Exogenous Variable

What is an Exogenous Variable?

An exogenous variable is a variable that appears in a model, but whose value is determined outside of it. Its name derives from the Greek words exo, meaning “outside”, and gen, meaning “born”.

Sometimes the term exogenous variable is used with the meaning “independent variable”, which identifies those variables in an equation whose values determine the values of variables explained by the equation (also called dependent or endogenous variables). This usage is very common in econometrics literature, where the term has also been applied with the meaning “not correlated with the error term”.

For example, in the LM economic model used to determine the interest rate given a level of money supply, the money supply is the exogenous variable and the interest rate the endogenous factor.

However, a factor that is considered exogenous in one model, may be defined as endogenous in another model. This can happen when, for example, a model becomes part of another more comprehensive model.

For example, the IS model considers interest rate as exogenous and the LM model defines income (output) as exogenous. However, when both models are combined in the IS-LM model, both interest rate and output are considered endogenous to it.


Why are Exogenous Variables Important?

When creating a model, researchers have to identify those variables that pertain to it. This is often a difficult task that requires systematic analysis. In this process, it is important to identify those factors that are outside the scope of the model, those who influence the outcomes of the model (exogenous), and those that are determined by the model (endogenous). Failure to do so can result in an inefficient or illogical model that fails to produce the expected results.


Exogenous Variables + LogicPlum

As a modeling tool, LogicPlum’s platform deals with endogenous and exogenous variables. Besides, the platform uses techniques to re-arrange, eliminate, or group input factors. However, as this system works in an automated way, users don’t have to understand the statistical and mathematical fundamentals behind the obtained model, and only need to concern themselves with the interpretation of the results according to their fields of expertise.


Additional Resources

Wikipedia. (2020). Dependent and Independent Variables. Available at https://en.wikipedia.org/wiki/Dependent_and_independent_variables



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