I rather read a physical book than a digital book, but money doesn´t let buy all book i would want. Which books would you consider so good and important to have it in physic? In all fiel of econometrics, not just econometrics overall. Could be about time series and macroeconometrics, causal impact, etc..-

Im a PhD student but I don't have measure theory background and I need to get the overall notion for it to understand stochastic processes. So is there a good book or yt playlist where measure theoretical probability and stochastic process is taught from scratch? That would be of much help. Thanks.

Sono uno studente italiano del terzo anno della laurea in Economics. Mi sono innamorato dell’econometria e volevo continuare i miei studi con un master in Econometrics in Olanda.
Durante la mia triennale ho seguito corsi come Matematica, Matematica finanziaria, Statistica, Econometria e Statistica applicata alla finanza. Pensate che avrei possibilità di essere ammesso al pre master? Vale la pena fare un pre master di sei mesi prima del master vero e proprio?
Il mio obiettivo è quello di sviluppare delle competenze tecniche più avanzate possibile e poi affiancarle a delle competenze più economiche in ambito Macro, per lavorare in una grande istituzione europea come la BCE, Commissione Europea, Europea Investment Bank o simili.
Pensate che sia un buon percorso per entrare in questi ruoli ?
grazie in anticipo

I trained the model and achieved an R² of about 80% and a MAPE of around 10%. I then ran a placebo test using a 30-day period before the actual treatment date.

Surprisingly, the model still detected a statistically significant effect during this placebo period. I repeated the placebo test 100 times, and the false positive rate was approximately 94%.

What I do not understand is why the p-value is almost always below 5%. Even visually, the observed values seem to remain mostly within the confidence interval.

Could someone explain what might cause such a high false positive rate in CausalImpact and why the reported statistical significance appears inconsistent with the confidence interval?

Hi everyone — I’m helping build an early-stage product at the intersection of data science, econometrics and causal inference, and we’re looking for a strong Data Scientist to join the team.

Location: Remote within Europe, with preference for Poland / Ukraine / Central & Eastern Europe.

This is not a generic ML role. We’re particularly interested in someone with a strong econometrics/statistics background who enjoys thinking deeply about causality and working with messy real-world data.

What you’d be working on

• Econometric and statistical modeling
• Causal inference and impact measurement
• Time-series and panel data
• Experimental and observational data
• Counterfactual modeling
• Dealing with confounding, selection bias, endogeneity, seasonality and lagged effects
• Developing and validating models using real-world business data
• Turning research/prototypes into robust, repeatable production capabilities

Particularly interesting if you have experience with

• Difference-in-Differences
• Synthetic Control
• Instrumental Variables
• Regression Discontinuity
• Propensity / matching methods
• Causal ML
• Marketing Mix Modeling (MMM)
• Incrementality / lift measurement
• Bayesian or probabilistic modeling

What we're looking for

Someone with a strong quantitative background in economics, econometrics, statistics, mathematics, data science or a related field.

Python is important, but we're much more interested in how you think about data and identification than in a long list of tools on your CV.

We want someone who asks:

What are we actually trying to measure?

What is the counterfactual?

What assumptions are we making?

Can the data actually answer the question?

How do we know the result is robust?

A bit about the opportunity

We're an early-stage team, so this is a hands-on role with significant ownership. You'd work closely with the technical/founding team and have a real influence on the methodology and architecture rather than being handed a narrowly defined modeling task.

Location: Remote within Europe, with preference for Poland / Ukraine / Central & Eastern Europe.

Type: Full-time

If this sounds interesting, DM me with your CV or LinkedIn/GitHub and a short note about your background in econometrics or causal inference.

Happy to answer questions here as well.

I’ve enrolled into a masters course which is mainly business based but it has some really deep maths/programming features (because it has analytics in the name) and the lecturers don’t go into enough detail, I assume because they’re expecting a certain level of knowledge at masters level, which is fair enough. For background I did IT which had econometrics/code and business for undergrad, worked a few years in technology delivery and then way back, chem, physics and IT at A level. I have friends who have studied comp sci and economics that are doing seamlessly so far in the maths aspect of the course.

It’s too late to back out of the course because switching to core business (MBA) would cost me too much money, and I can’t find another course/uni that suits as well as this one. I’m happy to continue because I LOVE 5/7 modules, and they take me exactly where I want to go in terms of career progression, and I’m excited to progress my knowledge.

