I have a dependent variable that is a ratio, i.e. it takes the values between 0 and 1. Some 30% of values are 1s. The dependent variable measures the distribution of funds, i.e. it is calculated just like so: amount of distributed money / the total amount of proposed money. So the ratio comes from continuous data (the amounts of distributed and proposed money).

I am building a model to see what factors influence the disbursement rate.

I was told that a fractional logit regression could be appropriate in such a case? Here is a source that talks about fractional regression.

What puzzles me is that this source mentions "raw counts" in the introduction:

"Introduction

It is sometimes the case that you might have data that falls primarily between zero and one. For example, these may be proportions, grades from 0-100 that can be transformed as such, reported percentile values, and similar. If you had the raw counts where you also knew the denominator or total value that created the proportion, you would be able to just use standard logistic regression with the binomial distribution. Similarly, if you had a binary outcome (i.e. just zeros and ones), this is just a special case, so the same model would be applicable. Alternatively, if all the target variable values lie between zero and one, beta regression is a natural choice for which to model such data. However, if the variable you wish to model has values between zero and one, and additionally, you also have zeros or ones, what should you do?"

Do you know if this suggests that a dependent variable must necessarily come from count data if we are to use fractional regression, or can it come from continuous data like in my example?

Appreciate any useful sources and comments.