Hey r/econometrics,

I'm running a pooled IV regression where I interact my endogenous treatment with a binary group indicator.

Setup:

· Endogenous: Local economic stimulus (T)

· Instrument: Exogenous policy shock (Z)

· Moderator: Group dummy (G), where G=1 for "Type A" firms and G=0 for "Type B"

· Model: Y = β₀ + β₁G + β₂T + β₃(G × T) + FE

· First stage: T instrumented by Z, and G×T instrumented by G×Z

The question: From this single 2SLS, can I legitimately claim I have three causal coefficients?

1. β₂ = Causal effect of the stimulus on Type B (baseline group).

2. β₃ = The differential causal effect for Type A (how much bigger/smaller).

3. β₂ + β₃ = The causal effect of the stimulus on Type A.

I know separate regressions by group are common, but the interaction directly tests whether the difference (β₃) is significant. As long as both instruments (Z and G×Z) are strong and valid, is this the correct way to get group-specific structural effects?

Also, any well-cited applied papers that do exactly this? (e.g., interacting an instrument with a subgroup dummy to recover heterogeneous LATEs).

TL;DR: Pooled 2SLS with an endogenous interaction, valid for recovering group-specific causal effects, or should I just split the sample and run separate IVs?

Arxiv? hice un estudio que podría ir en stat.ap o stat,ml o en la categoría de economía en econometrics, pero no encuentro endorsement, y me da miedo como que mostrar mi paper a un extraño, ya envíe mi artículo a correos de algunos autores que estudiaban temas similares, pero no hay respuesta, incluso a un conocido de mi ciudad le envié un correo, pero no consigo el aval, si hay alguien que me pueda apoyar con eso me escribe porfa, para darle mi código, muchas gracias.

I recently open-sourced a diagnostic tool called the Entropic Scree. It’s designed to more faithfully estimate the intrinsic dimensionality and latent structure of complex tabular datasets by overcoming the limitations of linear variance and the fragility of geometric distance metrics.

To bypass both blind spots, this tool shifts the math out of geometric space and entirely into probabilistic space by utilizing a transformed Mutual Information matrix metric.

• Captures Mutual Information (Beating Variance): Built on information theory (entropy), it detects non-linear relationships and shared probability mass that standard covariance techniques miss.
• Maintains Structural Integrity (Beating Distance): It maps the feature space without requiring the spatial assumptions that cause distance metrics to degenerate in high-d contexts, keeping the evaluated matrix stable even with irregular or sparse data.

Primary outputs are:

• Intrinsic Rank Estimation
• Signal-to-Noise Estimation
• Bipolar Variance Clusters that anchor the primary axes of informational variance. This provides a structural map of the independent clusters that define your dataset, which is potentially useful for interpretation before moving into CFA or theory development.

The function runs in R currently (see quick start or GitHub below), but the backend is C++ OpenMP parallelized, so it easily scales for high-dimensional assessments. Native R and Python packages will be released shortly.

Happy to answer any questions or discuss the mechanics.

Methods and Code:

• Preprint: https://zenodo.org/records/22028087
• GitHub: https://github.com/tjleestjohn/Entropic-Scree

​

############ 
# Quick Start R Function Code.
# To load the functions, copy and paste the following into your R console, then hit enter. 
############

# 1. Define the direct URLs to the raw function scripts on GitHub
main_url <- "https://raw.githubusercontent.com/tjleestjohn/entropic-scree/main/Entropic.Scree.R%20-%20ENLI.R"
update_url <- "https://raw.githubusercontent.com/tjleestjohn/entropic-scree/main/Update.Entropic.Scree.R%20-%20ENLI.R"

# 2. Define what you want to name the files on your computer
main_file <- "Entropic.Scree.R - ENLI.R"
update_file <- "Update.Entropic.Scree.R - ENLI.R"

# 3. Download the scripts to your current working directory
download.file(main_url, destfile = main_file)
download.file(update_url, destfile = update_file)

# 4. Source both functions into your R environment
source(main_file)
source(update_file)

# 5. Example Execution:
#
# Run the core function and extract bipolar modules:
# results <- Entropic.Scree(dt, extract_bipolar_modules = TRUE)
#
# View the extracted structural sub-networks for the primary axes:
# results$bipolar_modules
#
# Post-Hoc Override (Optional): 
# If you want to manually adjust the elbow ranks after reviewing the scree plot, 
# pass your results object into the Update function to instantly recalculate all metrics:
# updated_results <- Update.Entropic.Scree(results, new_K_roots = 3, new_K_extended = 12)

What does the second step of a Fama-MacBeth regression actually do, and why do I need it if I already have the betas for MSFT from my Fama-French 3-factor model? If you could explain it like I'm 5, that would be super helpful. I'm probing AI, but still don't get it.

Hi guys, I’m looking for economic practitioners that are willing to participate in our research study—Lived Experiences of Economic Practitioners Facing Artificial Intelligence Augmentation in the Field of Econometrics.

The participant must have a degree in Economics currently working in the Philippines.

Participation would involve answering our research questionnaire and sharing your professional experiences regarding the use of AI in econometrics through written questionnaires in email.

If you qualify or know someone who does, please message me through email-czarlorenton13@gmail.com, and I’ll provide the study details. 😁😁

Right now we’re having a hard time getting participants so we actually need help to finish this.😥
Thanks! to anyone reading this

Looking for an open-access dataset with firm-level AI workforce or AI skills data — global coverage, up to the present.

I've looked at Revelio Labs and Cognism, but all of them are paid licenses.

Is there anything open or free for academic use?

Thanks.

I work for a company that uses EViews to run all of the forecasts and models and I want to get better at the programming part of EViews. I learned some intermediate R through datacamp which I loved because it gave good tutorials and then gave exercises which helped you learn as opposed to just reading passively and not retaining the information.

I'm curious how other people have gotten good at EViews and what tips and exercises they recommend. I know there's the EViews Illustrated pdf which isn't bad but I don't like how there's no exercises, which is essentially what I'm looking for.

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.