VuongClarke.bcm(obj1, obj2, sig.lev = 0.05)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.
In the Clarke test, if the two models are statistically equivalent then the log-likelihood ratios of the
observations should be evenly distributed around zero
and around half of the ratios should be larger than zero. The test follows asymptotically a binomial distribution with
parameters $n$ and 0.5. Critical values can be obtained as shown in Clarke (2007). Intuitively,
the model in obj1 is preferred over that in obj2 if the value of the test
is significantly larger than its expected value under the null hypothesis ($n/2$), and vice versa. If
the value is not significantly different from $n/2$ then obj1 can be thought of as equivalent to obj2.
For details on the actual implementation of the tests see Radice, Marra and Wojtys (submitted).SemiParSampleSel