Endogenous Variables vs. Exogenous Variables

Attribute	Endogenous Variables	Exogenous Variables
Definition	Variables that are determined within the model	Variables that are determined outside the model
Control	Can be controlled or manipulated in the model	Cannot be controlled or manipulated in the model
Causality	Can cause changes in other variables in the model	Do not cause changes in other variables in the model
Endogeneity	Endogenous variables can be endogenous to the model	Exogenous variables are always exogenous to the model

Definition

Endogenous variables are those variables in a statistical model that are determined by the relationships within the model itself. These variables are influenced by other variables in the model and are often the focus of the analysis. On the other hand, exogenous variables are those variables that are not influenced by other variables in the model and are typically used as inputs or predictors in the analysis.

Relationship to the Model

Endogenous variables are crucial to the model as they are the variables that the researcher is trying to explain or predict. These variables are often the dependent variables in the model and are influenced by the independent variables. Exogenous variables, on the other hand, are used as inputs to the model to help explain or predict the endogenous variables. These variables are not influenced by other variables in the model and are considered to be exogenous to the system.

Control

Endogenous variables are often more difficult to control for in a statistical analysis because they are influenced by other variables in the model. Researchers must carefully consider the relationships between endogenous variables and other variables in the model to ensure that the results are valid. Exogenous variables, on the other hand, are easier to control for as they are not influenced by other variables in the model. Researchers can manipulate or control exogenous variables to see how they impact the endogenous variables.

Endogeneity

Endogeneity is a common issue in statistical analysis that occurs when an endogenous variable is correlated with the error term in the model. This can lead to biased estimates and incorrect conclusions. Researchers must use techniques such as instrumental variables or control functions to address endogeneity in their models. Exogenous variables, on the other hand, are not subject to endogeneity issues as they are not influenced by other variables in the model.

Causality

Endogenous variables are often used to study causal relationships between variables in a model. Researchers are interested in understanding how changes in the independent variables impact the dependent variables. Exogenous variables, on the other hand, are used as predictors in the model and are not typically used to study causal relationships. These variables are considered to be exogenous to the system and are used to help explain or predict the endogenous variables.

Examples

Examples of endogenous variables include income, education level, and job satisfaction in a model studying the relationship between education and job satisfaction. These variables are influenced by each other and are the focus of the analysis. Examples of exogenous variables in the same model could include gender, age, and location. These variables are not influenced by the other variables in the model and are used as inputs to help explain the relationship between education and job satisfaction.