You both raise good questions- one the one hand, the more transparency and explanation the better, but on the other hand there are space constraints and you hope you can assume some knowledge on the part of your readers. The most important consideration when you're deciding how much to explain is to be certain the reader can know what your results mean. Rather than just reporting a coefficient, explain what that coefficient means in terms of your substantive question. This is why presenting first differences and other quantities that are truly of interest is useful. People care about 'results,' so your quantities of interest should truly be of interest and should be easy to understand. You should explain as much of the model as you think is necessary for readers to understand your quantities of interest. No reader wants to be walked through the lines of algebra in any estimator, so often you have to rely on the reader trusting you that the model is producing the quantities of interest you say. Often it's enough to give an introduction to what the model 'does,' a couple of sentences about what sorts of questions the model is usually used to ask, maybe include a citation to a more thorough exposition of the model, and then spend the rest of the explaining time discussing the quantities of interest from the model rather than the nuts and bolts of the model itself. Like all parts of the project, though, what works best requires a judgment call. Without seeing more of the papers you describe, Sean it seems like a citation to the paper you mention could be used in place of most of the detailed description of the model, and John, as per above, you probably don't need to discuss much of the underlying math in those models- just make sure the reader knows what the output of those sorts of models really says. You know your own projects better than I do, though, so use your discretion. Best of luck in the final hours, Jenn On Sat, May 3, 2008 at 10:29 PM, John Sheffield <john.sheffield at gmail.com> wrote: