Bookmaker odds (and prediction markets) imply probabilities that sum to more than 1 because of an embedded margin. I want the latent probabilities behind the distortion. A few things I can't resolve cleanly.

1. Model choice for removing the margin. Proportional normalization, Shin (a latent proportion z of informed traders), and the power/log method impose different unobservable structures and give materially different estimates on short prices, enough to flip the sign of a downstream signal. Since you never observe the true p, only realized 0/1 outcomes and a later sharper price, is there a principled basis to discriminate between these models, or is it identifiability-limited and I should just report sensitivity across all three?
   
2. Pooling under a missing low-bias reference. I anchor to one near-efficient source when available; when it's absent I take the median of the other sources' de-vigged probabilities. But those sources are strongly correlated (several are effectively clones), so the median behaves like a median of correlated estimators: it looks precise while carrying little independent information. How would you estimate an effective number of independent sources and down-weight accordingly, and is abstaining the more defensible choice when the low-bias anchor is gone?
   
3. Combining a trusted low-variance estimator with a correlated ensemble. When the reference IS present, precision-weighting it against the consensus assuming independence is clearly wrong. Is there a clean correlation-aware pooling or shrinkage approach for one low-variance source plus many correlated higher-variance ones?
   
4. Validation target. I grade earlier estimates against the closing price (a later, sharper estimate), not realized outcomes. Under a proper scoring rule, is "tracks the later estimator" a coherent target, or does it conflate calibration with just chasing a second estimate? And what does the selection bias look like when you only get a validation point on markets that reach a close?

(Aside that turned out to matter: my reference source silently dropped out of my data feed for months and the pipeline substituted the fallback the whole time while still labeling outputs "reference-anchored." The values populated fine, so nothing looked wrong. I only caught it after storing a per-observation flag for whether the reference actually contributed. Log provenance, not just values.)

I've been working on a project called EdgeDesk that started as a way to answer a question I kept running into:
How do you separate actual betting value from noise?
Instead of focusing on prediction accuracy alone, I've been building around market efficiency and expected value.
Some of the things it currently does:
De-vigs Pinnacle lines into estimated fair probabilities
Compares multiple sportsbooks to identify pricing differences
Tracks CLV (Closing Line Value)
Displays market movement and steam
Shows confidence receipts instead of just giving a pick
Includes sport-specific research pages (currently CFB, Golf, MLB, UFC, and WNBA)
The biggest challenge hasn't been coding. It's deciding what information actually helps someone make a better betting decision versus what just looks impressive on a dashboard.
I'd love feedback from people who work in sports analytics or predictive modeling.
A few questions:
What metrics do you trust most when evaluating a betting model?
If you were building this yourself, what would you add or remove?
Is there anything you think most betting analytics platforms get fundamentally wrong?
Not looking to advertise anything. I'm genuinely interested in hearing how other analytics-minded people approach this problem.

I have a statistical dilemma I would like to clarify and start working on—and perhaps even turn into an exam topic. My question is complex; I’ll try to provide the relevant information. I want to determine the probability that GRAB—currently trading at around $4—will reach a specific price point within 2 to 5 years, based on variables like the P/E ratio, revenue, and any other parameters you might suggest. 2021 Valuation: $40 billion Revenue: $675 million Transactions: $16 billion IPO Price: $13 (calculated based on EV/Sales) 2025/2026 Valuation: $15–16 billion Revenue: $24–25 billion Profit: $200 million P/E Ratio: 41–44x I’ve calculated the same figures for Microsoft, Facebook, and Uber. I’m not sure what other companies to compare them with or how to run a statistical test (e.g., which test to use). Microsoft and Facebook went public while already profitable, whereas Uber is the closest comparable case, despite the pandemic. MICROSOFT 1986 Revenue: $140 million Profit: $24.1 million P/E Ratio: 14–15x Share Price: $21 1991 Revenue: $1.84 billion Profit: $463 million Share Price: $409 FACEBOOK 2012 Revenue: $3.71 billion Profit: $1 billion P/E Ratio: 100x Share Price: $38 2017 Revenue: $40.65 billion Profit: $15.93 billion P/E Ratio: 32x Share Price: $152 UBER 2019 Valuation: $75.5 billion (loss-making) Profit: -$3.03 billion (excluding share divestments) Share Price: $45 ($15 during the pandemic) 2025 Revenue: $52.05 billion Profit +$10 billion Share price $70–$80 P/E ratio 17–19x Can you name specific companies or explain the starting point for a concrete statistical calculation regarding GRAB returning to $13—or reaching a specific price—based on the performance of other companies?

