# Choosing a numerical programming language for economic research: Julia,

**URL:** <https://discourse.julialang.org/t/choosing-a-numerical-programming-language-for-economic-research-julia/85697>\
**Category:** Community\
**Tags:** blog, blog-post\
**Created:** [August 13, 2022, 7:37am UTC](https://discourse.julialang.org/t/choosing-a-numerical-programming-language-for-economic-research-julia/85697 "2022-08-13T07:37:53Z")\
**Posts on this page:** 1\
**Showing post:** 69

<div class="post-metadata">

**Author:** ![dlakelan](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/dlakelan/32/8491_2.png) [@dlakelan](https://discourse.julialang.org/u/dlakelan)\
**Post date:** [August 15, 2022, 2:11pm UTC](https://discourse.julialang.org/t/choosing-a-numerical-programming-language-for-economic-research-julia/85697/69 "2022-08-15T14:11:43Z")

</div>

> [@y.lin](#):
>
> And in such cases, the best language is generally that which can do most things, not a language that is really good in some things and bad in others. This is where R wins out over Julia

I strongly disagree, but I’m thinking you probably just have a really narrow idea of “most things” for example try coding an agent based model in R, that will be slower than molasses. Or try solving PDEs for each time step in a time series model or write a model with a delay differential equation. Or do an optimization for each week in a 20 year time series… Etc

I worked on a model where after each week of collecting new data we sampled a Bayesian model for a few thousand samples and then ran an optimization the choose a portfolio of bets to maximize an expected outcome at the end of the week. It would have been ridiculously painful to do in R.

---

_[View the full topic](https://discourse.julialang.org/t/choosing-a-numerical-programming-language-for-economic-research-julia/85697)._