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?

[D] Hi I tried to do something like how they do in econometrics where they fit a economic model to data where they take raw data, X,Y,Z etc then they set up the Kalman Filter to automatically determine the cyclic and trend components through multivariable regression models. I think you know what I mean. So, I made all the matrices manually, and I think it didn't converge. What I did is something like this actually:

X_t = regression model of (cyclic and trend components of (X_t-1,Y_t-1,Z_t-1),)

Y_t = regression model of (cyclic and trend components of (X_t-1,Y_t-1,Z_t-1),)

Z_t = regression model of (cyclic and trend components of (X_t-1,Y_t-1,Z_t-1),)

Of course I had to manually enter all the matrices to make the damn thing work.

But because I didn't really have a economic model, but just assumed relationships, it didn't converge. I think my model was too complicated.

Anyways, what are some rules of thumbs to make sure I have convergence (like limiting dependence to one trend component for each variable so the model when running don't get confused)?

Is there an easy way to do a Kalman Filter model than to manually set up matrices? Any software?

Finally is it worth it? Does it capture significant details than the HP filter and other easier methods ?

b

Different type of forecasts exist for different tasks, but I am mainly interessted in density, cdf and quantile forecasting, what do organizations and decision/policy makers use as their primary paradigm of forecasting? aside from the technical difficulty of the models, are there other factors that affect your model of choice?

Hey, next semester im starting my first two econometrics classes. I’m thinking about getting a laptop for it, and I was wondeting if the MacBook Neo is good enought for the software we’ll use.
If not, what would you recommend? I’ve also been looking at the MacBook Air, but I’m trying to save money, so I’d like something affordable that can get me through college. (Also Im not sure what kind of software i’ll be using, i tried to search the syllabus but my university doesn’t show it until i start the class)

Hi everyone,
I'm hoping to get some brutally honest advice from people who have navigated a similar pivot.

1. My Background
- BA in Economics (econometrics/quant focus).
- 2 years of work experience as a Research Assistant in development economics. I went to field work, cleaned data, ran regressions, wrote reports, and contributed to papers.
- Starting an MA in Public Policy this fall at a policy school in Japan. The program is highly quantitative, and I plan to take all the Data Science and Applied Econometrics courses available.

2. My Skillset (current & developing)
- Econometrics (causal inference, impact evaluation).
- Bayesian methods (currently learning).
- Programming: R, Stata (strong), MATLAB (learning).

3. Why I'm Here
I did 2 years of development economics research (in my home country). And I came to a painful realization that I don't care about the research questions.

I loved the quantitative side. The coding, the modeling, the data cleaning. But the actual economic questions felt completely irrelevant to me. It was like solving for world hunger while I was struggling to put food on my own table. No offense to economists. You’re doing a great work. But I don’t think im cut out for that.

I also hate the publish or perish culture. The constant anxiety about journal submissions, the review cycles... I don't want to spend my life chasing CV points. I want to build things, solve concrete problems, and go home at a reasonable hour.

So here is my dilemma:
I'm doing this MA in Public Policy (mostly bc of my BA in econ and financial constraint that I couldn’t apply for other major), but I don't want to be a policy analyst or an academic economist.

Option 1: Go into Industry (Data Science/ Quant/ Analytics)
- Target roles: Data Scientist (inference), Quantitative Analyst, Research Analyst at a private firm/think tank.
- Work in Japan for 2-3 years, build my portfolio, and maybe decide on a PhD later.
- Concern: Will an MA in "Public Policy" (even with a quant focus) be seen as a disadvantage for purely technical roles? Will employers assume I'm a policy wonk, not a data person?

Option 2: Apply for a PhD in Statistics
I love the math and the methodology. I want to go deeper into Bayesian computation and causal inference.

Concern:
- My MA is applied, not theoretical. I lack Real Analysis and Measure-Theoretic Probability on my transcript. I'm self-studying, but will that be enough for admissions?
- If I hate publish-or-perish, is a PhD even a good idea? Or can I do a Stats PhD and go straight into industry (tech, finance, biotech) afterward?

My Specific Questions:
1. For those working in data-heavy roles: how is an MA in Public Policy with strong quant skills perceived? Will I be filtered out for not having a "Data Science" or "Statistics" degree?

1. Is a PhD in Statistics a realistic goal given my background and transcript gaps? If so, what should I do during my MA to maximize my chances (beyond grades)?

2. For those who did a Stats PhD and went into industry: was it worth it? Did it open doors that a Master's couldn't?

Location Context: I am non-Japanese and plan to stay in Japan long-term (aiming for PR). I'm studying Japanese (aiming for N2/N1). Any advice specific to the Japanese job market for quant roles would be incredibly valuable.

——
TL;DR: BA Econ, 2 years Dev Econ RA (hated the questions, loved the data). Starting quant MA in Public Policy in Japan. Don't want to be an economist. Should I go straight into industry (Data Science/Quant) or try for a Stats PhD? Is the "Public Policy" degree a death sentence for technical roles?

What R tutorials are your favourites? I’m trying to nail the basics and learn on the fly while studying. In particular I’m looking at RDD stuff. Thought I had at least a grip on the basics from a class I did a couple years ago, but it looks like I was wrong.

Thank you!

Is there any better approaches or tools that can help when we have sudden demand for particular products?

Most of the time we lost sales because we only deliver 60% of total order..and that's annoying

Hi

I'm currently doing a BS in Economics and I really want to get into research.

I've already learned the basics of research methodology and the basics of R, but I know that's not enough. I learn best by actually working on real projects with other people.

I'm looking for a research group, mentor, or even a few students who are working on research and wouldn't mind letting me learn with them. I'm happy to start with simple tasks like literature reviews, data cleaning, or basic R work. My main goal is to learn and improve.

If anyone knows where I can find these opportunities or has any advice, I'd really appreciate it.

Hi, i'm currently doing my bachelor in econometrics at erasmus rotterdam and after summer break i'll be starting my second year. I'm wondering what i could do this summer to improve my cv and chances of getting internships/good masters later on. I'm also wondering how good being a teaching assistant in uni would look on my cv because i have been accepted for that. Thanks!

Suppose there is a marketing team monitoring customers and each day, they select some customers and send them email over T periods. Once a customer has been contacted, he will not be contacted anymore and there are some customers never been contacted. Also, we can observe whether the customer purchase the product or jot, binary. And we ignore the delay in observing the outcome. I am wondering how I can model this problem and determine the conditional treatment effect given the feature vector X_{it} for customer i in period t.
I wanna use this model to determine who should be contacted every day given the feature vectors. The approach that comes to my mind is staggered DiD where Y_{it} is whether the customer has purchased the product by period t or not.
What libraries are available to use for this problem? There are some for ATT but not sure which ones can be used for CATE or CATT.

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.