r/econometrics

I've been learning more about econometrics recently, and one concept I'm still trying to fully understand is the difference between correlation and causation.

I understand the basic idea: if two variables are correlated, it means they tend to move together, but that doesn't necessarily mean that a change in one variable causes a change in the other.

The classic example is ice cream sales and drowning deaths. Both increase during summer, but buying ice cream obviously doesn't cause people to drown. A third variable, temperature or season, affects both.

What I'm more interested in is how this works in real economic research, where there are usually many variables changing at the same time.

For example, suppose we find that countries with higher education spending also have higher GDP. How would an economist determine whether higher education spending actually causes higher GDP?

Could someone explain this =, but also go a little beyond the basic "correlation ≠ causation" explanation?

Specifically, how do methods such as control variables, experiments, natural experiments, instrumental variables, or difference-in-differences help economists get closer to identifying a causal relationship?

I'm especially interested in understanding what makes us confident that X caused Y rather than simply being correlated with Y.

Hi everyone, next week starts my econometrics course on the rijksuniversiteit Groningen (the Netherlands) it’s the highest difficulty in europe (no clue if this is a necessary detail).
I still have to buy I laptop but I can’t really find the minimum specs that I need for this kind of course. Anyone got some tips? I would like a HP.

Hi everyone! I'm currently working as a port development analyst and I have been recently assigned to forecast maritime demands. I'm looking for a econometric and/or an advanced statistic textbook where i can learn about different models and be able to descriminate among their application for its application in the transportation field.

Thank u all in advance!!!

I’m about start statistics and econometrics at college, which book do you think is the best to start studying by myself before start taking the classes?

I suppose my teacher will recommend me the book he uses for the classes, I’m asking for a book to start studying before the semester and to prepare the subject. Also open to recommendations to follow the classes.

Mastering ‘Metrics (Joshua Angrist and Pischke)
Basic econometrics (Gujarati)
Introductory econometrics: A Moders Approach (Wooldridge)
Introduction to Econometrics (Stock and Watson)

Hi r/econometrics

I have to decide in a few days if I’ll be studying either BSc Mathematics or BSc Econometrics at the University of Amsterdam (so Econometrics in the Netherlands in undergraduate)

In Econometrics I will hopefully take these courses:
- Macroeconomics, Calculus, Microeconomics, Probability Theory and Statistics I, II and III, Linear Algebra, Advanced Linear Algebra, Multivariate Analysis Econometrics I and II, Life Insurance Mathematics, Statistical Learning, Mathematical Economics I and II, Time Series Analysis and Microeconometrics.
- Minor in Sets and Proofs, Topology, ODE, Markov Chains, Functional Analysis and Measure Theory.
- Maybe a honours (i hope it) that includes either Optimization or Algorithms and Data Structures in Python

It seems like a lot of mathematics but BSc Econometrics still doesn’t grant me immediate access to some interesting master’s that the BSc Maths does.

I also think it’s unfortunate that they don’t have Operations Research like VU and EUR. Although it’s possible as a elective to take Optimization it seems quite interesting.

Also Econometrics itself seems quite hard as a subject and it still has a lot of core maths courses. I hope I don’t regret not having taken more maths because of less Master’s degree possibilities even though it has a lot of maths courses. From year 2 it’s mostly maths I think.

Maths seems very fun because it is more broad it has discrete maths, probability and statistics. If I mainly care about the maths I think a BSc Maths is more robust and it already has the courses like ODE/PDE without the minor. So it would be possible to just take a minor in CS or something else like from the Social Sciences such as “Western Esotericism” which seems very cool.

But quite scared that Maths will be harder. Econometrics seems to build maths a little slower with 2 courses at most each period and Maths can have 3/4 courses (Calculus -> PTS I -> Linear Algebra -> Probability and Statistics II and III -> Multivariate Analysis -> Advanced Linear Algebra)

I hope I’ll end up in something applied anyway such as a master’s Applied Mathematics at TU Delft (which has a bridging programme for BSc Econometrics) or Econometrics at EUR (which has a direct admission for BSc Maths if you take a minor in Econometrics and stats/mathematical finance electives) or Computer Science/AI.

Starting an MSc in economics in September and trying to get a head start. My BSc was economics and business with no econometrics in it at all, only statistics, so I'm fine with the basics and not much past that.

My lecture notes are actually good and I've been working through them alongside Wooldridge's Introductory Econometrics. The problem is the module has changed hands, so I don't have the new professor's notes. All I know is that they'll still be working off Wooldridge, the graduate one (Cross Section and Panel Data), and that book is quite long.

So I'm after something shorter that still uses graduate notation, matrix form, and ideally with exercises to work through.