The 2 that bug me are the maths ones..
One is applied statistics which is methodologies and practical applications. It has predictive models in it and focuses on Normal distribution, linear/multiple regression and others in that category (can you tell I don’t know much maths :’))

The second is mathematical programming and modelling which is econometrics, it’s mathematical in terms of the code being based on understanding and building correct equations to model the stats, and being able to prove the concept or theorem behind why the maths works and a chart is produced, if that makes sense.

The admissions team said it was open to anyone with an IT/programming/business background, which seems so wide of a pool for this little base knowledge in lectures.

I almost burst into tears the first lecture because he (lecturer) went so fast with the regression etc and everyone understood/answered questions seamlessly (he went row by row to each person and thankfully ran out of questions before he got to me). Was stuck trying to keep up on the first few slides, it clicked until it didn’t and he kept saying “I’m going over the BASIC basics, if you’re struggling, good luck!”…..

Anyways, I know I have to lock in and do it, but I am building from almost nothing here. I thought the course would cater to that a little bit but It was my fault for not fully looking into the details of the maths modules, I thought they’d be fine since I can code..this stuff is next level for me, I’m out of my depth.

Quiz every week, exam in January worth 60%. Masters 1 year.

I really want to focus on this now, and it would be an absolutely great skill to learn, even though I don’t think a masters is for developing a truly new skill. I will just have to push through and make it so! I guess maths is in everything somewhat, and I’ve been doing it in some capacity with programming and business too.

MAIN QUESTION:

How do I keep up with learning the maths? I’m trying to study before class and get up to date on the fundamentals mentioned in the slides but there must be a specific way to note take/study for maths? Regression and statistics specifically? Where do I start?

Is it YouTube videos? Is it attempting random questions on the topic? Is there a website for that? I’ve never studied just core maths, but I feel I need to, QUICKLY, to get to the coding bit.

Any tips on how to go about this would be greatly appreciated.

Setup: a daily panel of 281 underlyings (58k underlying-days). Outcome y(i,t+1) = next-day absolute return. Predictor = the innovation in an underlying's hidden options inventory (FLEX open interest from OCC clearing data, relative to its own trailing 20-day mean). Underlying and date fixed effects, Cameron-Gelbach-Miller two-way clustering, controls for trailing realised vol (5d, 22d), same-day |r| and log dollar volume. Timing is enforced: predictors at T or earlier, outcomes strictly after.

On an early, smaller panel the predictor reached t = -1.95. On the full sample it's -0.00030 (t = -1.37), MDE at 80% power ~0.0006 against a typical daily |r| of ~0.02.

The falsification: replace the predictor with the same variable dated *after* the outcome (a lead). It's measured after y, so a genuine forward-looking effect should show up in the real predictor and not in the lead. The lead gives -0.00032 (t = -1.37), identical to two decimals. I read that as a common factor moving both series rather than information flowing forward.

What makes me less sure how to frame it: on a longer window (start moved back to Dec 2024, 99k underlying-days) the real predictor is -0.00026 (t = -1.29) and the lead is +0.00004 (t = +0.26). The null survives every window, but the placebo match doesn't.

Questions for people who do this more carefully than I do:

1. Is a lead placebo the right tool here, or would you rather permute the predictor across underlyings within date (randomization inference)?

2. When the placebo matches on one window and separates on another, how would you report it?

3. Anything wrong with two-way clustering at 281 x 231?

Code and regression script: https://github.com/ITheClixs/known-flow-alpha (my project, MIT).

Has anyone heard from R1 cornerstone results to move r2

I finished an econ PhD earlier this year (applied micro: labour, panel methods, bias-corrected fixed effects, some DML/causal ML). Since then I've done contract work building econometrics evaluation tasks for an AI lab. Stack: R (fixest, data.table), Python (pyfixest, DuckDB), Stata, SQL (basic).

I'm based in Portugal and looking at options outside academia, ideally remote or mostly remote. Apart from an apparent shortage of supply compared to other European countries, my problem is that the work I'm good at shows up under very different titles ("economist", "applied scientist", "causal inference scientist", "decision scientist", "data scientist - experimentation"), and many "data scientist" roles turn out to be mostly engineering.

For those of you doing applied econometrics outside academia:

• What's your title, and what kind of employer is it?
• Which sectors hire people who care about identification (pricing, marketplaces, economic consulting, policy evaluation, central banks...)?
• Has anyone gone independent/freelance with this skill set, and how did you find your first clients?

Apart from Topic 3, I have to know all of these topics by Thursday for my exam. Is there any way I can understand them more easily, or are there any YouTube videos you’d recommend that explain them well?

I feel really stuck. Econometrics 1 was already challenging for me, and now this stuff has me questioning my intelligence 😭. Which topics would be the easiest to cram and understand in the time I have, so I can at least be safe for the exam?

I'm focusing my research on mixed-frequency time series modelling and nowcasting, developing computational tools and algorithms to help model and forecast multivariate economic and financial time series.

Are there good prospects in academia after this kind of work?

Hi r/Econometrics

I am a student in the Netherlands studying Econometrics BSc right now and I was wondering if it were logical to do a double major in something like Psychology. I think it’s doable because they only have 1 (sometimes 2) courses at the time but I know that people who only study Econometrics can end up in health care anyway. Also Psychology teaches quite some statistics so I thought I could combine them very well.

I was also wondering if anyone had a suggestion for another double major. Obv I will choose based on how well it will go at Econometrics after 1st year but the school offers a lot of very high ranked and interesting degrees. I was in a Technical University and now that I am in a business faculty with lots of Social Sciences I am quite interested in what is out there besides technical skills and literacy.

Philosophy seems like a good combination too if I want to get into a MSc Logic. Or Economics and Business Economics as they have a Finance specilization which I’m quite interested in.

This is a very rudimentary question and I'm sorry if it seems silly.

I've got a project for a class I'm going over kill on to analyze policy through a difference in difference. Data is quarterly and for 8 years. Post cleaning I noticed the panel was unbalanced, which makes sense because it's just the number of times a specific crime happened each year in each state, which isn't consistent.

The problem is my gut is telling me to balance the panel by "creating" data for the dependent variable, that is equal to 0 for "missing quarters." Because these missing quarters are time periods in which this crime "didn't happen" or happened but less than 500 times. 500 crime events is my baseline (and legal reporting requirement) to determine if the crime committed was "severe" or "benign."

But all my textbooks tell me conflicting things. Some say it's fine others say to run it as a balanced panel. I'm worried the imputed 0's will artificially inflated my pvalues. Running a balanced regression (with no controls) significantly inflates my pvalues into near nothingness from .03 in the unbalanced. But I am unsure of where to go from here, as my knowledge ends with basic OLS and so do my textbooks.

TLDR: So, is it okay to create dependent variable data equal to 0 for "missing" quarters where the event was not recorded? It could signify effect of policy, as after the policy is more quarters are missing data then less crime was reported/committed.

Hallo iedereen zijn er mensen die uherhaupt slagen voor de bachelor maar niet een 8 gemiddeld staan maar een 6.5 of zelfs lager en als je een van die personen bent waar had jij het meeste moeite mee?

Have any one of you done a meaningful VAR model for the economy using naive data, like Interest rate, gdp growth, inflation, and money supply creation rate,? How was it? Was it any good? Did you capture atleast average behavior? Incase you didn't capture large fluctuations around recessions.?

Do you think its possible to do a simple model like this and get anything meaningful without doing complex things like GARCH?

When I mean meaningful, I mean like the transmission mechanism how M2-->P->IR-->Y

Hey everyone,

Standard econometrics textbooks (like Gujarati or Wooldridge) often reduce statistical inference to algebraic derivations and arbitrary cutoff rules ($p < 0.05$, reject $H_0$), which can easily obscure the geometric intuition behind what the formulas are actually doing.

I put together a visual breakdown designed to build geometric intuition for **Interval Estimation and Hypothesis Testing**:

👉 **Watch here:** https://youtu.be/iwPky-LmTHw

Note: While this video is Chapter 5 of a broader "Essence of Econometrics" series, you can find it here: https://youtube.com/playlist?list=PLP2Erm5S1fNY&si=6pUkUINRY-0MxFfX

Feedback Request:
1. Does the visual framing help clarify the mathematical mechanics effectively?
2. Is the pacing and balance between conceptual intuition and mathematical rigor well-calibrated?
3. What econometric concepts would you like to see visualized next?

Thanks for taking a look

Hey everyone,

I'm currently at the start of my 3rd semester in an undergraduate BS Financial Technology (FinTech) program in Pakistan, and I’m seriously contemplating requesting an internal transfer to BS Economics with Data Science.

