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Economics Nobel laureate Paul Romer is a Python programming convert

170 points by okket 8y ago

TorKlingberg 8y ago
I'm surprised there seems to be have been no HN discussion on this years Economics prize. Or are my HS search skills lacking?
- daxat_staglatz 8y ago
  I haven't found any, either. This is a shame because economics is already a computational discipline and increasingly so. It can be of interest to the HN crowd. There is an awful lot of programming done by people that are not professional programmers. As an economist myself, I think code have become so central to our profession that we can no longer afford to ignore software engineering best practices. Conversely, there may be quite a few topics in computational economics that interest programmers. For instance, an awful lot of macroeconomics - including to an extent Romer's sub-field, endogenous growth theory - is essentially programming in Dynare [0] and doing computer simulations. Nordhaus got the prize for developing (quite basic, actually) simulation models that integrate the economic systems and environmental systems. See for instance there [1]. Micro-simulation is of course a big area where programming and economics meet - you can follow development on github [2, 3]. This is code that has an enormous impact on society, as it is used as a basis to evaluate the effects of policies. Econometrics, aka statistics for economics, is also a big area where programmers could contribute to economic research. There is a big push to adapt ML techniques to solve economists' problems, eg Athey's research [4]. [0] http://www.dynare.org/ http://www.dynare.org/ [1] https://sites.google.com/site/williamdnordhaus/dice-rice https://sites.google.com/site/williamdnordhaus/dice-rice [2] https://github.com/openfisca https://github.com/openfisca [3] https://github.com/InseeFr/Destinie-2 https://github.com/InseeFr/Destinie-2 [4] https://arxiv.org/abs/1510.04342 https://arxiv.org/abs/1510.04342
  - nl 8y ago
    This is a great comment. I believe the NY Fed has open source economic models in Julia, too.
    - daxat_staglatz 8y ago
      Indeed : https://github.com/FRBNY-DSGE/DSGE.jl https://github.com/FRBNY-DSGE/DSGE.jl
- dhimes 8y ago
  Maybe part of the problem is that unless you delve beyond layman level (and maybe even if you do?) economics is close-coupled to political views, and those start wars- so we tend to steer clear on this board.
  - cirgue 8y ago
    The dismal science has earned the name many times over: no one wants to read a long-winded epistle on the relationship between bond yields and non-farm employment in Alabama between 1993 and 1998, and that's where a lot of the interesting parts of econ lie.
- rchaud 8y ago
  Likely because there aren't too many Economics majors on this forum (I am, BTW). From what I've seen on previous HN posts on economics topics, the discussion veers towards "Economics isn't a real science" variety, with the majority of the posts going back on forth on that comment, rather than the actual topic of the article.
daxat_staglatz 8y ago
People interested by the use of Python in economic research should check out QuantEcon [0], a project to develop tools and courses for computational economics [1] in Python and Julia [0] https://quantecon.org/ https://quantecon.org/ [1] A fancy expression to say "using programming to solve economic models"
- FabHK 8y ago
  Yeah, and Economics Nobel laureate Thomas Sargent is a Julia programming convert (he even gave the JuliaCon 2016 keynote, video embedded below:) https://juliacomputing.com/case-studies/thomas-sargent.html https://juliacomputing.com/case-studies/thomas-sargent.html EDIT to add: I should say that Thomas Sargent is one of the founders of QuantEcon (cited by OP), that's why I'm bringing this up here. Another resource on Julia in Computational Economics: https://juliaeconomics.com https://juliaeconomics.com
- drej 8y ago
  (Cofounded by another Nobel laureate.)
- oktavist 8y ago
  > A fancy expression to say "using programming to solve economic models" That fancy expression existed well before the birth of programming.
21 8y ago
> he tried to use Mathematica to share one of his studies in a way that anyone could explore every detail of his data and methods. It didn’t work. He says that Mathematica’s owner, Wolfram Research, made it too difficult to share his work in a way that didn’t require other people to use the proprietary software, too Sometimes I wonder where Mathematica would be if it were open sourced lets say in 2010. It had such a head start over Python. Interestingly, Mathematica interop with Python is basically non-existent today, even worse than it was in the past. Short term moat building destroying long term viability.
- hellogoodbyeeee 8y ago
  I loved using Mathematica in college. It was the first programming language I ever really used to make some cool things. I wish it was more accessible. I think one of the requirements of my dream job would be a work place that uses Mathematica. It's really fun reading through "code golf" challenges where other languages take 10-15 lines for something that Mathematica has a built in for.
  - jabl 8y ago
    I used Mathematica a lot as an undergrad, but more or less exclusively for doing symbolic math, mostly tricky or tedious integrals. Much later I realized that it's actually a more or less full-fledged programming language as well, but, I never got into that.
  - SonOfLilit 8y ago
    Did you just say "code golf" and Mathematica in the same sentence? Then you will love these. I own like 10 copies, which I give to people for their birthdays: http://adereth.github.io/blog/2017/11/02/playing-with-wolfram-playing-cards/ http://adereth.github.io/blog/2017/11/02/playing-with-wolfra...
    - owenversteeg 8y ago
      Oh, those are quite cool. I'll have to get a pack sometime. I also think the Executed Today deck looks pretty cool (totally different card subject)
varjag 8y ago
But Python 2 or Python 3?
- waivek 8y ago
  The Turing complete one.
mxuribe 8y ago
Beyond the cool python angle, I find the following statement quite interesting: "...James Somers argued that Jupyter notebooks may replace the traditional research paper typically shared as a PDF..." I've only been exposed to jupyter notebook references here and there...but i guess i should become a little more familiar with them.
- Rainymood 8y ago
  I was recently asked by a company to do a take-home assignment, some data analysis stuff. I opted for a jupyter notebook as it sequentially allows you to both explore the data in an interactive manner while still programming in Python. It was great. I love notebooks.
- AlanSE 8y ago
