Application of Quantile Regression: Modeling Body Mass Index in Ethiopia

Child malnutrition is the leading public health problem in developing countries. It is a major cause of child morbidity and mortality. Under-five children are the most vulnerable group for malnutrition. Body Mass Index (BMI) is a measure of nutritional status and is defined as the ratio of weight (kg) to squared height (m²). Studying the determinants of under-five children’s BMI is an important issue that needs to be addressed. This study applies quantile regression to study the determinants of under-five children BMI in Ethiopia.

The weight-for-height index measures BMI. Quantiles are a generalization of percentiles for continuous random variables. Because it makes no distributional assumption about the error term in the model, quantile regression offers considerable model robustness.

The findings using quantile regression at different quantile levels were presented. The estimates across quantile levels were also performed. The findings of this study identified that for children under the age of five, the current age of mother, mother’s BMI, region (SNNPR and Somali) and weight of the child at birth (average and large) were found to be significantly affecting under-five children’s BMI across quantile levels.

Quantile regression allows us to study the impact of predictors on different quantiles of the response distribution, and thus provides a complete picture of the relationship between the dependent and explanatory variables.