting input-intensive and natural resource management (NRM) practices in rural Ethiopia.
Ali Mohammed Oumer +2 more
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Bayesian inference for multivariate probit model with latent envelope [PDF]
BiometricsABSTRACTThe response envelope model proposed by Cook et al. (2010) is an efficient method to estimate the regression coefficient under the context of the multivariate linear regression model. It improves estimation efficiency by identifying material and immaterial parts of responses and removing the immaterial variation. The response envelope model has
Kwangmin Lee, Yeonhee Park
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