  While I think this is a good thing, an additional requirement is needed. The published notebook should be sequentially executed by an automated tool. Users can execute commands out of order and have artifacts from deleted commands. Without verification, you can be publishing notebooks with bugs in them that you don't see until you re-execute them.
  - dunpeal 8y ago
    Jupyter notebooks are ultimately just a text file, which can be version controlled, verified (via the VCS or independent hashing), and if necessary - digitally signed in a cryptographically secure manner. Given that Git is already secure, I'd say all the researcher needs to know is basic usage of Git. Version controlling the notebook satisfies all the requirements you mentioned: prevents accidental distortion, is verifiable, restorable, etc.
    - ashtonbaker 8y ago
      It's not just tracability and security that's the issue, though. It's an issue of reproducibility. When I give you a notebook, how do you 'run' it to verify my calculations? You can't just press "run", because I might have executed the cells out of order, or deleted cells which later cells depended on. Version control doesn't solve this problem. Good practice might solve it, but it turns out that researchers are stunningly bad at following a strict top-to-bottom execution of notebooks. And who can blame them? The non-linearity of notebooks is one of their features. But when it comes to reproducibility and just the basic mechanics of sharing code, we definitely need a better solution.
lellotope 8y ago
Am I missing something or are parts of this article really distorted? For example, this seems to set up most of the article: "Economics involves a lot of math and statistics. The most commonly used tools to crunch numbers are the spreadsheet software Microsoft Excel and programming languages Stata and Mathematica." Is this really true? Mathematica and Stata seem like established but niche products to me at this point. I wouldn't say either of them are "the most commonly used tools to crunch numbers." If you asked me to predict what a quantitative economist would be using, it would be Python, followed by R, and maybe followed by Java or C, or something like that. This was an interesting article in the sense I like learning these sorts of things about people, but the premise seemed off to me. But I'm not an economist so maybe this is something about economics per se.
- wjossey 8y ago
  Nate Silver shared that a vast majority of the latest five thirty eight model for 2018 is constructed in Stata. Just an anecdote, but fairly practical example of something recently written that’s using that technology.
- thousandautumns 8y ago
  Depends on the field. In the social sciences, Mathematica and Stata are probably the most widely used tools. Might be that way for economics too, but I'm not sure.
  - emgee_1 8y ago
    I believe they prefer window like interfaces instead of scripting or programming your models. Stata used to be only command line driven and probably still has that functionality ( I used it in 1997-2007) ; then again as a mathematician I prefer R. Or any computer algebra system.
- prepend 8y ago
  “If you asked me to predict what a quantitative economist would be using, it would be Python, followed by R, and maybe followed by Java or C, or something like that.” I too was looking for good data. It’s hard to measure this as practical application doesn’t line up with publication. I was surprised when I started working in health 10 years ago that the predominant tool was Excel, and then SAS. Even 5 years ago in health grad school, they only taught SAS. This is slowly changing to R and python, but general data analysis skills is less than basic engineering stuff 20 years ago.
tragomaskhalos 8y ago
I use Python and it's OK, but I find a lot of the breathless hype around it as a language a little baffling as there are more than an average number of stupidities in there. However this piece is really around exploiting the amazing infrastructure that has built up around Python that empowers mathematical and statistical research, which is fair enough.
- dunpeal 8y ago
  What are these "stupidities" that bother you? Personally, I use Python a lot for anything related to data science. No language is perfect, but I'd say Python has a lot less deficiencies and warts than most other mainstream languages, and it's extremely well suited for tasks in data science and related fields such as machine learning. The main issue with Python is that its default platform (CPython) isn't very efficient. That's not a problem in the language itself; in fact, it's partly caused by all the benefits of a high-level language: you simply don't have the same facilities to optimize your resource consumption as you do in, say, Rust. The upside is of course that the code is far more concise and readable.
  - Insanity 8y ago
    Whilst that is true, for data science related things I end up using Pandas (and Numpy) often which are fast. So most of the heavy lifting is not done by python itself.
    - dunpeal 8y ago
      Yes, that's the case for most data scientists, and often for users in general. You can get pretty good performance levels from Python by delegating to C correctly. Even end users tend to do that because the data science stack of Python (numpy and the many libraries using it) spend almost all of their runtime in C routines. If you are using Numpy correctly (even with little understanding of Python itself and its C-API) then virtually all your hot loops are executed in optimal C code.
    - mynewtb 8y ago
notfromhere 8y ago
There's probably a lot of gnashing of teeth over this article at WRI/Mathematica.
- copperx 8y ago
  As long as it makes Stephen Wolfram angry, I'm happy.
  - notfromhere 8y ago
    If you're a former employee, you know he is.
agumonkey 8y ago
Next one will be a Julia convert.
- okket 8y ago
  According to this comment further down there already is one: https://news.ycombinator.com/item?id=18174769 https://news.ycombinator.com/item?id=18174769
patagonia 8y ago
Someone should point him toward SageMath. The article’s title is misleading. Doesn’t sound like he is a Python convert. Sounds like he is an open source tool convert.
nycticorax 8y ago
Does anyone have any insight into why Paul Romer is a Julia skeptic? In the blog post https://paulromer.net/jupyter-mathematica-and-the-future-of-the-research-paper/ https://paulromer.net/jupyter-mathematica-and-the-future-of-... he says: "Which reminds me. If you are a Julia enthusiast, how do you suppose the investors in this new language plan to make their big score?" Also there's this: https://twitter.com/paulmromer/status/985507319114096640 https://twitter.com/paulmromer/status/985507319114096640 It doesn't seem like he's followed up on his threat/promise to write a blog post about why he's not a Julia enthusiast, however.

