Hi r/econometrics,

I wanted to share a new open-source Python tool that might be useful for anyone working with semi-parametric discrete choice models.

As many of you know, Manski’s Maximum Score Estimator (MSE) is a powerful semi-parametric alternative to Logit/Probit because it relaxes heavy distributional assumptions on errors. However, because the objective function is a step function with multiple local optima, traditional optimization relies on Simulated Annealing, which can be computationally slow and unstable due to random number generators.

Based on our recent paper in *SoftwareX* (2025) by Florios, Louka, and Bilias, we developed the **HITS algorithm (Hyperplanes Intersection Tabu Search)**. It provides a fast, deterministic global optimization approach specifically tailored for the Maximum Score Estimator.

We just officially published the Python implementation on PyPI: **`hitspy`**

### Key Features:

* **Deterministic:** No more relying on random seeds or stochastic jumps; the search leverages the geometric structure of the hyperplanes.

* **Scalable:** Drastically reduces computation time compared to traditional simulated annealing for larger datasets.

* **Easy Integration:** Built on top of standard libraries (NumPy/Pandas) and ready to be integrated into your empirical pipelines.

### Links & Resources:

* 📦 **PyPI:** `pip install hitspy` (https://pypi.org)

* 💻 **GitHub Repository:** [https://github.com/kflorios/hitspy]

* 📄 **Paper (SoftwareX, 2025):** [https://doi.org/10.1016/j.softx.2025.102164]

If you are currently working on discrete choice models or global optimization in econometrics, I would love for you to try it out! Any feedback, feature requests, or bug reports on GitHub are highly appreciated.

Happy to answer any questions about the algorithm or the implementation below!

Does anyone here know how many econometrics students graduate cum laude? Or at least how was it in your uni? Because I can’t find any sources online and I was just wondering - just that it’s pretty hard unless you’re super smart.

What is the quality of this bachelor’s programme seriously I am in it now and I regret it so much. The teaching in Calculus is so weird as they introduce things like we should know them as pure maths students but then we don’t even have a book to cover these “extra” materials such as propositional logic or quantifiers. I can’t find a single explanation about why it is the case that ¬∀x(P(x)) = ∃x(¬P(x)). Why isn’t this formally introduced and why do we have to puzzle this out lol. Why do we learn about contrapositive and then never work with it? I thought mm well they teach 2 courses at a time that must be easier then the VU but the VU study guide at least introduces renowned books such as David C.Lay. Linear Algebra as where our courses sometimes don’t even have any books

Do they want us to abuse their stupid UvA AI chat to figure things out? Go over the whole internet? Why can’t they state paragraphs to find elaborate explanations on why things are like that cuz my Dutch Maths B has never introduced logic and quantifiers if thats what they expected lol

Im a first year student and im thinking to buy a laptop but idk what should i buy or what to look for it. I wanna use it till i graduate .

(From turkey)

I used to build GDP nowcasts at a bank, and one thing that always bugged me about DIY backtests was how often they used revised data without accounting for it. Pull the latest series from FRED, run a historical backtest, and the results might look great. Problem is, some of those numbers weren't available on the forecast date. You're testing a model with information it couldn't have had.

That was the motivation behind AI Economist. It's an installable agent skill that runs GDP nowcasts using bridge equations, an SVD factor baseline, and a calibration layer. You can ask for this quarter's nowcast and get a distribution of possible growth outcomes, plus a Taylor-rule check for the implied policy rate. I also built in vintage tracking so historical runs use the data releases available at the time, with revisions recorded separately.

It's a small project for people interested in macro forecasting who want to mess around with the models. The backtests are there to demonstrate the workflow. Using vintage data addresses one source of look-ahead bias, but it doesn't settle model selection or out-of-sample performance. I wouldn't treat the results as research-grade forecasts.

If you give it a try, I'd be curious whether you spot any issues with the vintage handling or backtest setup. That's the part I spent the most time on.

Repo: https://github.com/garroshub/ai-economist-skill

Live dashboard: https://garroshub.github.io/ai-economist-skill/

I just took my midterm and I feel like I got a 0 on it. I studied for hours over a week, so I studied topics once and came back to them multiple times. I completed the textbook problems and problems from the slides.

