ic: Extract information criteria of stochastic frontier models

In sfaR: Stochastic Frontier Analysis Routines

Extract information criteria of stochastic frontier models

Description

ic returns information criterion from stochastic frontier models estimated with sfacross, sfalcmcross, or sfaselectioncross.

Usage

## S3 method for class 'sfacross'
ic(object, IC = "AIC", ...)

## S3 method for class 'sfalcmcross'
ic(object, IC = "AIC", ...)

## S3 method for class 'sfaselectioncross'
ic(object, IC = "AIC", ...)

Arguments

object	A stochastic frontier model returned by sfacross, sfalcmcross, or sfaselectioncross.
IC	Character string. Information criterion measure. Three criteria are available: 'AIC' for Akaike information criterion (default) 'BIC' for Bayesian information criterion 'HQIC' for Hannan-Quinn information criterion .
...	Currently ignored.

Details

The different information criteria are computed as follows:

• AIC: -2 \log{LL} + 2 * K

• BIC: -2 \log{LL} + \log{N} * K

• HQIC: -2 \log{LL} + 2 \log{\left[\log{N}\right]} * K

where LL is the maximum likelihood value, K the number of parameters estimated and N the number of observations.

Value

ic returns the value of the information criterion (AIC, BIC or HQIC) of the maximum likelihood coefficients.

See Also

sfacross, for the stochastic frontier analysis model fitting function using cross-sectional or pooled data.

sfalcmcross, for the latent class stochastic frontier analysis model fitting function using cross-sectional or pooled data.

sfaselectioncross for sample selection in stochastic frontier model fitting function using cross-sectional or pooled data.

Examples

## Not run: 
## Using data on Swiss railway
# LCM (cost function) half normal distribution
cb_2c_u <- sfalcmcross(formula = LNCT ~ LNQ2 + LNQ3 + LNNET + LNPK + LNPL,
udist = 'hnormal', uhet = ~ 1, data = swissrailways, S = -1, method='ucminf')
ic(cb_2c_u)
ic(cb_2c_u, IC = 'BIC')
ic(cb_2c_u, IC = 'HQIC')

## End(Not run)

sfaR documentation built on July 9, 2023, 6:58 p.m.