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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