Description Usage Arguments Details Value Author(s) References See Also Examples
This function compares two bivariate copula-based regression models
1 | vuongtest(model1,model2,selection="AIC")
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model1 |
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model2 |
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selection |
model selection criterion. Options are AIC and BIC. Default ist AIC. |
Let us denote by { \ell}^{(1)},\,{\bm \ell}^{(2)} the vectors of pointwise loglikelihoods for a model with copula family 1 and 2 respectively. Here, we assume that both models have the same degrees of freedom, i.e. the same number of parameters. We now compute the differences of the pointwise loglikelihood as
m_i:=\ell^{(1)}_i - \ell^{(2)}_i,\ i=1,…,n\,.
Denote by
\overline{m}=\frac{1}{n}∑_{i=1}^n m_i
the mean of the differences. The test statistic
T_V:= \frac{√{n}\cdot\overline{m}}{ √{∑_{i=1}^n ≤ft(m_i - \overline{m}\right)^2}},
is asymptotically normally distributed with zero mean and unit variance. Hence, we prefer copula family 1 to copula family 2 at level α if
T_V> Φ^{-1}≤ft(1-\frac{α}{2}\right)\,,
where Φ denotes the standard normal distribution function. If
T_V< Φ^{-1}≤ft(\frac{α}{2}\right)\,,
we prefer copula family 2. Otherwise, no decision among the two copula families is possible. If the models contain different numbers of estimated parameters, the test statistic is corrected using either the AIC or BIC criterion.
value of the test statistic
Nicole Kraemer
N. Kraemer, E. Brechmann, D. Silvestrini, C. Czado (2013): Total loss estimation using copula-based regression models. Insurance: Mathematics and Economics 53 (3), 829 - 839.
Vuong, Q. H. (1989). Ratio tests for model selection and non-nested hypotheses. Econometrica 57 (2), 307-333.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | n<-200 # number of examples
R<-S<-cbind(rep(1,n),rnorm(n)) # design matrices with intercept
alpha<-beta<-c(1,-1) # regression coefficients
exposure<-rep(1,n) # constant exposure
delta<-0.5 # dispersion parameter
tau<-0.3 # Kendall's tau
family=3 # Clayton copula
# simulate data
my.data<-simulate_regression_data(n,alpha,beta,R,S,delta,tau,family,TRUE,exposure)
x<-my.data[,1]
y<-my.data[,2]
# joint model without standard errors
my.model.clayton<-copreg(x,y,R,S,family=3,exposure,FALSE,TRUE)
my.model.gauss<-copreg(x,y,R,S,family=1,exposure,FALSE,TRUE)
#
vuongtest(my.model.clayton,my.model.gauss)
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