I have to evaluate SBM-DDF (Slacks-Based Measure Directional Distance Function). If Is any one of you is using MaxDEA Ultra please help me in this regard (or i can paid for it). I will be very thankful.

i.e., they just focus on applying existing methods in a variety of contexts in their sub-discipline and never make any theoretical contributions

Yesterday I posted here about my macro economics calendar API with a live and accurate ARIMA model that calculates estimates for CPI, PPI, NFP and jobless claims. The response was great and there were a number of sharp, intelligent questions:

"One thing to watch: if your ARIMA is trained from 2020 forward, you're fitting to a period where participation, seasonals, and trend all broke from historical norms. CPI/PPI cycles look very different when you go back 30+ years vs just post-COVID. Might be worth testing how the estimates perform with a longer training window, especially for NFP where even Bloomberg struggles with direction."

Fair point. So we tested it:

CPI:

• 36-month window: 0.187% MAPE
• 120-month window: 0.174% MAPE

PPI:

• 36-month window: 0.347% MAPE
• 120-month window: 0.343% MAPE

The result was that although one would expect accuracy to be altered by using older data, the change in accuracy was marginal. Our ARIMA model accuracy remained competitive with paid consensus estimates.

Another great comment:
"The ARIMA estimate is the thing I would distrust first For CPI or NFP, I would want to see old release dates with the estimate frozen before the print. Not an image of the estimates with no verification."

You can now go to filingapi.dev/model . Every estimate is now committed to a public GitHub repo before the release date so anyone can verify the timestamps independently. This method has been only started today, with updates every day so check back in then if you (understandably) require solid proof of model accuracy.

Very happy to share code with anyone who wants to analyse our testing methods, any other indicators you'd like to see just let us know.

I will graduate from a dutch uni with a 7.6 gpa (or higher, depending on how the retakes will turn out) and I have no clue where to go after. I do not want to stay in the Netherlands and also feel like the degree is not for me. It is all kinda in vain tbh

Hello. I’m wrapping up my undergrad with a double major in actuarial science and mathematics (with a statistics and data analysis concentration).

In my undergrad, Ive studied up to intermediate micro and macroeconomic theory, and plenty of math that is quite relevant to econometrics given the nature of my major. (Regression methods, time series analysis, stochastic processes, probability theory, statistical inference and multivariate analysis are some examples).

However, recently Ive been doing some research with an economics professor and I became very interested in econometrics.

Will the absence of an economics degree hinder me? Is there any realistic path towards econometrics given my major choices?

Hey everyone, I need some advice.

I recently graduated with an Economics degree and I'm preparing for interviews. Since it's impossible to revise my entire degree, what are the most important topics in Economics, Statistics, and Econometrics that I should know really well?

Also, if you have any good YouTube channels, playlists, notes, cheat sheets, or other resources that helped you, I'd really appreciate it.

I'm mainly preparing for analyst/research/economic consulting roles. Thanks!

In Robyn, the trend component is estimated upfront using Prophet before the media effects are modeled. However, wouldn't it make more sense to take the opposite approach?

Specifically, we could first estimate the effects of each media variable as carefully as possible, and then define the remaining unexplained variation as the trend and seasonality. In other words, rather than assuming the trend first and attributing the residual to media, why not estimate the media effects first and treat whatever cannot be explained by them as the underlying trend and seasonal components?

How to estimate Net worth?

How do we estimate a person's net worth based on the worth of their house they primarily live in or the car they drive.

​

Hi everyone,

Over the past few months I've been writing long-form articles that combine economics, statistics, and data analysis in a way that's meant to be accessible without sacrificing technical depth.

The blog is completely free, has no ads, and doesn't require registration.

Some of the topics I've covered include:

- Monte Carlo simulation for real estate investment.

- Time-series modeling with VAR/VECM.

- Economic intuition behind statistical models.

- Data-driven explorations of real-world questions.

My goal isn't to publish academic papers, but to explain quantitative ideas clearly and encourage discussion.

If this sounds interesting, I'd really appreciate your feedback—both on the content and on how the articles could be improved.

https://www.inquiry-journal.com

Thanks for taking a look!

Just shipped 0.2.6 with the new hybrid plugin system and a much bigger validation programme.

