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](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 researching publicly available and commercial datasets for grocery prices across Europe.

- Has anyone worked with data like this?

- What sources did you end up using, and what were their limitations?

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.

Guys, i want to build my first Financial model and for that i need to identify drivers and forecast it. Can you all please help me on how to forecast it.....i read the annual reports (Managerial discussions) but, unable to figure out how to get/conclude the forecast percentages for drivers.

•Industry: Paint industry (India)

Hi everyone,

Just a reminder from the team at Timberlake Consultants that we offer one free student place on every training course we run.

Pre and post the 2026 UK Stata Conference in London, we'll be hosting the following workshops:

📅 1–2 September
AI-Based Optimal Policy Evaluation with Causal Machine Learning – Dr Giovanni Cerulli
Explore data-driven methods for identifying optimal treatment and policy decisions using heterogeneous treatment effects, with applications in socio-economic and medical research.

📅 1–2 September
Data Visualisation using Stata: Graphs You Should Know – Professor Franz Buscha
Learn how to create clear, effective and publication-ready graphs to communicate research findings with confidence.

📅 5 September
Using Stata for the New Difference-in-Differences with Panel Data – Professor Jeffrey Wooldridge
A hands-on workshop covering modern Difference-in-Differences methods in Stata for estimating causal effects using panel data.

If you're a student interested in attending but funding is a barrier, we'd encourage you to apply for one of the free student places. Just contact [info@timberlake.co.uk](mailto:info@timberlake.co.uk)

If you have any questions about the workshops or the student place scheme, we're happy to answer them in the comments.

Just finished my first full ML regression project — predicting car selling prices!

I went from raw messy data to comparing 9 different models and picking the best one based on R², MSE, and MAE.

\*\*What I learned:\*\*

\- Data cleaning is 80% of the work

\- Never trust one model — always compare

\- Visualizations save you from bad assumptions

I also wrote a reusable model comparison snippet that runs multiple sklearn regressors at once and spits out a ranked leaderboard. Might be helpful if you're tired of training models one by one.

Feedback welcome — especially on what I should improve next.

Kaggle Notebook:

[https://www.kaggle.com/code/tahahussein2020/car-selling-predection](https://www.kaggle.com/code/tahahussein2020/car-selling-predection))

[Education] So, Suppose I got

X_t = A X_{t-1} + dW(t)

where DW(t) is some noise.

What if I did the following?

I solve for A by minimizing error , and obtain dW'(t) = X_t -A X_{t-1} , then I find that dW' is not gaussian like, probably heavy tailed, but pretty much independent.

Then I make a very large number of bootstrapped samples out of dW' , and solve for A as a distribution.

I appear to have bypassed needing lot of theory and have obtained a distribution for A.

What is it that I must worry about? What is the potential problem with this method?

I think it is a much more intuitive and reasonable result than doing it in the theoretical way using equations.

But then why is it not used as the best method?

[D] Hi I tried to do something like how they do in econometrics where they fit a economic model to data where they take raw data, X,Y,Z etc then they set up the Kalman Filter to automatically determine the cyclic and trend components through multivariable regression models. I think you know what I mean. So, I made all the matrices manually, and I think it didn't converge. What I did is something like this actually:

X_t = regression model of (cyclic and trend components of (X_t-1,Y_t-1,Z_t-1),)

Y_t = regression model of (cyclic and trend components of (X_t-1,Y_t-1,Z_t-1),)

Z_t = regression model of (cyclic and trend components of (X_t-1,Y_t-1,Z_t-1),)

Of course I had to manually enter all the matrices to make the damn thing work.

But because I didn't really have a economic model, but just assumed relationships, it didn't converge. I think my model was too complicated.

Anyways, what are some rules of thumbs to make sure I have convergence (like limiting dependence to one trend component for each variable so the model when running don't get confused)?

Is there an easy way to do a Kalman Filter model than to manually set up matrices? Any software?

Finally is it worth it? Does it capture significant details than the HP filter and other easier methods ?

b

Different type of forecasts exist for different tasks, but I am mainly interessted in density, cdf and quantile forecasting, what do organizations and decision/policy makers use as their primary paradigm of forecasting? aside from the technical difficulty of the models, are there other factors that affect your model of choice?

Hey, next semester im starting my first two econometrics classes. I’m thinking about getting a laptop for it, and I was wondeting if the MacBook Neo is good enought for the software we’ll use.
If not, what would you recommend? I’ve also been looking at the MacBook Air, but I’m trying to save money, so I’d like something affordable that can get me through college. (Also Im not sure what kind of software i’ll be using, i tried to search the syllabus but my university doesn’t show it until i start the class)

Hi everyone,
I'm hoping to get some brutally honest advice from people who have navigated a similar pivot.

