Statistical Analysis for Non-Normal and Correlated Outcome in Panel Data

Abstract

There are many cases that cannot fulfill some assumptions in statistical analysis, such as normality and independence. In many practical problems, the normality as well independent assumption is not reasonable. For example, data that repeated over time tend to be correlated. If analysis ignores the non independent outcome the Standard Error (SE) on the parameter estimates tends to be too small. Generalized Linear Model (GLM), Generalized Estimating Equation (GEE), and Generalized Linear Mixed Model (GLMM) can be used for nonnormal data using the link functions. GEE includes working correlation matrix to accommodate the correlation in the data. GLMM may overcome the repeated observation and allows individual have different baseline/intercept. The study is aim at comparing result based on those approaches. The data that are used in this study are from BPS and the outcome that are used is poverty proportion. Based on the study shows that GEE approach is better than GLM for marginal model, and GLMM approach is better than GEE with dummy variable.