Re: [Gretl-devel] Report -- caution! long!

get the eigenvectors, and peel off the first few, and put these into series; these are pc. Estimate model, and perform the inverse transformation on the coefficients to get back to the coefficients in the original data rotation if desired. Stata team a long time to get it to the point that it is now. That said, it's not very hard to do in gretl on a model by model basis See chapter 16 of http://learneconometrics.com/gretl/using_gretl_for_POE4.pdf for some simple examples. The ability to jump seamlessly between matrices and gretl results makes it amazingly easy to use. In it there are a few examples of computing marginal effects and even standard errors for the average marginal effects. A more competent programmer could do something more elegant and general, but it it's not very hard to do on a case by case basis. Although, as Talha says, a 'button' or function is nice since most users don't appreciate the challenge of getting these like some of us do...:) One of the things that I've noticed about Stata's development is that they have expanded many of the commands to encompass almost every model and they continue to add functionality to them with each new version. For instance, predict is used to get in-sample model predictions, residuals, variances (e.g., from arch and garch), and to generate both static and dynamic forecasts. margins computes different types of marginal effects (discrete, continuous, average, at specific points, at the averages, and so on depending on the options used). In fact, these commands do so much it makes them a little hard to use correctly, IMO--for instance, it took some time to figure out exactly how and which marginal effects were being computed for the qualitative choice models. So, that presents something of a design choice. Should one use commands that apply to all circumstances (even though they may work differently under the hood, depending on model being estimated), or should estimation routines have their own set of postestimation commands for tests, marginal effects, and so on. I don't know the answer to this. Stata flirts with both models: ubiquitous (margins, predict) and model specific (estat postestimation commands). Open source suggests perhaps more modularity than one gets with a proprietary software like Stata (though nearly everything it does is executed in .do or .ado add-ons). What we discussed in Torun was the idea that Allin and Jack would work on the back-bone and that others would try to develop the expertise with the bundle concept to add specific functionality. So, the question is, is enhancing prediction or marginal effects a back-bone issue or an add-on? (I'm not sure) My idea of a back-bone issue would be the introduction of factor variables (version 2.0). In it, variables are defined as being continuous or discrete. They can be interacted in various combinations (continuous-discrete, continutous-continuous and discrete-discrete very easily within Stata's .do files). For us, it may solve an issue with the handling of missing values (0 or NAN) and permits a very straightforward way to interact variables and compute marginal effects in interaction models. Just a thought.... Cheers, Lee -- Lee Adkins Professor of Economics lee.adkins(a)okstate.edu learneconometrics.com