The topics for the first module are OLS and GLS in matrix form, heteroskedasticity, clustering, Wald/LR/LM, IV, 2SLS, GMM, panel (FE, RE, Hausman, lagged dependent variables), and probit/logit and ordered choice with ML.

I've found Bruce Hansen's Econometrics but haven't actually started on it yet. Is that the one to go with, or is there something else you would recommend?

Hey guys! I am a final year M.Com student and need some help to transition into a econometrics PhD in germany. Now dont come bashing at me I am doing courses in econometrics rn to familiarize myself. This post is solely to find ways to upskill myself in about an year.

I am preparing for the gre, doing a research project in my dads company, using econometrics obviously. I co authored a paper that involves minimum statistics. I am looking for RA position in this field that will start in january or later. Once my gre is done on october I will start a paper that uses DiD and my planned final sem thesis will be using VAR models. The research project Im on currently is a small N case so I couldnt use rigorous econometrics. My cgpa is 9.8/10 and im currently proficient in python.

The main things I need help on are:

1. How to get a paid RA position in think tanks or colleges in India with my profile?

2. If I still wont be eligible for a phd, What should i do differently?

3. Any other suggestions?

I’m working on a small research project around AI assisted investment research.
Instead of building an LLM that simply says BUY / HOLD / SELL, I’m testing an agent that maintains a probability distribution over possible states:
Undervalued
Fairly valued
Overvalued
Deteriorating
The agent then updates those beliefs when new evidence arrives and chooses an action based on both probability and the consequences of being wrong.
I’m particularly interested in three questions:
How should priors be constructed?
Should the prior come from historical comparable companies, sector/base-rate data, factor models, or something else?
How would you evaluate calibration?
If an agent says “60% probability of undervaluation,” what would you consider a meaningful test that this 60% actually means something?
How should asymmetric loss influence the decision?
For example, a 60% chance of being right may still be a bad BUY decision if the downside of being wrong is much larger than the upside.
I’m deliberately not trying to prove that an LLM can generate alpha.
The research question is narrower:
Does explicitly representing uncertainty + asymmetric decision costs produce better decisions than simply choosing the highest-probability state?
I’d especially appreciate criticism from people who have worked with Bayesian models, systematic investing, portfolio construction, or decision theory.
What am I getting wrong?

Over the past several months, I've been building a sports betting research platform because I got tired of jumping between multiple sites just to research one wager.
Rather than trying to create another "AI lock" or sell guaranteed picks, I wanted to build a research tool centered around market data and decision-making.
Current features include:
Closing Line Value (CLV) tracking
Sharp vs. public market movement
Historical odds and betting trends
Team and player statistics
Injury tracking
Research dashboards across multiple sports
Historical matchup data
Market movement visualization
The philosophy is simple: provide as much relevant information as possible in one place so users can make their own decisions instead of blindly following picks.
The homepage is free to explore, while the full research dashboards require a subscription because of the ongoing costs of data infrastructure and APIs.
I'm not here to claim I have a 70% win-rate model. I'm looking for honest feedback from people who enjoy sports analytics and quantitative betting research.
If you had access to a platform like this, what analytics or datasets would you consider "must-have" before paying for it?
I'd appreciate any constructive criticism, whether it's about the features, workflow, or analytics themselves.

Hi all,

I'm reviewing some basic econometric theory and I need help, please, in understanding the apparent tradeoff between adding more (informative) regressors to a model and thus reducing omitted variable bias vs those added regressors being correlated with one another, thereby increasing variance.

Say we run an 'auxiliary' regression among the regressors. E.g. if the original regression were wage = a + b1educ + b2exper + e, regress educ = a + b1exper to check the R^2 of this regression. If it is near zero, then experience is uninformative about education, so we're good. But if R^2_1 -> 1, we have the problem of 'near multicollinearity'. We can still invert X'X, and get a beta hat estimate, but this is problematic because multiple regression is trying to answer (say) "what is the effect of education on wages, holding all other regressors (e.g. exper) fixed." If educ and exper move together, we can't really separately identify the effects of educ / exper on wage.

This shows up by inflating the variance:

Var(b_j|X) = sigma^2 / (sum_i=1^n (x_ij - xbar)^2 * (1 - R^2_j)) where R^2_j is from the auxiliary regression, not the overall R^2. As R^2_j -> 1, Var(b_j|X) increases.

But usually in regressions we include many related things. Experience and education may be strongly related. Or say we add age in, then that may be related. It seems many regressors could be highly related.

Suppose two regressors are highly related, and both affect the dependent variable. Dropping one would lead to omitted variable bias. Keeping both would inflate the variance. It seems there is a tradeoff here, unless I am misunderstanding something. Please help me in understanding this better.

Thank you for your time and comments.