170 points by okket 8y ago

I'm surprised there seems to be have been no HN discussion on this years Economics prize. Or are my HS search skills lacking?

daxat_staglatz 8y ago
I haven't found any, either. This is a shame because economics is already a computational discipline and increasingly so. It can be of interest to the HN crowd. There is an awful lot of programming done by people that are not professional programmers. As an economist myself, I think code have become so central to our profession that we can no longer afford to ignore software engineering best practices. Conversely, there may be quite a few topics in computational economics that interest programmers. For instance, an awful lot of macroeconomics - including to an extent Romer's sub-field, endogenous growth theory - is essentially programming in Dynare [0] and doing computer simulations. Nordhaus got the prize for developing (quite basic, actually) simulation models that integrate the economic systems and environmental systems. See for instance there [1]. Micro-simulation is of course a big area where programming and economics meet - you can follow development on github [2, 3]. This is code that has an enormous impact on society, as it is used as a basis to evaluate the effects of policies. Econometrics, aka statistics for economics, is also a big area where programmers could contribute to economic research. There is a big push to adapt ML techniques to solve economists' problems, eg Athey's research [4]. [0] http://www.dynare.org/ http://www.dynare.org/ [1] https://sites.google.com/site/williamdnordhaus/dice-rice https://sites.google.com/site/williamdnordhaus/dice-rice [2] https://github.com/openfisca https://github.com/openfisca [3] https://github.com/InseeFr/Destinie-2 https://github.com/InseeFr/Destinie-2 [4] https://arxiv.org/abs/1510.04342 https://arxiv.org/abs/1510.04342
- nl 8y ago
  This is a great comment. I believe the NY Fed has open source economic models in Julia, too.
  - daxat_staglatz 8y ago
    Indeed : https://github.com/FRBNY-DSGE/DSGE.jl https://github.com/FRBNY-DSGE/DSGE.jl
dhimes 8y ago
Maybe part of the problem is that unless you delve beyond layman level (and maybe even if you do?) economics is close-coupled to political views, and those start wars- so we tend to steer clear on this board.
- cirgue 8y ago
  The dismal science has earned the name many times over: no one wants to read a long-winded epistle on the relationship between bond yields and non-farm employment in Alabama between 1993 and 1998, and that's where a lot of the interesting parts of econ lie.
rchaud 8y ago
Likely because there aren't too many Economics majors on this forum (I am, BTW). From what I've seen on previous HN posts on economics topics, the discussion veers towards "Economics isn't a real science" variety, with the majority of the posts going back on forth on that comment, rather than the actual topic of the article.

