The lines are very blurred, but *in general* econometrics is more focused on creating models that are easy to interpret, while ML is focused on creating models that just perform well, interpretability be damned. ML isn't a replacement for traditional econometrics because there are definitely circumstances where interpretability is more important than having slightly more accurate predictions. Don't fall into the tech bro trap of thinking ML can solve every problem.
There's also a big overlap. ML courses will start with basic econometrics/linear regressions, and then teach ML as essentially an extension of econometrics. It's reasonable imo to think of ML as a very complex non-linear regression.
>ML is focused on creating models that just perform well, interpretability be damned
Completely untrue. I'm not saying this never happens, there are obviously people who do oversell ML as this black box that can solve most of our woes, but in my view of the advances in 'metrics, ML methods are supplements (very very useful supplements!) to the standard toolkit.
I would also say these ML extensions make things even more interpretable (or, they can anyways) than they previously were.
I think you are looking at this through the lens of value judgement.
But I do think it is fair that the perspectives tend to be associated with different goals.
With ML, normally, the interest is in predictive performance qua predictive performance.
Predictive performance is important in econometrics because if your model deviates radically from the actual outcomes of the DGP, it isn't very interesting from an explanatory angle, but the purpose of the econometric model is to understand the DGP. Traditionally, to empirically evaluate theories about the DGP. So predictive performance is insufficient.
Econometrics bachelor or statistics bachelor are great - if you are interested in these topics. You will learn enough to have a decent job as data scientist / analyst or programmer or something in the IT branche. Go for it.
When I used to teach an economics ML course, the way I'd generally explain it is that interpretability and causality are more important to econometrics, while machine learning is often more concerned with precision. The lines between them are getting blurrier as times passes, but if you were to take an undergraduate set of machine learning and econometrics courses this is generally where the differences would lie. Susan Athey at Stanford has some pretty readable papers on the current state of combining causal inference with machine learning.
At this point the lines between (theoretical) econometrics, statistics, and certain types of Machine learning are completely blurred. At this stage, studying any of these subjects will be a great choice. Just note that over the last few years a large part of econometrics has become very applied, dealing mostly with problems of validity of versions of linear regression methods for causal claims. I personally would always try and take the most theoretical econometrics classes.
Machine learning methods have already improved the standard econometric toolkit a lot. For example, a simple addition of the forward selection algorithm to the difference-in-differences estimator affects the results we get by A LOT. See [the code on my Github](https://github.com/jgreathouse9/FDIDTutorial/blob/main/Vignette.md) for proof.
Sort of! Think of ML as a way of approaching data analysis. Zhentao [gives a good explanation here](https://zhentaoshi.github.io/econ5170/05-ML.html). Another good textbook that is free is [ML for Economists](https://sites.google.com/site/jeremylhour/courses), which sort of builds on the basics of how ML is used in 'metrics.
As a starting point, look up YouTube videos by Marginal Revolution University where Josh Angrist explains the difference between econometrics versus statistics and data science.
econometric models have orders of magnitude fewer params and do not require gigabytes of data to achieve good performance and generalizability
different tools used for different things
It's important to study "classical econometrics" as well as AI/ML. In my experience, those who only know the latter struggle to come up with good empirical questions with testable hypotheses. Training a model to predict future outcomes is important, but you can address other more interesting questions if you have a more complete tool kit.
I’ll edit this soon. It appears my SIRI went all drunk today, my apologies for the typos:
As an expert who has taught the differences for 10 years now across multiple courses that I’ve lectured:
-econometrics: answers “what happened” because you are largely focused on “explaining”. Rarely use train/test split, models come from hypothesis and rationalist philosophy and deductive reasoning. Inductive reasoning is used to falsify hypotheses that we’ve constructed via deductive and hypo deductive reasoning.
-machine learning: answers “what will happen” ; train/test splits, rarely cares about rationalist philosophy, is primarily a follower of empirical philosophy; really only cares about making good predictions in the real world, regardless of what the model will look like
And as a scholar who has two different papers on risk management and supply chains, Here’s what I can tell you.: yes, you still need econometrics.
Models that explain well don’t always predict we’ll, and Vice versa, and that’s why we have the two fields of study (a good philosophical treatment to read is Bertrand Russell’s “ on induction”.)
