I am new to this forum and I believe this is where my question should be posted - I apologize if I'm mistaken. I do not have a degree in math (I have a degree in Business and Finance) but am competent and am willing to learn. I do not speak the 'math language' so I'm having trouble understanding some of the research on QMLE. I was hoping someone might be able to answer the following questions in layman's terms. An example would be great.

What is rationale behind applying a QMLE to data set? From my (limited) understanding, it has something to do with when data is 'logged' and then 'unlogged' there is a downward bias that exists and that the QMLE attempts to cancel this out. Why does this downward bias occur? what is logging and unlogging doing to make it happen?

How do you determine the QMLE value? I think it's something to do with taking exp to the standard error of a regression/2 because I read it, but I don't know why. Can anyone help with a brief explanation and example?

Thanks in advance, your help is greatly appreciated.