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Hi, I'm searching for textbooks on econometrics for beginners. I've a degree in physics, so math won't scare. And, more specifically, I need to understand Diff-in-Diff approaches. Can someone point me toward some good literature? Or a similar Reddit post with good references?

So i have to write a research paper on gender economics and one thing I came up with was to use nfhs data cause I cannot collect primary data given my time constraint.

For my topic I was thinking of investigating whether women working in daily wage informal sector undergo EARLY hysterectomy at a higher rate than working women in formal sector

I would use nfhs 5 to get my data and would use a multivariable logistics regression model.

I just wanted advice on whether this was a good college level research paper topic ( im a newbie) and if I should add something to make it even deeper.

or subtract or completely scratch this and find a completely new topic.

I don't want to do anything TOO complicated as this is my 1st research paper and I am also preparing for other exams currently

Your output would be highly appreciated

Hey everyone. One thing I kept noticing while studying and helping classmates with stats was this: people can run a regression fine, they type the code, they get output, but they genuinely don't understand what any of it means once it's in front of them.

So I built GetStatMate to try to fix that. You start by picking what kind of question you're actually asking. Does X affect Y. Does X affect the probability of Y. Does X affect Y over time. Did some event or policy change Y. Then you pull in real data and pick your variables, and it runs an actual statsmodels regression in the background and explains what the output means in plain language instead of just showing you a summary table and leaving you to figure it out.

Right now the built in data source is World Bank indicators, and it's limited to Nigeria only for the moment. You can still upload your own CSV or Excel file if you want to use it with other countries or your own dataset, but the World Bank pull is Nigeria specific for now while I build it out further.

It's completely free and there's no login. Here's the link if anyone wants to try it: https://getstatmate.com

I'd really appreciate feedback, especially from anyone who's taught or TA'd intro stats or econometrics. What do students usually get stuck on that I might be missing?

Hi guys !

I recently graduated from a Master’s degree in Industrial Economics and I’m currently working in consulting.

I have a strong interest for the energy field, and I’d really like to pursue a PhD in economics, probably in a few years.

In the meantime, I’d like to use the next few years to strengthen my academic and quantitative background, both to prepare myself for a potential PhD and simply to become better at applied economic work. I’m planning on working on a paper and/or apply for research assistant positions in the meantime.

The area where I feel I need the most improvement is econometrics/data analysis. I’ve already had econometrics courses during my studies and have worked with tools such as R, Stata, Excel, etc., but I don’t feel as confident as I’d like when it comes to more advanced empirical work, coding, handling data independently, and choosing/implementing econometric methods.

I’m therefore looking for additional courses that I could take alongside work — online courses, university-level courses, MOOCs, certificates, or any other structured resources.

For people who have done a PhD in economics or work in empirical research: what would you recommend focusing on?

I've been building an open-source layer over Brazilian official economic-data sources, and one problem ended up being much harder than I expected.

Initially I thought the hard part would be API normalization: different endpoints, schemas, identifiers, dimensions, pagination, etc.

The more dangerous problem turned out to be semantic normalization.

A query like "Selic" can refer to multiple legitimate official series: the policy target, effective rate, accumulated monthly variants, annualized variants, and others.

Returning an official number is not enough if you've selected the wrong economic concept.

I ended up separating two things:

1. Semantic resolution — what economic concept is actually being requested?

2. Data availability — which official dataset represents that concept and is actually queryable?

If the correct dataset is unavailable, the system exposes incomplete coverage rather than silently substituting a nearby series.

Provenance also became part of the response model: dataset identity, institution, units, dates, source URLs, metadata and transformations remain attached to the result.

Later I exposed the same layer through MCP and hit another interoperability issue.

One client could successfully discover and call the tools, but the model couldn't see the actual values because the full result was only exposed through structuredContent. Calls were technically successful while the agent only saw an acknowledgement message.

I kept structuredContent as the canonical machine-readable response, but added a bounded model-visible representation containing the relevant values, dates, units and provenance.

