A non-separable model error term refers to the part of the model that cannot be explained by the independent variables. It represents the random variability in the outcome variable that is not accounted for by the explanatory variables.
The non-separable model error term can affect the results of an impact evaluation by introducing bias into the estimated effects of the independent variables. This can lead to incorrect conclusions about the effectiveness of a program or intervention.
No, non-separable model error terms cannot be eliminated completely. However, they can be minimized by carefully selecting and controlling for relevant variables and using appropriate statistical methods.
There are several potential sources of non-separable model error terms, including measurement error, omitted variables, and unobserved heterogeneity among individuals or groups being evaluated.
To address non-separable model error terms, researchers can use techniques such as instrumental variables, fixed effects, or difference-in-differences to control for potential sources of bias. It is also important to carefully consider and justify the choice of variables and statistical methods used in the evaluation.