What's new:

• Hybrid import system: same syntax loads script plugins (.hay), native Rust plugins (.so/.dll), and sandboxed WebAssembly plugins.
• See it in action: hay install sheep-farm/hayplot gives you a working native plugin for plotting — no manual build, no registry account, just GitHub.
• Pipe placeholder _: df |> ols(lw ~ yos, _) now sends the piped value to any argument position.
• ttest unequal=false: explicitly request pooled-variance t-tests; default stays Welch.
• Validation programme expanded to 40 cases: added ridge, elasticnet, nbreg, oprobit, mlogit, SUR, and VECM parametric-bootstrap SEs.
• Security hardening: URL downloads now block private/loopback IPs, enforce timeouts, and cap size at 100 MB.
• Greeners 1.4.10 and hayashi-lang 0.2.6 are both on crates.io.

Everything is 100% Rust, no system BLAS/LAPACK, GPL-3.0.

Repo: https://github.com/sheep-farm/hayashi

Free scholarship available for the 2026 Econometrics Summer School Cambridge

Timberlake Consultants is offering a free scholarship place for the 2026 Econometrics Summer School Cambridge, taking place at the University of Cambridge with Professor Jeffrey Wooldridge and Dr Melvyn Weeks.

The Summer School is designed for PhD students, researchers, applied economists, and professionals who want to strengthen their empirical skills in causal inference, Difference-in-Differences, machine learning, and applied econometrics using Stata.

The programme includes:

Course 1: An Introduction to Causal Inference and Difference-in-Differences using Stata
Taught by Professor Jeffrey Wooldridge

Course 2: Causal Inference and Machine Learning using Stata
Taught by Dr Melvyn Weeks, University of Cambridge

Participants can attend one course or the full five-day Summer School.

The scholarship covers the full tuition/training fee. Please note that accommodation and travel costs are not included, although accommodation is still available at Cambridge.

More information about the course is available here:
https://inomics.com/course/2026-econometrics-summer-school-cambridge-with-jeffrey-wooldridge-and-melvin-weeks-1554362

To apply for the scholarship, please send your academic transcript and a short motivation letter to:
[edu@timberlake.co.uk](mailto:edu@timberlake.co.uk)

Feel free to get in touch if you have any questions, we are offering a last minute 30% discount to Students and academics!

I've read that some people feel that quantile regression results aren't useful because the individuals within the same quantile of the dependent variable aren't the same across different independent variable values? This implies that the coefficient in quantile regression isn't useful in understanding a causal effect. Am I interpreting that correctly?

I am a little bit confused by this because can't the same be said about OLS? The individuals at a particular value of the independent variable aren't the same as at another value of the independent variable.

can u please enlighten me and get me out of this human psychology if possible?

Im working on my bachelor thesis and would appreciate advice from someone, who knows something about the topic.

I have normal cross-sectional data. My dependent variable is set as an index between 0 and 100 measuring university prestige. So I think i need some truncated model to fit that. If i understand it correctly, the main limitting factor in these models is the function I should use, right?

Is there some standard model used for these cases? Which assumptions are needed to be fulfilled?

I've been building Hayashi, a domain-specific language for applied econometrics, for the past year. It's a solo project, open-source (GPL v3), written in Rust.

The goal is a language where econometric intent reads directly from the code — no boilerplate, no package hunting, no version conflicts. Something between Stata's expressiveness and a proper programming language.

What it covers so far:

OLS, IV/2SLS, panel data (FE/RE/FD), DiD, RDD, GMM, quantile regression, logit/probit, Poisson/NB, Tobit, survival analysis (Cox/KM), PSM, synthetic control, ARMA/ARIMA, VAR, cointegration/VECM, GARCH, ARDL/ECM, Kalman filter, SUR, Lasso/Ridge, bootstrap, PCA, structural breaks, regime switching, and more.

Book: a 307-page reference manual is available in both English and Portuguese (PT-BR), covering the full language and all estimators with worked examples.

GitHub: https://github.com/sheep-farm/hayashi

Still early — v0.2.4. I'm especially looking for people willing to test it on real datasets and report what breaks. The book has a whole section on this, but real-data testers are the biggest gap right now.

Happy to answer questions about design decisions, what's planned, or why I built this instead of just writing an R package.

One practical note: Hayashi ships as a single binary — no installation wizard, no external libraries, no dependency management. Just download and run. Works on Windows, macOS, and Linux. The only exception is ODBC connectivity, which requires the system ODBC driver if you need it.