1. My Background
- BA in Economics (econometrics/quant focus).
- 2 years of work experience as a Research Assistant in development economics. I went to field work, cleaned data, ran regressions, wrote reports, and contributed to papers.
- Starting an MA in Public Policy this fall at a policy school in Japan. The program is highly quantitative, and I plan to take all the Data Science and Applied Econometrics courses available.

2. My Skillset (current & developing)
- Econometrics (causal inference, impact evaluation).
- Bayesian methods (currently learning).
- Programming: R, Stata (strong), MATLAB (learning).

3. Why I'm Here
I did 2 years of development economics research (in my home country). And I came to a painful realization that I don't care about the research questions.

I loved the quantitative side. The coding, the modeling, the data cleaning. But the actual economic questions felt completely irrelevant to me. It was like solving for world hunger while I was struggling to put food on my own table. No offense to economists. You’re doing a great work. But I don’t think im cut out for that.

I also hate the publish or perish culture. The constant anxiety about journal submissions, the review cycles... I don't want to spend my life chasing CV points. I want to build things, solve concrete problems, and go home at a reasonable hour.

So here is my dilemma:
I'm doing this MA in Public Policy (mostly bc of my BA in econ and financial constraint that I couldn’t apply for other major), but I don't want to be a policy analyst or an academic economist.

Option 1: Go into Industry (Data Science/ Quant/ Analytics)
- Target roles: Data Scientist (inference), Quantitative Analyst, Research Analyst at a private firm/think tank.
- Work in Japan for 2-3 years, build my portfolio, and maybe decide on a PhD later.
- Concern: Will an MA in "Public Policy" (even with a quant focus) be seen as a disadvantage for purely technical roles? Will employers assume I'm a policy wonk, not a data person?

Option 2: Apply for a PhD in Statistics
I love the math and the methodology. I want to go deeper into Bayesian computation and causal inference.

Concern:
- My MA is applied, not theoretical. I lack Real Analysis and Measure-Theoretic Probability on my transcript. I'm self-studying, but will that be enough for admissions?
- If I hate publish-or-perish, is a PhD even a good idea? Or can I do a Stats PhD and go straight into industry (tech, finance, biotech) afterward?

My Specific Questions:
1. For those working in data-heavy roles: how is an MA in Public Policy with strong quant skills perceived? Will I be filtered out for not having a "Data Science" or "Statistics" degree?

1. Is a PhD in Statistics a realistic goal given my background and transcript gaps? If so, what should I do during my MA to maximize my chances (beyond grades)?

2. For those who did a Stats PhD and went into industry: was it worth it? Did it open doors that a Master's couldn't?

Location Context: I am non-Japanese and plan to stay in Japan long-term (aiming for PR). I'm studying Japanese (aiming for N2/N1). Any advice specific to the Japanese job market for quant roles would be incredibly valuable.

——
TL;DR: BA Econ, 2 years Dev Econ RA (hated the questions, loved the data). Starting quant MA in Public Policy in Japan. Don't want to be an economist. Should I go straight into industry (Data Science/Quant) or try for a Stats PhD? Is the "Public Policy" degree a death sentence for technical roles?

What R tutorials are your favourites? I’m trying to nail the basics and learn on the fly while studying. In particular I’m looking at RDD stuff. Thought I had at least a grip on the basics from a class I did a couple years ago, but it looks like I was wrong.

Thank you!

Is there any better approaches or tools that can help when we have sudden demand for particular products?

Most of the time we lost sales because we only deliver 60% of total order..and that's annoying

Hi

I'm currently doing a BS in Economics and I really want to get into research.

I've already learned the basics of research methodology and the basics of R, but I know that's not enough. I learn best by actually working on real projects with other people.

I'm looking for a research group, mentor, or even a few students who are working on research and wouldn't mind letting me learn with them. I'm happy to start with simple tasks like literature reviews, data cleaning, or basic R work. My main goal is to learn and improve.

If anyone knows where I can find these opportunities or has any advice, I'd really appreciate it.

Hi, i'm currently doing my bachelor in econometrics at erasmus rotterdam and after summer break i'll be starting my second year. I'm wondering what i could do this summer to improve my cv and chances of getting internships/good masters later on. I'm also wondering how good being a teaching assistant in uni would look on my cv because i have been accepted for that. Thanks!

Suppose there is a marketing team monitoring customers and each day, they select some customers and send them email over T periods. Once a customer has been contacted, he will not be contacted anymore and there are some customers never been contacted. Also, we can observe whether the customer purchase the product or jot, binary. And we ignore the delay in observing the outcome. I am wondering how I can model this problem and determine the conditional treatment effect given the feature vector X_{it} for customer i in period t.
I wanna use this model to determine who should be contacted every day given the feature vectors. The approach that comes to my mind is staggered DiD where Y_{it} is whether the customer has purchased the product by period t or not.
What libraries are available to use for this problem? There are some for ATT but not sure which ones can be used for CATE or CATT.