I felt like I had a good grasp of the content. I really don’t know what I could have done differently.

I’m currently in a master’s that requires the first two classes of the PhD Econometrics sequence. I want to do a PhD and my grade in this class will be a good signal to the programs I’m applying to.

Any tips?

I have been working on causal inference and experimentation frameworks within the e-commerce tech industry lately, specifically dealing with platform behavioral and transaction telemetry data to determine how customers behaves on those e-commerce platforms.

Lately i have been facing problems where standard textbook statistical and econometrics theory fails when dealing with messy, real-world data distributions as human behaviour is unpredictable and many unexpected things, which i didnt even thought could happen, can happen in the real world. Such as product delivery delay, causing the customers to be angry and hence put them off from buying the product, each of these variables have to be accounted for.

To model and have these unexpected psychological state among customers accounted for, we use variables such as sudden spikes in customer support chat volume, rapid page refreshes, or immediate session drops.

Also, Marketing headers and automated CRM systems love to push blanket promotional campaigns, but as statisticians, we know that measuring simple unadjusted A/B lift is a massive selection-bias trap.

I been trying to come up with a Statistical model that is trying to model complex human behavior—specifically isolating true customer revenue incrementality from dead-weight margin cannibalization. Essentially, trying to answer: Did the coupon cause the revenue spark, or did we just hand a discount to a power-buyer who was already determined to buy anyway even without the vouchers?

Are people in the tech industry genuinely valuing such skills nowadays? Using statistics to model and predict human behavior and intent, answering questions like does our vouchers and ads really work?

I would love to hear your experiences how do u guys deals with the unpredictable messiness of human intent.

Hi guys, I need some advice regarding programes for Bsc. I would like to start studying one of those programes next year, but I have not yet decided which one should I opt for. Im an international student, interested in both banking and asset management and to work in a bb bank / firm.

Friend of mine told me that EOR is a great thing to do, because of how it prepares you for the math part, which is helpful especially with GMAT for the future. On the other hand EBE seems to me as more suitable for what I want to do in the future.

Nevertheless I would love to do my Masters in switzerland, espcially in finance, but for me its hard to tell wether I will be eligible with EOR bsc, as there is no accounting in th beginning(Ive found there is elective in the last year course intermidiate financial accounting). Also, lets assume I would always try to have my GPA for both of them at least 9/10.

Another thing that worries me is the AI coverage. Which one should be less affected by AI? Which degree in your opinion has birghter future?

If I picked EOR, what would be my advantages/disadvantages compared to other candidates for the banking AM roles in bb? which one is more advantegous?

Since I would love to start doing internships right after 1st year in AM at smaller firm, and lets say Im at EOR, after year 1 will I have learned Excel and programming skills or do I have to self learn them for my internship?

Ultimately, what would be your advice for me, with goals to work in bb asset management or banking, doing masters in finance in switzerland, should I therefore study as traditionally economics or should I go for econometrics and operations research?

I recently made a video that explores economic forecasting. It's rather long, the econometric portion starts at the 10 minute mark. I would appreciate any feedback. Thanks.

https://youtu.be/QXLkuZ1m3k8

I am doing my bachelor's thesis. So im completely new to research.

My supervisor told me to use the "Control Variable Approach." However, when i google this, i found nothing but "what is control variable, " like google gives me the definition of control variable.

I dont know if(Control Variable Approach) this is a method or something.

If anyone has resources, please share them with me.

Hi all, I'm working on a student project and would like a sanity check on our design.

We treat regional wage orders as the policy intervention. These orders have been issued ever since, so there is no single policy date, and a region can have an order in one year, none in the next, then another. The outcome is a gap measure observed in a few survey years (not every year).

The dummy is 1 if the region had a wage order that changed the wage in that year, and 0 otherwise. Standard errors are clustered by region. The idea is to compare the change in the outcome for regions with a wage order against regions without one in the same year, instead of a classic pre/post comparison.

