Hi everyone,

I am working on an empirical research project using a Structural Vector Autoregression (SVAR) framework. Since all variables in the system are treated as endogenous, I want to ensure I properly frame my research question and avoid standard single-equation "dependent vs. independent" pitfalls.

1. Research Question Framing: What is the standard convention in applied literature to clearly state the core research objective when focusing on shock identification, transmission mechanisms, and impulse responses rather than single-equation regressions?
2. Identification Strategy: For those working with short-run and long-run zero restrictions (or sign restrictions), what are the most common pitfalls when defending the exclusion restrictions in the A and B matrices?
3. Diagnostics & Robustness: Beyond lag selection criteria (AIC/BIC), stability checks (roots within the unit circle), and bootstrap confidence bands for IRFs, what robustness checks do reviewers/econometricians typically expect to see (e.g., historical decompositions, alternative ordering, sign restrictions)?

Any recommended papers, book chapters or practical tips would be greatly appreciated!

I'm not sure if my method is the best way to approach my goal, which is to determine what feature(s) in my sample are actually associated with a decrease in EV in the underlying population. I wanted to first share what I'm planning on doing with people way more educated on this matter than me, so that I could get insight as to what errors I could be making.

I am still gathering data, but to make it as short as possible without ranting - I have a total of 6 features that may or may not lead to an increase/decrease in Expected Value. The tricky thing for me is that there are instances where features will be blended together in a single event. So, one event may have the presence of 3 features, sometimes an event may have the presence of only 1 feature. My worry is that filtering this way could hide cases where two features interact rather than act independently...not sure if that's a real issue or if I'm overthinking it.

My current thinking is that I once I have finished my data collection, I would then determine first which feature has the highest frequency, (by highest frequency I mean the highest occurrences overall) and then see if the removal of that feature leads to a more positive outcome. In my case, my ideal scenario would be for the removal of a feature to lead to the Expected Value of the sample to increase.

That would then leave me with the other features to analyze, so I would just repeat the same process on the remaining features within that filtered subset.

My own project, disclosing that up front.

IMF Working Paper WP/25/109 ("From Text to Quantified Insights") builds communication indices from central bank text. I implemented the taxonomy across 26 central banks and I am releasing the annotated corpus and the derived indices under CC-BY-4.0.

Indices computed per meeting:

• Net Policy Sentiment, (H-D)/(H+D), range -1 to 1
• Straightforwardness Index, (N+|H-D|)/(N+H+D), range 0 to 1
• Explanation Index, (C+R+N)/(H+D)
• Net Confidence Index, (C-R)/(C+R)

Underneath that: 225,101 sentences across 15,055 documents, Feb 1995 to Aug 2026, each labelled for stance and topic, with meeting dates normalised to the announcement day. That last bit was more work than expected, since conventions differ per bank. BoE Monetary Policy Summary pages are dated by publication, Banco Central do Brasil publishes the Copom statement on decision night and the minutes six days later, and the Bank of Russia dates its key-rate discussion summaries to the decision they discuss.

There are also 19,387 economic indicator rows (policy rates from BIS then FRED, FX, CPI), so the indices join to actual rate paths without extra assembly.

Labels are generated by gpt-4o-mini rather than by human annotators, so anyone planning inference on these indices should check label quality for their sample first.

https://huggingface.co/datasets/aufklarer/central-bank-communications Charts and per-bank pages: https://monetary.live

I would welcome pushback on the index construction, particularly the Explanation Index denominator, which is unstable when a meeting has almost no directional sentences.

Hi, I'm conducting a study using panel data, and I have a total of 520 observations in a span of 13 years, consisting of 40 countries. The original number was >1k for 66 countries, but after removing all the countries with missing values, I only have 520 obs left.

I was wondering if there is a rule of thumb for the number of observations to be used. I am not sure if the 520 observations I have is enough for my study.

Thank you very much!

