**Definition:**Akaike's Information Criterion is a criterion for selecting among nested econometric models. The AIC is a number associated with each model:

AIC=ln (s_{m}^{2}) + 2m/T

where m is the number of parameters in the model, and
s_{m}^{2} is (in an AR(m) example) the estimated residual
variance: s_{m}^{2} = (sum of squared residuals for model
m)/T. That is, the average squared residual for model m.

The criterion may be minimized over choices of m to form a tradeoff between
the fit of the model (which lowers the sum of squared residuals) and the
model's complexity, which is measured by m. Thus an AR(m) model versus an
AR(m+1) can be compared by this criterion for a given batch of data.

An equivalent formulation is this one: AIC=T ln(RSS) + 2K where K is the number of regressors, T the number of obserations, and RSS the residual sum of squares; minimize over K to pick K.(Econterms)

**Terms related to Akaike's Information Criterion:**