Likelihood Ratio test for Stochastic Frontier Models

Description

Testing parameter restrictions in stochastic frontier models by a Likelihood Ratio test.

Usage

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   ## S3 method for class 'frontier'
lrtest( object, ... )

Arguments

object

a fitted model object of class frontier.

...

further fitted model objects of class frontier.

Details

If lrtest.frontier is called with only one argument/object (i.e. argument ... is not used), it compares the fitted model to a corresponding model without inefficiency (i.e. estimated by OLS).

If lrtest.frontier is called with more than one argument/object (i.e. argument ... is used), it consecutively compares the fitted model object object with the models passed in ....

The test statistic is 2 * ( logLik( mu ) - logLik( mr ) ), where mu is the unrestricted model and mr is the restricted model.

If a Frontier model (estimated by ML) is compared to a model without inefficiency (estimated by OLS), the test statistic asymptotically has a mixed χ^2 distribution under the null hypothesis (see Coelli, 1995).

If two Frontier models (estimated by ML) are compared, the test statistic asymptotically has a χ^2 distribution with j degrees of freedom under the null hypothesis, where j is the number of restrictions.

Value

An object of class anova, which contains the log-likelihood value, degrees of freedom, the difference in degrees of freedom, likelihood ratio Chi-squared statistic and corresponding p value. See documentation of lrtest in package "lmtest".

Author(s)

Arne Henningsen

References

Coelli, T.J. (1995), Estimators and Hypothesis Tests for a Stochastic: A Monte Carlo Analysis, Journal of Productivity Analysis, 6, 247-268.

See Also

sfa, lrtest

Examples

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# rice producers in the Philippines (panel data)
data( "riceProdPhil" )
library( "plm" )
riceProdPhil <- plm.data( riceProdPhil, c( "FMERCODE", "YEARDUM" ) )

# Error Components Frontier with truncated normal distribution
# and time effects (unrestricted model)
mu <- sfa( log( PROD ) ~ log( AREA ) + log( LABOR ) + log( NPK ),
   truncNorm = TRUE, timeEffect = TRUE, data = riceProdPhil )

# Error Components Frontier with half-normal distribution
# without time effects (restricted model)
mr <- sfa( log( PROD ) ~ log( AREA ) + log( LABOR ) + log( NPK ),
   data = riceProdPhil )

## compare the two models by an LR-test
lrtest( mu, mr )

## compare each of the models to a corresponding model without inefficiency
lrtest( mu )
lrtest( mr )