I have time series of several variables of 60 or so rows of count data. I want to do a regression model
y ~ x. I've chosen to use a Quasipoisson & Negative Binomial GLMs as there's overdispersion etc.
x Min. : 24000 1st Qu.: 72000 Median :117095 Mean :197607 3rd Qu.:291388 Max. :607492 y Min. : 136345 1st Qu.: 405239 Median : 468296 Mean : 515937 3rd Qu.: 633089 Max. :1218937
The data itself are very high and so it may be best to model these as count data (this is what I'm trying to investigate - at which point I can model count data as continuous). It seems to be very common practice, what I want to know is the motivation for this?
Are there any texts that actually show the problem of modelling high count data with Poisson distribution? Perhaps something that shows the factorial in the distribution makes things difficult.