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In the LM model of interest rate determination, [1]: pp. 261–7 the supply of and demand for money determine the interest rate contingent on the level of the money supply, so the money supply is an exogenous variable and the interest rate is an endogenous variable.
This model uses the following variables: Y is real GDP; C is real consumption; I is real physical investment, including intended inventory investment; G is real government spending (an exogenous variable) M is the exogenous nominal money supply; P is the exogenous price level; i is the nominal interest rate; L is liquidity preference (real ...
The exogenous latent variables are background variables postulated as causing one or more of the endogenous variables and are modeled like the predictor variables in regression-style equations. Causal connections among the exogenous variables are not explicitly modeled but are usually acknowledged by modeling the exogenous variables as freely ...
Of course, changes in these variables in the opposite direction shift the IS curve in the opposite direction. The IS–LM model also allows for the role of monetary policy. If the money supply is increased, that shifts the LM curve downward or to the right, lowering interest rates and raising equilibrium national income.
Variables may be of many types; real or integer numbers, Boolean values or strings, for example. The variables represent some properties of the system, for example, the measured system outputs often in the form of signals, timing data, counters, and event occurrence. The actual model is the set of functions that describe the relations between ...
Statistical packages implement the ARMAX model through the use of "exogenous" (that is, independent) variables. Care must be taken when interpreting the output of those packages, because the estimated parameters usually (for example, in R [15] and gretl) refer to the regression:
Again, each endogenous variable depends on potentially each exogenous variable. Without restrictions on the A and B, the coefficients of A and B cannot be identified from data on y and z: each row of the structural model is just a linear relation between y and z with unknown coefficients. (This is again the parameter identification problem.)
For example, in a regression model in which cigarette smoking is the independent variable of primary interest and the dependent variable is lifespan measured in years, researchers might include education and income as additional independent variables, to ensure that any observed effect of smoking on lifespan is not due to those other socio ...