Bivariate Binary Regression Research Articles

Application of Copulas to Improve the Modeling of Housing Tenure and Affordability

The tenure statuses made by households (renter-occupancy or owner-occupancy) are influenced by a multitude of factors, some of which cannot be directly measured. However, economists are still interested in knowing the relative severity and patterns of influence these factors have on housing tenure status. The purpose of this research was to determine the effect of assuming joint dependency between housing tenure and affordability on the model results. The effect of model-mis-specification on severity and relative importance of the explanatory variables was also assessed. Joint bivariate binary regression was applied to multi-year cross-sectional General Household Survey (GHS) data from Statistics South Africa (STATSA). An assumption of a univariate model when modeling both housing affordability and tenure led to model mis-specification, because most of the coefficients between the univariate and bivariate joint models were significantly different. Model mis-specification also led to significant differences in rankings of the levels of influence of the explanatory variables. Bivariate joint modeling with appropriate error-model copulas improved the model results. Older households that were above 49 years were consistently more likely to be owner-occupiers, and the household head age variable for older households was the most influential factor for housing owner-occupancy and affordability.

An Investigation of Agricultural Farm Dynamics using Bivariate Binary Regression

Bivariate binary regression is a convenient way to identify the covariates associated with two correlated binary variables. At the same time, logarithm of odds ratio can be utilized to cross-check their interactions validity. Model parameters can be estimated in Bayesian approach with available prior information. This paper uses data obtained from a longitudinal survey conducted in 2002–2005. The aim of this survey was to make a guideline for the agricultural development and food security in Africa. Using available variables, the terms ‘Extensification’ and ‘Intensification’ was defined as two traditions for the farm dynamics. A simple data analysis is done using bivariate binary regression in WinBUGS. Results indicate that some factors, for instance, ‘availability of new crop technology’, ‘import of Maize’ and ‘stopped intercropping’ shows negative association with farm dynamics. These variables are hindering the production of Maize and areal increase of its cultivation. Whereas ‘Change in fertilizer use’, ‘cultivated area increase’ and ‘started selling maize’ are the factors supporting the argument of areal cultivation and Maize production increase. The most important explanation of farm dynamism is described by farm holders’ access to modern crop technologies, and commercial incentives by the government to staple crop production.