One of the great new features in Stata 13 is a command called forecast. This is not just another version of predict, it’s more like a forecast system management/dependency tool. You can take one or more regressions and deterministic equations and forecast takes your exogenous variables, pulls their values from your data set, feeds them into the equations/regressions that use them, take the resulting endogenous variables and feeds them into the equations/regressions that use them, chaining together a whole multi-part forecast. It also has tools for testing alternative scenarios, for inserting shocks and other modifications of endogenous variables, and for calculating confidence intervals of the system via simulation.
Statablog
Stata for R users pt 2
In Part 1 of this Stata for R users series, I mentioned many of the strengths of Stata that might be attractive to R users. I forgot several important strengths, which I’ll add and expand in Part 3. The more I work with it, the more I’m impressed with Stata.
In this Part 2, as promised, I’ve listed several thoughts and tips for R users who are new to Stata.
Stata for R users pt 1
I did a quick Google search on “Stata for R users” (both as separate words and as a quoted phrase) and there really isn’t much out there. At best, there are a couple of equivalence guides that show you how to do certain tasks in both programs. (Plus a whole lot of “R for (ex-) Stata users” articles.) I’m writing this post, as a long-term R user who recently bought Stata, because I believe that Stata is a good complement to R, and many R users should consider adding it to their toolbox.
I’m going to write this in two parts. Part one will describe why an R user might be interested in Stata — with various Stata examples. Part Two will give specific tips and warnings to R users who do decide to use Stata.
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Thinkinator 