Endogenous variables are of primary importance to economists as they are central to both the predicting and the understanding of economic trends. Unlike exogenous variables, which either affect a system from outside, or remain constant and unaffected by the system itself, endogenous variables are determined within the system and affect the system.

Essentially, endogenous variables are determined by their relationship with other variables and are thus not independent of their environment. Examples of endogenous variables include investment, savings and consumption decisions, inflation and economic growth, and market prices and interest rates. For example, an increase in the supply of money will often cause a rise in market prices, which means that the total demand for goods and services remains unchanged.

In terms of econometrics, endogenous variables represent a dependent variable, which means they are affected by changes in other variables in the system. These changes are often modeled through regression techniques or experimental design, with the goal of understanding how one variable impacts the other. Endogenous variables can also act as a measure of the effectiveness of a particular policy or program, by measuring how the policy affects other economic variables.

In economic theory, endogenous variables are often used to determine the cause of a particular effect. For example, economists can examine how an increase in national income might affect the unemployment rate. If they can show that the rise in national income is the main cause behind the observed change in the unemployment rate, then those two variables are said to be endogenous.

Endogenous variables hold the key to unraveling many of the mysteries of the world economy. By understanding the relationship between different variables, economists are able to gain a better understanding of how a particular policy or program affects the economy, and in turn, are better able to make informed decisions. This knowledge is essential for creating better economic models, which can be used for forecasting and policymaking.