STOCHASTIC PROCESS

(Statistics) A process is said to be stochastic when its future cannot be predicted exactly from its past; describing an event or process that involves random chance or probability. A stochastic relationship is assumed to be inexact and therefore involves a Disturbance (or Error) Term which is used to account for the unexplainable portion of the relationship. Consequently, a simple (stochastic) functional relationship shows that for any time period, t (where t=1, 2, ..., n), the relationship between the dependent (Endogenous) variable, Y, and the independent (Exogenous) variable, X, may be written as: Yt = a + ß Xt + et; where: Y represents the dependent variable of variable to be explained; t represents time periods of observation (i.e.,t=1,2,...,n); a (alpha) represents the constant term (without a time reference); ß (beta, also a constant term without a time reference) represents the coefficient of the independent variable, X; X represents the independent, or explanatory variable; and e (epsilon), the error term, represents the value of the unexplained disturbance term. Contrast with Deterministic Process.