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Great Idea #1
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Thanks for your kind words! I suspect that many of your needs are already met by StatsModels.jl, even if the syntax is a little different. For instance, you can pass "hints" when constructing a schema to indicate that you want to treat a variable as a The way that StatsModels.jl is designed, any kind of special syntax needs to be in the form of a function call. So using Lead/lag are already supported by statsmodels (you can do |
Thanks! I'm gonna have to study this.
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I think it could be really useful to do a "rosetta stone" for people coming from different statistical software backgrounds. I've never used stata or SAS for instance, and I (like many of the people involved with developing StatsModels.jl) have an R background, so many of the design decisions make sense to an R user but could be hard to translate...
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@kleinschmidt |
Thanks for the shout out, but to be clear the package only deals with the panel/time series issue of easy leads/lags/diffs creation. (I.e., only point 5 in #1 (comment)) Also, would be good if somebody other than me tried out the package so I know if it works :) |
Hi @kleinschmidt,
I think the Julia ecosystem would benefit from something like this!
If we wanna do serious stats it should be easy to automatically generate all interactions (up order n) etc.
Some things I find particularly useful in my other stats packages outside Julia:
"i.x1" makes x1 into a factor variable in a formula
Suppose x1 takes the values: 1.2, 5, 6.4
reg y x1
: treats x1 as continuous & returns 1 coef (assuming no intercept)reg y i.x1
creates 3 dummies for each level of x1 & returns 3 coefficients(if there is an intercept it randomly drops one level unless the user chooses which level to drop)
i.x1#(c.x2 i.x3)
Interacts all dummies of x1 w/ x2 (continuous)
Interacts all dummies of x1 w/ all dummies of x3
Leads & Lags. Suppose D is at the state-year level.
$D_{t+4}$
L.D
: creates a 1 year lag of DL(4).D
: creates a 4 year lag of DF(4).D
: creates a 4 year lead of D.reg y F(-1 0 1 2).D
estimates: y_t =b_{-1} x_{t-1} + b_{0} x_{t} +b_{1} x_{t+1} +b_{2} x_{t+2}
If Julia is to be "as easy for statistics as R" these features should be in StatsModels.
I'd love to help if I can.
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