Exact inference on poverty predictors based on logistic regression approach

The paper deals with the exact inference on poverty indicators included in a predictive logistic regression model as predictor variables. Based on a multiple stratification applied in a household survey, small size or unbalanced subgroups are likely to occur in practice with regard to the number of poor and hence the standard unconditional maximum likelihood estimation of a regression parameter may fail to exist. Focus is brought on exact inference which is still possible to make even at this case. The paper gives a brief overview of problems of exact p-value and confidence interval calculation in small samples for the case when the unconditional maximum likelihood estimate does not exist or the large sample asymptotic properties are violated. Besides, some empirical examples are presented based on a survey of Hungarian households