The Vuong test is likelihood-ratio-based tests that can be used for choosing between two non-nested models.
vuong.test(obj1, obj2, sig.lev = 0.05)
It returns a decision.
Objects of the two fitted bivariate non-nested models.
Significance level used for testing.
Maintainer: Giampiero Marra giampiero.marra@ucl.ac.uk
The Vuong test is a likelihood-ratio-based tests for model selection that use the Kullback-Leibler information criterion, and that can be employed for choosing between two bivariate models which are non-nested.
The null hypothesis is that the two models are equally close to the actual model, whereas
the alternative is that one model is closer. The test follows asymptotically a standard normal
distribution under the null. Assume that the critical region is \((-c,c)\), where \(c\) is typically set to 1.96. If the value
of the test is higher than \(c\) then we reject the null hypothesis
that the models are equivalent in favor of model obj1. Viceversa if the value is smaller than \(c\). If
the value falls in \([-c,c]\) then we cannot discriminate between the two competing models given the data.
Vuong Q.H. (1989), Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses. Econometrica, 57(2), 307-333.