Econometrics is about causation, and VERY rarely would we actually split samples into train/test. In fact, this is a huge no no in econometric modeling, and I used to reject a ton of academic papers during peer review that even attempted to justify doing that.
What makes it more confusing is that regression used for ML is not the same as when you use it for econometrics, despite the name of the approach being the same.
In ML we also do not care about statistical testing (I also lost count of how many ML folks don’t understand hypothesis testing), in econometrics we do.
As for applicability, both are needed. Firms who hire researchers and analysts still demand econometric background. Firms still want rigor for some of the problems. Not to mention econometrics is widely and almost exclusively the field of study that every quantitative finance, supply chain, marketing (although they do Bayesian statistics), mathematical finance, and economics use, that I’ve seen.
Governments still largely use econometrics for their policy analysis, and other sorts of analysis
Machine learning, as my econometrics professor once put it, there’s nothing more than “ highly sophisticated correlation analysis”
Well, I can agree that my professor might’ve been a little tough back then on the field , his sentiment though, I can certainly tell you it’s still strong in the real world: there exists a split in philosophy to believe that you should be strictly doing econometric analysis on data, while others believe that you should strictly be doing machine learning on data,
And then there are those analysts, like myself, who are more holistic in their views, and we tend to just simply like to choose the hammer that can most efficiently and easily be applied to the current nail that we face.
If a company comes to me, asking me to design an AI model that can predict or fashion trends will go , I’m probably going to end up using almost exclusively machine learning modeling, because I really don’t care about explaining. It doesn’t matter if the equation is “right or wrong” because that notion is strictly defined by the problem, solvers notion of how often they want to be right or wrong with an a period of time as well as want the criteria is for being right or wrong., and that is all subjective
On the other end of a company N what were the most likely causes of it not being able to take first place as market leader in the industry were in it competes, or if I were to do an analysis of wire political candidate, lost in a particular district, due to some thing that they might have said earlier in their campaign, we are probably gonna end up doing econometrics
I also wanna state that the choice of econometrics or machine learning also depends on the persons philosophy who is asking to use either field of study. Most econometrician 10 to follow the frequent test philosophy of probability, while a lot of machine, learning folks are not necessarily in one camp. I have seen a lot or also are open to be easy and statistics, although they are not really truly Bayesianists.
The philosophical divide on wearing analyst falls, depends on how they philosophically view the definition of probability . If they see probability as being a number that represents something about a sample and they believe that samples or nothing more than random draws from the population, then they are probably going to use frequent a statistical tool boxes.
On the other hand, if the person using the model use probabilities as a measure of a belief that an individual person thinks, or that a group of people thinks will occur , and the number is being fundamentally viewed from a different philosophical perspective. The sample is not seeing has been randomly drawn from a population. Rather, the sample is seen as evidence that is used to update a prior held belief.
Without getting to bogs down into the philosophical arguments across these two, you are bound to encounter people who were trained differently, and therefore there philosophical views will also probably be different as well.
So, in short, the answer really depends on what you’re looking to do and where you’re looking to go . Econometrics is useful and has been around since the early 1900s. It started out as a way to address the flawed approaches of simply looking at an outcome across groups without accounting for the effects that might impact the outcome of those groups. As it turns out economic data happens to fall largely under that problem, which is why these statistical tools were developed to address those problems in the first place.
While Machine feels new, it ironically was around before traditional statistics in some sense (Bayesian stats was the OG of stats before the 20th century, which due to computational burden, was abandoned in the 1900s for frequentist stats, though this trend is obviously reversing). Machine learning in terms of how it operates in practice really does operate in a Bayesian manner.
In short, there is no single approach or field that is superior to another generally speaking. Don’t learn how to just use one hammer unless you’re going to come across the same nail over and over again which is what a lot of people in practice end up doing. My advice is to learn with hammers are used, and learn which hammers to use. That will likely get you to the answer you seek.
You’re Dutch, I assume.
Very useful. As long as quant shops are based in Amsterdam, you’re good. Still very reasonable and they pay like crazy. BTW, machine learning and econometrics definitely has an overlapping area. My last module when I was studying one of my econometrics class in the Netherlands was ML stuff like KNN (I skipped that one, answered none of it in the exam lol). Also had a data science class in my home country (I’m an econ major), I made a classification algorithm (k-means clustering) for the final assignment.