Questions:

1. Is it fair to call this a DiD even without a single pre and post period?

2. Is it a problem that treatment is repeated and switches on and off?

3. Any papers or methods I should read for this kind of setup?

I’m estimating a 6-variable BVAR in Stata (bayes: var with minnconjprior) using about 100 quarterly observations, one lag, Cholesky identification, and a Minnesota prior. The first variable is an exogenous index, which is stationary in levels and ordered first for identification. The other five are macro variables, log-transformed and first-differenced, and all are stationary.

For stationary variables, I understand that setting the own-first-lag prior mean to 0 is the standard default (cross-variable means are 0 either way). My question is whether I should instead center the own-lag mean near each variable’s estimated univariate AR(1) coefficient, either for the index alone (since it’s more persistent) or for all variables. My AR(1) estimates are listed below.

I will do plenty of robustness checks, but want to better understand how to choose the optimal model. Apologies in advance, I do not have a linear algebra nor bayesian statistics background, but I am trying to learn as much as I can. Any recommendations for better learning these topics in general, or in the context of econometerics will be greatly appreciated. Thanks in advance!

AR(1) coefficients:

.70200785
.37907053
.51473649
.40885412
-.14160671
.06355205

The most frequently provided user feedback was "help me with Chapter 3, not just Chapter 4".

Most users will struggle to select and defend an approach, so that's what this next update will focus on.
What's new: Select your variables, click "test" and it will run the ADF test in level and first difference. It will tell you I(0), I(1), or I(2), and what that means for your model. Word friendly version. One click conversions. Log, first difference, % change, lag. For anything non-stationary there is a 'Use Δ' button. Built-in CBN data. 43 series: inflation MPR exchange rate reserves money supply, GDP growth, etc., yearly quarterly or monthly. You see the years you can select before you select them. Method suggestion: It considers your data and informs you whether OLS, logit, or a time-series model is more appropriate, and why. I also fixed a bug where the stationarity test was selecting too many lags on short annual samples and was producing incorrect results.

It now selects lags in the same manner as EViews. Not there yet: the ARDL bounds test and panel data.

Both are coming soon. Still free, no sign-up.

If you try it out and get a result that seems off, in the long-term or in the statistics, please comment.

Try it : https://getstatmate.com

I'm scoping out a research project on Dutch disease and want to avoid the typical aggregate macro estimation (commodity boom, real exchange rate appreciation, manufacturing contraction), which is already quite saturated in the empirical literature. I'm interested in exploring more innovative or under-researched angles. What methodological approaches do you think still have significant gaps and would add genuine value to the current discussion?

Hey everyone!
I recently started my degree and want to make a project.
The goal is to do a project for the sake of it, I don’t expect it to boost my CV or anything, I just want to make some practice outside my course work.
What is the simplest econometrics project you can think of?
I know basic python and pandas, generally familiar with how OLS works, never implemented 1 tho.
Maybe try an OLS but something interesting to apply to.
I understand that I might be a stupid question but as I said, I’m very early in my career and studies and just want to do a little extra.
Thank you and hope you’re having a good day!

Hey everyone, I recently finished my FSc and I’m planning to start university, but I’m confused about whether I should pursue economics or statistics .I have very little knowledge about either field, so I’m not really sure what the coursework, career prospects, or difficulty of each degree are like.
For people who have studied either of these degrees, Which ine would you recommend and why ? I’d especially appreciate advice about job opportunities and career prospects after graduation.Any advice or personal experiences would be greatly appreciated.

NOW this is a theoretical question/tought, when we collect data we assume that it comes from a multivariate distribution aka multiple random variables on a probability space formed by omega, the sigma algebra and the probability measure. So we basically observe one w that belongs to the sample space, this little omega mapped on our theoretical multivariate distribution will give us our data set, so for example the sample space could be all the possible states of the world. Our job as an econometrician is to try to guess the functional form of the distribution behind the data generating distribution, so we basically are trying to estimate the multivariate joint cumulative distribution of our sequence of random variables X1,X2,….Xn. Its like there is some kinda “gambling” game,We just observe the Numbers and we are trying to Infer informations about how this random Numbers are generated. Thats how I look at this

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.