SemiParBIVProbit (version 3.2-11)

VuongClarke.bcm: Vuong and Clarke tests

Description

The Vuong and Clarke tests are likelihood-ratio-based tests that can be used for choosing between two non-nested models.

Usage

VuongClarke.bcm(obj1, obj2, sig.lev = 0.05)

Arguments

obj1,obj2

Objects of the two fitted bivariate non-nested models.

sig.lev

Significance level used for testing.

Value

• It returns two decisions based on the tests and criteria discussed above.

Details

The Vuong (1989) and Clarke (2007) tests are likelihood-ratio-based tests for model selection that use the Kullback-Leibler information criterion. The implemented tests can be used for choosing between two bivariate models which are non-nested. In the Vuong test, 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 the model in 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).

References

Clarke K. (2007), A Simple Distribution-Free Test for Non-Nested Model Selection. Political Analysis, 15, 347-363. Radice R., Marra G. and M. Wojtys (submitted), Copula Regression Spline Models for Binary Outcomes. Vuong Q.H. (1989), Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses. Econometrica, 57(2), 307-333.

See Also

SemiParBIVProbit

Examples

## see examples for SemiParBIVProbit