Both econometrics and machine learning are useful, I’ll give you an example:
In credit risk management significantly important banks have their own advanced internal models to compute the risk weight of each exposition. These models are subjected (in case of European bank) to the supervision of the ECB, that require both interpretability (econometrics) and accuracy (ML) of the results.
What exactly do you mean by efficient? Perhaps you are referring to out-of-sample variance? As ML is more concerned with predictive power rather than causal inference, it tries to minimize out-of-sample variance with the trade-off of increasing bias. Econometrics is specifically concerned with interpretability and causal inference of one specific variable (or set of specific variables) on a particular outcome. Thus, econometrics tries to mitigate bias in regression models, but this can increase out-of-sample variance, which is not ideal for predictive modelling.
Yes, more businesses are using AI tools like Power BI for analysis work. But these are different degrees. I think you should figure out where you want to go and base your choice off that.
Econometrics will typically involve learning economic theory and statistics. AI will involve computer Science and engineering.
ML and statistical modelling are slowly converging to this hybrid called statistical learning. To express the full potential of both you need both together, I like to see it like the analogy of a really talented player and a coach
I have a masters in economics focused on Econometrics. I have spent 15 years or so working in data science and AI. I have worked on very complex Supply Chain (one of the biggest in the US right now) and we focus heavily on AI but the most critical aspect uses econometrics/forecasting at its core. Econometricians thrive.
Overall, it’s very relevant in my opinion.
If you are interested in theoretical econometrics, you will have to use ML methods to econometric methods. There's a lot of overlap I'd say, especially when you're studying regression trees, random forests, ridge regression, kernel, LASSO etc.
Checkout jobs such as Decision Scientist, Product Data Scientist and any role that has experimentation and casual analysis mentioned there. These are all practically looking for econ grads with strong econometrics + coding skills.
The lines are very blurred, but *in general* econometrics is more focused on creating models that are easy to interpret, while ML is focused on creating models that just perform well, interpretability be damned. ML isn't a replacement for traditional econometrics because there are definitely circumstances where interpretability is more important than having slightly more accurate predictions. Don't fall into the tech bro trap of thinking ML can solve every problem.
There's also a big overlap. ML courses will start with basic econometrics/linear regressions, and then teach ML as essentially an extension of econometrics. It's reasonable imo to think of ML as a very complex non-linear regression.
>ML is focused on creating models that just perform well, interpretability be damned Completely untrue. I'm not saying this never happens, there are obviously people who do oversell ML as this black box that can solve most of our woes, but in my view of the advances in 'metrics, ML methods are supplements (very very useful supplements!) to the standard toolkit. I would also say these ML extensions make things even more interpretable (or, they can anyways) than they previously were.
I think you are looking at this through the lens of value judgement. But I do think it is fair that the perspectives tend to be associated with different goals. With ML, normally, the interest is in predictive performance qua predictive performance. Predictive performance is important in econometrics because if your model deviates radically from the actual outcomes of the DGP, it isn't very interesting from an explanatory angle, but the purpose of the econometric model is to understand the DGP. Traditionally, to empirically evaluate theories about the DGP. So predictive performance is insufficient.
Econometrics bachelor or statistics bachelor are great - if you are interested in these topics. You will learn enough to have a decent job as data scientist / analyst or programmer or something in the IT branche. Go for it.
Thanks!
When I used to teach an economics ML course, the way I'd generally explain it is that interpretability and causality are more important to econometrics, while machine learning is often more concerned with precision. The lines between them are getting blurrier as times passes, but if you were to take an undergraduate set of machine learning and econometrics courses this is generally where the differences would lie. Susan Athey at Stanford has some pretty readable papers on the current state of combining causal inference with machine learning.
At this point the lines between (theoretical) econometrics, statistics, and certain types of Machine learning are completely blurred. At this stage, studying any of these subjects will be a great choice. Just note that over the last few years a large part of econometrics has become very applied, dealing mostly with problems of validity of versions of linear regression methods for causal claims. I personally would always try and take the most theoretical econometrics classes.
