AIC.fixest: Aikake's an information criterion

In fixest: Fast Fixed-Effects Estimations

Aikake's an information criterion

Description

This function computes the AIC (Aikake's, an information criterion) from a fixest estimation.

Usage

## S3 method for class 'fixest'
AIC(object, ..., k = 2)

Arguments

object	A fixest object. Obtained using the functions femlm, feols or feglm.
...	Optionally, more fitted objects.
k	A numeric, the penalty per parameter to be used; the default k = 2 is the classical AIC (i.e. AIC=-2*LL+k*nparams).

Details

The AIC is computed as:

AIC = -2\times LogLikelihood + k\times nbParams

with k the penalty parameter.

You can have more information on this criterion on AIC.

Value

It return a numeric vector, with length the same as the number of objects taken as arguments.

Author(s)

Laurent Berge

See Also

See also the main estimation functions femlm, feols or feglm. Other statictics methods: BIC.fixest, logLik.fixest, nobs.fixest.

Examples

# two fitted models with different expl. variables:
res1 = femlm(Sepal.Length ~ Sepal.Width + Petal.Length +
             Petal.Width | Species, iris)
res2 = femlm(Sepal.Length ~ Petal.Width | Species, iris)

AIC(res1, res2)
BIC(res1, res2)

fixest documentation built on Nov. 24, 2023, 5:11 p.m.