I am trying to build a model that will estimate people´s willingness to pay for a certain good.
My dataset is comprised of more than 1000 observations and 30 variables. This is how my distribution looks like if I plot the frequencies of my dependent variable:
For better visibility for all values up until 1000:
The values represent the price people are willing to pay for a certain good. As you can see, the willingnes to pay varies greatly. Some people would not even pay a single Euro while others would be willing to pay up to 2000 Euro. Now I want to model, what influences the willingnes of these people to pay something. I have collected a set of theory-based explanatory variables such as age and income ect.
As a first step, I have used the descdist function of fitdistrplus package in R to produce a Cullen and Frey Plot and see which distribution fits my data best and to start model building from there.
I have tried this with just my collected data and bootstrapping and the plot indicates a Beta distribution.
library(fitdistrplus) descdist(mydata[complete.cases(mydata),"var1"], discrete= FALSE)
Yields the following graph:
And this code: library(fitdistrplus) descdist(mydata[complete.cases(mydata),"var1"], discrete= FALSE, boot = 1000)`
Yields this graph:
I am a bit puzzled by this since my data takes on values above 1 as it is not a probability distribution.
I do have a lot of true zeros which indicate people's unwillingness to pay for the good in question, but I also have values as high as 2000 €.
Does anyone have an idea what could have gone wrong and how to proceed from here?