daxat_staglatz 8y ago

People interested by the use of Python in economic research should check out QuantEcon [0], a project to develop tools and courses for computational economics [1] in Python and Julia [0] https://quantecon.org/ https://quantecon.org/ [1] A fancy expression to say "using programming to solve economic models"

FabHK 8y ago
Yeah, and Economics Nobel laureate Thomas Sargent is a Julia programming convert (he even gave the JuliaCon 2016 keynote, video embedded below:) https://juliacomputing.com/case-studies/thomas-sargent.html https://juliacomputing.com/case-studies/thomas-sargent.html EDIT to add: I should say that Thomas Sargent is one of the founders of QuantEcon (cited by OP), that's why I'm bringing this up here. Another resource on Julia in Computational Economics: https://juliaeconomics.com https://juliaeconomics.com
drej 8y ago
(Cofounded by another Nobel laureate.)
oktavist 8y ago
> A fancy expression to say "using programming to solve economic models" That fancy expression existed well before the birth of programming.

21 8y ago

> he tried to use Mathematica to share one of his studies in a way that anyone could explore every detail of his data and methods. It didn’t work. He says that Mathematica’s owner, Wolfram Research, made it too difficult to share his work in a way that didn’t require other people to use the proprietary software, too Sometimes I wonder where Mathematica would be if it were open sourced lets say in 2010. It had such a head start over Python. Interestingly, Mathematica interop with Python is basically non-existent today, even worse than it was in the past. Short term moat building destroying long term viability.

hellogoodbyeeee 8y ago
I loved using Mathematica in college. It was the first programming language I ever really used to make some cool things. I wish it was more accessible. I think one of the requirements of my dream job would be a work place that uses Mathematica. It's really fun reading through "code golf" challenges where other languages take 10-15 lines for something that Mathematica has a built in for.
- jabl 8y ago
  I used Mathematica a lot as an undergrad, but more or less exclusively for doing symbolic math, mostly tricky or tedious integrals. Much later I realized that it's actually a more or less full-fledged programming language as well, but, I never got into that.
- SonOfLilit 8y ago
  Did you just say "code golf" and Mathematica in the same sentence? Then you will love these. I own like 10 copies, which I give to people for their birthdays: http://adereth.github.io/blog/2017/11/02/playing-with-wolfram-playing-cards/ http://adereth.github.io/blog/2017/11/02/playing-with-wolfra...
  - owenversteeg 8y ago
    Oh, those are quite cool. I'll have to get a pack sometime. I also think the Executed Today deck looks pretty cool (totally different card subject)

varjag 8y ago

But Python 2 or Python 3?

waivek 8y ago
The Turing complete one.

mxuribe 8y ago

Beyond the cool python angle, I find the following statement quite interesting: "...James Somers argued that Jupyter notebooks may replace the traditional research paper typically shared as a PDF..." I've only been exposed to jupyter notebook references here and there...but i guess i should become a little more familiar with them.

Rainymood 8y ago
I was recently asked by a company to do a take-home assignment, some data analysis stuff. I opted for a jupyter notebook as it sequentially allows you to both explore the data in an interactive manner while still programming in Python. It was great. I love notebooks.
AlanSE 8y ago
While I think this is a good thing, an additional requirement is needed. The published notebook should be sequentially executed by an automated tool. Users can execute commands out of order and have artifacts from deleted commands. Without verification, you can be publishing notebooks with bugs in them that you don't see until you re-execute them.
- dunpeal 8y ago
  Jupyter notebooks are ultimately just a text file, which can be version controlled, verified (via the VCS or independent hashing), and if necessary - digitally signed in a cryptographically secure manner. Given that Git is already secure, I'd say all the researcher needs to know is basic usage of Git. Version controlling the notebook satisfies all the requirements you mentioned: prevents accidental distortion, is verifiable, restorable, etc.
  - ashtonbaker 8y ago
    It's not just tracability and security that's the issue, though. It's an issue of reproducibility. When I give you a notebook, how do you 'run' it to verify my calculations? You can't just press "run", because I might have executed the cells out of order, or deleted cells which later cells depended on. Version control doesn't solve this problem. Good practice might solve it, but it turns out that researchers are stunningly bad at following a strict top-to-bottom execution of notebooks. And who can blame them? The non-linearity of notebooks is one of their features. But when it comes to reproducibility and just the basic mechanics of sharing code, we definitely need a better solution.