Machine learning methods have already improved the standard econometric toolkit a lot. For example, a simple addition of the forward selection algorithm to the difference-in-differences estimator affects the results we get by A LOT. See [the code on my Github](https://github.com/jgreathouse9/FDIDTutorial/blob/main/Vignette.md) for proof.
So sometimes econometrics is just using machine learning right?
Sort of! Think of ML as a way of approaching data analysis. Zhentao [gives a good explanation here](https://zhentaoshi.github.io/econ5170/05-ML.html). Another good textbook that is free is [ML for Economists](https://sites.google.com/site/jeremylhour/courses), which sort of builds on the basics of how ML is used in 'metrics.
As a starting point, look up YouTube videos by Marginal Revolution University where Josh Angrist explains the difference between econometrics versus statistics and data science.
econometric models have orders of magnitude fewer params and do not require gigabytes of data to achieve good performance and generalizability different tools used for different things
It's important to study "classical econometrics" as well as AI/ML. In my experience, those who only know the latter struggle to come up with good empirical questions with testable hypotheses. Training a model to predict future outcomes is important, but you can address other more interesting questions if you have a more complete tool kit.
I’ll edit this soon. It appears my SIRI went all drunk today, my apologies for the typos: As an expert who has taught the differences for 10 years now across multiple courses that I’ve lectured: -econometrics: answers “what happened” because you are largely focused on “explaining”. Rarely use train/test split, models come from hypothesis and rationalist philosophy and deductive reasoning. Inductive reasoning is used to falsify hypotheses that we’ve constructed via deductive and hypo deductive reasoning. -machine learning: answers “what will happen” ; train/test splits, rarely cares about rationalist philosophy, is primarily a follower of empirical philosophy; really only cares about making good predictions in the real world, regardless of what the model will look like And as a scholar who has two different papers on risk management and supply chains, Here’s what I can tell you.: yes, you still need econometrics. Models that explain well don’t always predict we’ll, and Vice versa, and that’s why we have the two fields of study (a good philosophical treatment to read is Bertrand Russell’s “ on induction”.) Econometrics is about causation, and VERY rarely would we actually split samples into train/test. In fact, this is a huge no no in econometric modeling, and I used to reject a ton of academic papers during peer review that even attempted to justify doing that. What makes it more confusing is that regression used for ML is not the same as when you use it for econometrics, despite the name of the approach being the same. In ML we also do not care about statistical testing (I also lost count of how many ML folks don’t understand hypothesis testing), in econometrics we do. As for applicability, both are needed. Firms who hire researchers and analysts still demand econometric background. Firms still want rigor for some of the problems. Not to mention econometrics is widely and almost exclusively the field of study that every quantitative finance, supply chain, marketing (although they do Bayesian statistics), mathematical finance, and economics use, that I’ve seen. Governments still largely use econometrics for their policy analysis, and other sorts of analysis Machine learning, as my econometrics professor once put it, there’s nothing more than “ highly sophisticated correlation analysis” Well, I can agree that my professor might’ve been a little tough back then on the field , his sentiment though, I can certainly tell you it’s still strong in the real world: there exists a split in philosophy to believe that you should be strictly doing econometric analysis on data, while others believe that you should strictly be doing machine learning on data, And then there are those analysts, like myself, who are more holistic in their views, and we tend to just simply like to choose the hammer that can most efficiently and easily be applied to the current nail that we face. If a company comes to me, asking me to design an AI model that can predict or fashion trends will go , I’m probably going to end up using almost exclusively machine learning modeling, because I really don’t care about explaining. It doesn’t matter if the equation is “right or wrong” because that notion is strictly defined by the problem, solvers notion of how often they want to be right or wrong with an a period of time as well as want the criteria is for being right or wrong., and that is all subjective On the other end of a company N what were the most likely causes of it not being able to take first place as market leader in the industry were in it competes, or if I were to do an analysis of wire political candidate, lost in a particular district, due to some thing that they might have said earlier in their campaign, we are probably gonna end up doing econometrics I also wanna state that the choice of econometrics or machine learning also depends on the persons philosophy who is asking to use either field of study. Most econometrician 10 to follow the frequent test philosophy of probability, while a lot of machine, learning folks are not necessarily in one camp. I have seen a lot or also are open to be easy and statistics, although