midks.test {acid}

Kolmogorov-Smirnov Test assessing a Parametric Mixture for a Conditional Income Distribution

Description

This function performs a Kolmogorov-Smirnov test for a parametrically specified cdf composed of a mixture distribution either by cdf.mix.dag or cdf.mix.LN.

Usage

midks.test(x, y, ..., w = NULL, pmt = NULL)

Arguments

x

a vector of observed incomes.

y

a function specifying the parametric cdf.

...

arguments to be passed to y.

w

the weights of the observations contained in y.

pmt

point mass threshold equivalent to thres0 in y.

Value

statistic

returns the test statistic.

method

returns the methodology - currently always One-sample KS-test.

diffpm

the difference of the probability for the point mass.

diff1

the upper difference between for the continuous part of the cdfs.

diff2

the lower difference between for the continuous part of the cdfs.

Author(s)

Alexander Sohn

References

Sohn, A., Klein, N. and Kneib. T. (2014): A New Semiparametric Approach to Analysing Conditional Income Distributions, in: SOEPpapers, No. 676.

Examples

# parameter values
pi0.s<-0.2
pi1.s<-0.1
thres0.s<-0
thres1.s<-25000
mu.s<-20000
sigma.s<-5
nu.s<-0.5
tau.s<-1

# generate sample
n<-100
s<-as.data.frame(matrix(NA,n,3))
names(s)<-c("cat","y","w")
s[,1]<-sample(1:3,n,replace=TRUE,prob=c(pi0.s,pi1.s,1-pi0.s-pi1.s))
s[,3]<-rep(1,n)
for(i in 1:n){
  if(s$cat[i]==1){s$y[i]<-0
  }else if(s$cat[i]==2){s$y[i]<-runif(1,thres0.s,thres1.s)
  }else s$y[i]<-rGB2(1,mu=mu.s,sigma=sigma.s,nu=nu.s,tau=tau.s)+thres1.s
}

# midks.test
midks.test(s$y,cdf.mix.dag,pi0=pi0.s,thres0=thres0.s,pi1=pi1.s,thres1=thres1.s,mu=mu.s,
sigma=sigma.s,nu=nu.s,tau=tau.s,w=s$w,pmt=thres0.s)$statistic