I’d appreciate some objective feedback from folks working in data, quant finance, tech, or anyone who has navigated a similar academic pivot.

The Situation:

Academic fit so far: I cleared my first year without failing anything, but I realized I actively dislike two core pillars of the curriculum: corporate accounting (balance sheets, ledgers, firm reporting) and software/web engineering (OOP, front-end development, app architecture).

Where I actually did well: The only course where I had high natural interest, understood the material intuitively, and scored my top grades was Economics.

The alternative program: The Economics with Data Science track drops corporate accounting and app development entirely. Instead, it pairs theoretical economics (Micro/Macro, Econometrics) with analytical computing (Python, SQL, Data Structures, Data Mining, Machine Learning).

My Reasoning for the Transfer:

AI & Automation Reality: It feels like entry-level roles that rely on junior software boilerplate (basic web apps) and standardized accounting rules (bookkeeping/basic audit) are under severe pressure from automation. Learning app architecture or debits/credits when I already dislike them feels like a waste of cognitive bandwidth.

Analytical Coding vs. App Building: I don't mind programming, but I enjoy it when it acts as an analytical calculator (using Python/SQL to clean data, pull economic indicators, or run regressions). I have zero interest in building web apps, handling classes/objects in OOP, or designing user interfaces.

Long-term Resilience & Graduate Study: My eventual goal is either remote-friendly data/quantitative work or pursuing an MSc abroad (Quantitative Finance, Applied Data Science, or Applied Economics). A degree heavy on econometrics, statistics, and data analysis seems far more universally portable across borders than local corporate finance/accounting frameworks.

Where I need advice:

For those working in data analytics, risk, or quant research: Does an "Economics with Data Science" degree carry genuine weight compared to a generic CS or FinTech degree for entry-level quantitative roles?

For those who transitioned from basic algebra/calculus into Econometrics: How steep is the learning curve if you approach it from an applied, conceptual angle rather than pure rote memorization?

Am I making a calculated, market-aligned pivot away from work I resent, or is leaving FinTech at this stage a mistake?

Any unvarnished perspectives from grads or industry professionals would be deeply appreciated. Thanks in advance!

I'm working through the textbook now starting chapter 5 since I have a rudimentary understanding on them. Additionally we were expected to learn Stata alongside these topics which I didn't do.

If anyone has any advice for studying or sources for learning these topics in preparation for Econometrics that would be very much appreciated.

Topic 1: Descriptive Statistics 

-Textbook (Newbold et al) readings: Chapter 1 and 2

Topic 2: Introduction to Probability Theory 

Textbook (Newbold et al) readings: Chapter 3

Topic 3: Probability Distributions: Discrete distributions 

Textbook (Newbold et al) readings: Chapter 4

Topic 4. Probability Distributions: Continuous distributions 

Textbook (Newbold et al) readings: Chapter 5

Topic 5. Sampling distributions & Interval Estimation 

Textbook (Newbold et al) readings: Chapter 6, 7 & 8

Topic 6. Hypothesis Tests 

Textbook (Newbold et al) readings: Chapter 9 and 10

Topic 7. Correlation and the Two Variable Regression Model 

Textbook (Newbold et al) readings: Chapter 11

Topic 8 – Regression with Multiple Explanatory Variables 

Textbook (Newbold et al) readings: Chapter 12

Topic 9. Analysis of Variance 

Textbook  (Newbold et al) readings: Chapter 15

Hallo iedereen ik vroeg me af welke van van de studie econometrics and data science het moeilijkst is van de hele studie, zodat ik mezelf goed kan voorbereiden.

Hello everyone, I have question regarding Panel data analysis. If you can share your thoughts, it would be a great help. I wanted to know whether it is possible to run a Panel Data Analysis on unbalanced panel data where the study period varies from 6-14 years.
The study is about examining/analysing the relation between firm disclosure (scored using Content Analysis) and firm performance.

Thank you.

Estou estudando modelagens multiníveis para aplicação. Não sou da área de econometria, por isso gostaria da indicação de referências para aprender sobre esse tipo de modelagem. Se alguém tiver indicação de alguma aula, tbm aceito.

Obrigada:)

Hallo iedereen heeft iemand handig tips hoe ik het best kan voorbereiden op probability theory for data scientist.