lellotope 8y ago

Am I missing something or are parts of this article really distorted? For example, this seems to set up most of the article: "Economics involves a lot of math and statistics. The most commonly used tools to crunch numbers are the spreadsheet software Microsoft Excel and programming languages Stata and Mathematica." Is this really true? Mathematica and Stata seem like established but niche products to me at this point. I wouldn't say either of them are "the most commonly used tools to crunch numbers." If you asked me to predict what a quantitative economist would be using, it would be Python, followed by R, and maybe followed by Java or C, or something like that. This was an interesting article in the sense I like learning these sorts of things about people, but the premise seemed off to me. But I'm not an economist so maybe this is something about economics per se.

wjossey 8y ago
Nate Silver shared that a vast majority of the latest five thirty eight model for 2018 is constructed in Stata. Just an anecdote, but fairly practical example of something recently written that’s using that technology.
thousandautumns 8y ago
Depends on the field. In the social sciences, Mathematica and Stata are probably the most widely used tools. Might be that way for economics too, but I'm not sure.
- emgee_1 8y ago
  I believe they prefer window like interfaces instead of scripting or programming your models. Stata used to be only command line driven and probably still has that functionality ( I used it in 1997-2007) ; then again as a mathematician I prefer R. Or any computer algebra system.
prepend 8y ago
“If you asked me to predict what a quantitative economist would be using, it would be Python, followed by R, and maybe followed by Java or C, or something like that.” I too was looking for good data. It’s hard to measure this as practical application doesn’t line up with publication. I was surprised when I started working in health 10 years ago that the predominant tool was Excel, and then SAS. Even 5 years ago in health grad school, they only taught SAS. This is slowly changing to R and python, but general data analysis skills is less than basic engineering stuff 20 years ago.

tragomaskhalos 8y ago

I use Python and it's OK, but I find a lot of the breathless hype around it as a language a little baffling as there are more than an average number of stupidities in there. However this piece is really around exploiting the amazing infrastructure that has built up around Python that empowers mathematical and statistical research, which is fair enough.

dunpeal 8y ago
What are these "stupidities" that bother you? Personally, I use Python a lot for anything related to data science. No language is perfect, but I'd say Python has a lot less deficiencies and warts than most other mainstream languages, and it's extremely well suited for tasks in data science and related fields such as machine learning. The main issue with Python is that its default platform (CPython) isn't very efficient. That's not a problem in the language itself; in fact, it's partly caused by all the benefits of a high-level language: you simply don't have the same facilities to optimize your resource consumption as you do in, say, Rust. The upside is of course that the code is far more concise and readable.
- Insanity 8y ago
  Whilst that is true, for data science related things I end up using Pandas (and Numpy) often which are fast. So most of the heavy lifting is not done by python itself.
  - dunpeal 8y ago
    Yes, that's the case for most data scientists, and often for users in general. You can get pretty good performance levels from Python by delegating to C correctly. Even end users tend to do that because the data science stack of Python (numpy and the many libraries using it) spend almost all of their runtime in C routines. If you are using Numpy correctly (even with little understanding of Python itself and its C-API) then virtually all your hot loops are executed in optimal C code.
  - mynewtb 8y ago

notfromhere 8y ago

There's probably a lot of gnashing of teeth over this article at WRI/Mathematica.

copperx 8y ago
As long as it makes Stephen Wolfram angry, I'm happy.
- notfromhere 8y ago
  If you're a former employee, you know he is.

agumonkey 8y ago

Next one will be a Julia convert.

okket 8y ago
According to this comment further down there already is one: https://news.ycombinator.com/item?id=18174769 https://news.ycombinator.com/item?id=18174769

patagonia 8y ago

Someone should point him toward SageMath. The article’s title is misleading. Doesn’t sound like he is a Python convert. Sounds like he is an open source tool convert.

nycticorax 8y ago

Does anyone have any insight into why Paul Romer is a Julia skeptic? In the blog post https://paulromer.net/jupyter-mathematica-and-the-future-of-the-research-paper/ https://paulromer.net/jupyter-mathematica-and-the-future-of-... he says: "Which reminds me. If you are a Julia enthusiast, how do you suppose the investors in this new language plan to make their big score?" Also there's this: https://twitter.com/paulmromer/status/985507319114096640 https://twitter.com/paulmromer/status/985507319114096640 It doesn't seem like he's followed up on his threat/promise to write a blog post about why he's not a Julia enthusiast, however.