Knowledge, attitudes and motivations among blood donors in Sao Paulo, Brazil

Recruiting safe, volunteer blood donors requires understanding motivations for donating and knowledge and attitudes about HIV. We surveyed 1,600 persons presenting for blood donation at a large blood bank in Sao Paulo, Brazil using a self-administered, structured questionnaire, and classified motivations into three domains as well as categorizing persons by HIV test-seeking behavior. Motivations, in descending order, and their significant associations were: ""altruism``: female gender, volunteer donor and repeat donor status; ""direct appeal``: female gender, repeat donor status and age 21-50 years; ""selfinterest``: male gender, age under 20 years, first-time donor status and lower education. HIV test-seekers were more likely to give incorrect answers regarding HIV risk behavior and blood donation and the ability of antibody testing to detect recent HIV infections. Altruism is the main motivator for blood donation in Brazil; other motivators were associated with specific demographic subgroups. HIV test-seeking might be reduced by educational interventions.

Monte Carlo evidence on cointegration and causation

The small sample performance of Granger causality tests under different model dimensions, degree of cointegration, direction of causality, and system stability are presented. Two tests based on maximum likelihood estimation of error-correction models (LR and WALD) are compared to a Wald test based on multivariate least squares estimation of a modified VAR (MWALD). In large samples all test statistics perform well in terms of size and power. For smaller samples, the LR and WALD tests perform better than the MWALD test. Overall, the LR test outperforms the other two in terms of size and power in small samples.

Applying linear time-varying constraints to econometric models: With an application to demand systems

When linear equality constraints are invariant through time they can be incorporated into estimation by restricted least squares. If, however, the constraints are time-varying, this standard methodology cannot be applied. In this paper we show how to incorporate linear time-varying constraints into the estimation of econometric models. The method involves the augmentation of the observation equation of a state-space model prior to estimation by the Kalman filter. Numerical optimisation routines are used for the estimation. A simple example drawn from demand analysis is used to illustrate the method and its application.