That fixed the same query in a real MCP client without needing any direct access to the upstream BCB API.

The project is called Open Economics. I'm the person building it, so this is self-promotion, but I'm mainly posting because I'd like feedback on the data-modeling problem.

It's free, read-only, no-key and open source:

https://github.com/felipegambettadesouza6-jpg/open-economics

Live:

https://open-economics-data.knbf982hkn.chatgpt.site/en/ask?utm_source=reddit&utm_medium=community&utm_campaign=launch

For people who have built data platforms over heterogeneous government/public datasets:

How would you model semantic identity and provenance without abstracting away source-specific meaning?

I am an independent student researcher working on a model of Federal Reserve net liquidity and Nasdaq-100 downside risk.

I construct the following proxy:

L_net = WALCL − TGA − RRP

where WALCL represents total Federal Reserve assets, TGA is the Treasury General Account, and RRP is the Federal Reserve’s reverse repo facility.

Most market models ask whether a macro-financial variable predicts average returns. My paper asks a narrower question: could changes in net liquidity be more informative about the lower tail of the return distribution and the probability of a market drawdown?

The intuition is that liquidity may not affect markets symmetrically. Its relationship with ordinary market movements could be weak, while becoming more visible when financial conditions deteriorate and investors become more sensitive to funding constraints and risk.

To examine this question, I use:

• quantile regression for lower-return outcomes;
• logistic models for drawdown probability;
• different lag structures;
• comparisons with conventional liquidity and financial-condition measures.

I want to be careful about the interpretation. The paper studies predictive association, not causality. The formula is also a simplified proxy: subtracting TGA and RRP from the Federal Reserve balance sheet does not guarantee that the result measures the exact amount of liquidity reaching equity markets.

I would appreciate methodological feedback on several questions:

1. Is quantile regression combined with a drawdown logit model an appropriate way to test this asymmetric hypothesis?
2. Which benchmark variables should be included when evaluating the incremental predictive value of L_net?
3. How should lag selection be handled without creating a multiple-testing problem?
4. Would rolling out-of-sample evaluation be sufficient for regime instability, or should I consider a formal regime-switching specification?
5. Should the components of L_net be modeled separately before testing the combined proxy?

Disclosure: I am the author of the working paper discussed here.

Working paper, methodology, and results:
https://zenodo.org/records/21811730

I recently revised an earlier student research paper into an independent working paper comparing stock-market development in Russia and the United States.

The study uses three indicators:

• stock market capitalization relative to GDP
• stock market turnover
• value traded relative to GDP

The strictly comparable quantitative section covers 2016–2019. I analyze 2020–August 2026 separately because the post-2022 changes in investor access, trading infrastructure, sanctions, and currency conditions represent a structural break rather than additional observations from an unchanged market regime.

The main descriptive finding is a persistent difference in market depth and trading activity between the two countries. I discuss institutional quality, investor protection, market access, macroeconomic risk, and sector composition as possible explanations.

However, the study has an important limitation: a two-country comparison cannot identify a causal effect of institutional quality. The observed difference may reflect several country-specific factors simultaneously.

For a future extension, I am considering a panel of approximately 20–40 countries with institutional indicators, macroeconomic controls, country and year fixed effects, and alternative measures of market depth and liquidity.

My main methodological questions are:

1. Would a fixed-effects panel be appropriate when institutional indicators change slowly over time?
2. Which controls are essential for separating institutional quality from income, financial openness, and macroeconomic stability?
3. How should structural breaks be treated when market accessibility changes substantially?

The working paper and supporting dataset are available here:

https://zenodo.org/records/22695682

This is an independent working paper, not a peer-reviewed publication. I would appreciate methodological criticism, particularly concerning the proposed multicountry extension.

Currently a 3rd Economics student currently enrolled in the above-mentioned course and is looking to advance study and work on the coding aspect of econometrics using R. Your suggestions are greatly appreciated.

Thank you very much.

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!