Does julia already have a package suited to censored regression models, as in https://en.wikipedia.org/wiki/Tobit_model ?

(PS: InstrumentalVariables? Giuseppe Ragusa’s package is no longer working—and it is not in the repository, either.)

Does julia already have a package suited to censored regression models, as in https://en.wikipedia.org/wiki/Tobit_model ?

(PS: InstrumentalVariables? Giuseppe Ragusa’s package is no longer working—and it is not in the repository, either.)

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I haven’t tried this particular model but I think this should be very possible to express in Turing.jl if you don’t mind writing your model down explicitly. It’s my go-to package in Julia for regression modeling. In R I typically go with RStan.

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It might not be too bad to fix it. Did you check with v0.7? He seems to be refactoring covariancematrices.jl, so maybe he’ll get around to IV after that?)

I would also prefer this approach, but note that for IV there are various caveats one should keep in mind. See eg

```
@article{lopes2014bayesian,
title={Bayesian instrumental variables: priors and likelihoods},
author={Lopes, Hedibert F and Polson, Nicholas G},
journal={Econometric Reviews},
volume={33},
number={1-4},
pages={100--121},
year={2014},
publisher={Taylor \& Francis}
}
```

A function which will do IV, if you frame it as GMM, is here: https://github.com/mcreel/Econometrics.jl/blob/master/src/GMM/gmm.jl

An example of usage, for a Hansen-Singleton type model, is here: https://github.com/mcreel/Econometrics/blob/master/Examples/GMM/portfolio.jl

Another example is here: https://github.com/mcreel/Econometrics/blob/master/Examples/GMM/MeasurementErrorIV.jl

By the way, I would like to make this function accept a dataframe and perhaps a formula, rather that the name of the moments function, but I need to think about how general moment conditions can be specified using formulae.

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It is not so much for me, as for my cookbook. I know there will be students looking for it. so, I need something simple to use. (thanks, michael, but GMM formulations exceed student starters.)

I dropped Giuseppe a note. maybe it will resurrect.

PS: personally, I am not a great fan of IV regressions in many cases in which I have seen them. there are few clear (real-world) cases that are obvious, except in theory. (this is *not at all* to say that they are useless, but they are difficult. yet, many users regard them like heteroskedasticity corrections, something to chalk off a list, and think very little about what their instrumental variables have to mean and what they are doing.)

How about https://github.com/matthieugomez/FixedEffectModels.jl ? I haven’t tried it, but it advertises IV with a Stata like syntax.

`dependent variable ~ exogenous variables + (endogenous variables ~ instrumental variables)`

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