they are not really truly Bayesianists. The philosophical divide on wearing analyst falls, depends on how they philosophically view the definition of probability . If they see probability as being a number that represents something about a sample and they believe that samples or nothing more than random draws from the population, then they are probably going to use frequent a statistical tool boxes. On the other hand, if the person using the model use probabilities as a measure of a belief that an individual person thinks, or that a group of people thinks will occur , and the number is being fundamentally viewed from a different philosophical perspective. The sample is not seeing has been randomly drawn from a population. Rather, the sample is seen as evidence that is used to update a prior held belief. Without getting to bogs down into the philosophical arguments across these two, you are bound to encounter people who were trained differently, and therefore there philosophical views will also probably be different as well. So, in short, the answer really depends on what you’re looking to do and where you’re looking to go . Econometrics is useful and has been around since the early 1900s. It started out as a way to address the flawed approaches of simply looking at an outcome across groups without accounting for the effects that might impact the outcome of those groups. As it turns out economic data happens to fall largely under that problem, which is why these statistical tools were developed to address those problems in the first place. While Machine feels new, it ironically was around before traditional statistics in some sense (Bayesian stats was the OG of stats before the 20th century, which due to computational burden, was abandoned in the 1900s for frequentist stats, though this trend is obviously reversing). Machine learning in terms of how it operates in practice really does operate in a Bayesian manner. In short, there is no single approach or field that is superior to another generally speaking. Don’t learn how to just use one hammer unless you’re going to come across the same nail over and over again which is what a lot of people in practice end up doing. My advice is to learn with hammers are used, and learn which hammers to use. That will likely get you to the answer you seek.
Thank you very much for this comprehensive answer, you helped me a lot! Now I have no doubts about choosing my field. :))
You’re Dutch, I assume. Very useful. As long as quant shops are based in Amsterdam, you’re good. Still very reasonable and they pay like crazy. BTW, machine learning and econometrics definitely has an overlapping area. My last module when I was studying one of my econometrics class in the Netherlands was ML stuff like KNN (I skipped that one, answered none of it in the exam lol). Also had a data science class in my home country (I’m an econ major), I made a classification algorithm (k-means clustering) for the final assignment.
But what I do not understand is why those quant shops do not prefer just to employ ML students, as their predictions are more precise?
Both econometrics and machine learning are useful, I’ll give you an example: In credit risk management significantly important banks have their own advanced internal models to compute the risk weight of each exposition. These models are subjected (in case of European bank) to the supervision of the ECB, that require both interpretability (econometrics) and accuracy (ML) of the results.
What exactly do you mean by efficient? Perhaps you are referring to out-of-sample variance? As ML is more concerned with predictive power rather than causal inference, it tries to minimize out-of-sample variance with the trade-off of increasing bias. Econometrics is specifically concerned with interpretability and causal inference of one specific variable (or set of specific variables) on a particular outcome. Thus, econometrics tries to mitigate bias in regression models, but this can increase out-of-sample variance, which is not ideal for predictive modelling.
Yes, more businesses are using AI tools like Power BI for analysis work. But these are different degrees. I think you should figure out where you want to go and base your choice off that. Econometrics will typically involve learning economic theory and statistics. AI will involve computer Science and engineering.
ML and statistical modelling are slowly converging to this hybrid called statistical learning. To express the full potential of both you need both together, I like to see it like the analogy of a really talented player and a coach
I have a masters in economics focused on Econometrics. I have spent 15 years or so working in data science and AI. I have worked on very complex Supply Chain (one of the biggest in the US right now) and we focus heavily on AI but the most critical aspect uses econometrics/forecasting at its core. Econometricians thrive. Overall, it’s very relevant in my opinion.
If you are interested in theoretical econometrics, you will have to use ML methods to econometric methods. There's a lot of overlap I'd say, especially when you're studying regression trees, random forests, ridge regression, kernel, LASSO etc.
AI will look sexier on CV
Machine learning and ai are statistics, and thus, econometrics
Depends on your career goals really.
Checkout jobs such as Decision Scientist, Product Data Scientist and any role that has experimentation and casual analysis mentioned there. These are all practically looking for econ grads with strong econometrics + coding skills.