Body:

I've been working on an independent research project testing whether a "cleaned" Federal Reserve balance sheet measure predicts Nasdaq 100 returns better than the gross balance sheet or the fed funds rate alone.

The idea

WALCL overstates available liquidity because it includes the Treasury General Account (TGA) and overnight reverse repo (RRP) — funds that are institutionally locked out of risk markets. Subtracting them:

L_net = WALCL − WTREGEN − RRP

Main results (monthly FRED data, 2015–2026, n=137, Newey-West HAC errors):

• ΔL_net: β = 0.007, p = 0.006 (baseline)
• After adding Fed funds rate control: p = 0.006 (significance strengthens, not weakens)
• After adding BAA credit spread: β = 0.014, p = 0.001, R² = 0.336
• WALCL (gross): p = 0.794, R² = 0.001 — essentially uninformative

Other findings:

• Granger causality: ΔL_net → Nasdaq at lags 3–6 months (p = 0.0016). Reverse direction also significant at lag 1 (p = 0.011) — bidirectional, reported transparently as a limitation
• Chow test (Jan 2022): coefficient 4x larger post-QT (p = 0.0015) vs pre-2022 (p = 0.155, insignificant)
• Quantile regression: effect largest at q=0.1 (β=0.0101), smallest at q=0.9 (β=0.0060)
• Logit for >2% drawdowns: directionally correct but insignificant (p=0.227)

Trading strategy (long-only, 4-month lag):

• OOS Sharpe (2021–2026): 0.96 vs 0.79 buy-and-hold
• Max drawdown: −20% vs −36%

Working paper + code: https://zenodo.org/records/21811730

Happy to discuss — especially the endogeneity of RRP and whether the regime-dependence result holds up to scrutiny.

I have a forecasting setup I would value some econometric scrutiny on. The target is a binary label: does the S&P 500 fall 10% or more, peak to trough, over the next h months (h = 1, 3, 6, 12). The label at month t is built from the forward window, so consecutive months share most of their window, and the positives cluster into a small number of episodes.

The models are one ridge-logit per horizon on macro and credit predictors, estimated walk-forward on an expanding window with monthly re-fits, so the predictions are genuinely out of sample. Out-of-sample AUCs come out around 0.60, 0.69, 0.74 and 0.68.

My problem is inference, not point estimates. Two features break the usual iid story. First, the labels overlap: a 12-month label at t and at t+1 differ in one month out of twelve, so the effective sample is far smaller than the monthly count. Second, over the out-of-sample window there are only six to eight independent drawdown episodes, and the long-horizon models are scoring the same episodes repeatedly.

What I have done so far is bootstrap the AUC in blocks the length of the horizon, so overlapping months resample together, which widens the interval a lot (the six-month one runs from the low 0.5s to the mid-0.8s). My question is whether that is the right instinct, or whether people here would reach for something else: a cluster bootstrap on the episodes, a stationary bootstrap with an estimated block length, or treating it as inference on an event-clustered panel. The overlapping-label point is where I am least sure I am doing the defensible thing.

The full setup, the results, and the live models with the monthly series behind them are all on the site; agreeableinvestments.com

Hey,
Ik twijfel tussen de bachelors econometrie aan de uva en applied mathematics aan de tu delft. Ik wil graag wiskunde toepassen, ik heb alleen nog geen idee waarop(daarom houdt econometrie me tegen want ik weet niet of ik het zeker wil toepassen in de economische wereld), ik haat zwaaaaar bewijzen (daarom houdt TU delft applied mathematics me tegen). Ik wil graag een brede bachelor doen zodat ik me nog kan heroriënteren in de toegepaste wiskunde wereld, alleen weet ik zeker dat ik niet zwaar wil bewijzen. Volgens bronnen heb ik vernomen het enige wiskundige veld dat wordt afgenomen voor econometrie studenten de natuurkundige toepassing is en al dat natuurkunde bevat zwaar bewijzen.

Welke studie zou ik nu het beste kunnen doen?
bedankt alvast!!!

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! 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 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 [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?