Does location matter in your data? Then join our live online course Spatial Cross-Sectional Econometrics Using @Stata to LEARN how to MODEL it PROPERLY. You will learn how to: - Build and row-standardise spatial weights matrices - Test for spatial autocorrelation with Moran’s I and LISA - Estimate SAR, SEM and SDM models by ML and GMM - Interpret direct, indirect and total (spillover) effects It's a HANDS-ON course with Stata’s official sp suite: spset, spmatrix and spregress. Ideal for PhD students, researchers & analysts in economics, regional science, geography, political science and policy institutions. 👉 Info & registration: bit.ly/4BoH3Nu #Stata #SpatialEconometrics #SpatialData #SpatialStatistics #Econometrics #EconTwitter #RegionalScience #EconomicGeography #GIS #PhDChat #AcademicTwitter #DataScience

Start of a NEW academic year, NEW research to run, data to analyse and based in Greece? Then your university may already give you FREE access to @Stata! Check below to see if you currently have access to a #Stata19 Departmental or Campus licence 👇

📣 Updated Call for Papers: the FIRST Greek Stata Users Conference 📍 Athens University of Economics and Business (AUEB) 🗓️ 28–29 January 2027 ⏳ Abstract deadline: 1 December 2026, one month to go! We are bringing the Stata community to Athens to present new commands, applied research, and to interact with the people who develop Stata. 🎤 Invited speaker: Demetris Christodoulou (University of Sydney) 🔧 From @Stata: • Jeff Pitblado on building publication-ready tables with the collect suite • Kristin MacDonald on causal mediation analysis with Stata 19's mediate command Looking for: ✅ New user-written commands and routines (estimation methods, Python/R/Java integration, dashboards, data visualization) ✅ Tips & Tricks (5–15 minute talks on things that make life easier in Stata) ✅ Applied studies using the latest Stata 19 methods @🎓 PhD students and early-career researchers: we have a dedicated POSTER SESSION for ongoing work, preliminary results, and creative uses of data. An opportunity to get feedback and make connections. The three best posters win Stata licences, training and Stata Press books. 💶 TStat is also fully sponsoring TWO PhD students (conference plus training course). 🛠️ Day 2 is dedicated to an optional hands-on workshop, "Automating your Research in Stata," led by @cerullig (CNR-IRCrES). Abstracts and posters are welcome in Greek or English. 📩 Submit to [email protected] by the 1st December 2026. Scientific Committee: Seraina Anagnostopoulou (University of Piraeus), Una-Louise Bell (TStat), Anna Chaimani (University of Oslo), Demetris Christodoulou (University of Sydney), Andrianos Tsekrekos (AUEB), Elias Tzavalis (AUEB) 🔗 More info @ bit.ly/46ReGtm Please share with colleagues, students, and anyone who lives in a do-file 🔁 #Stata #Stata19 #CallForPapers #Econometrics #PhDLife

What if #meta-analysis is not just for combining study results, but for explaining why firm-level parameters differ in the first place? Maria Elena Bontempi (Univ. Bologna) repurposes @Stata's multivariate meta-analysis toolkit: estimating first firm-specific leverage-adjustment dynamics, then using meta-regression as a second-stage model to explain that heterogeneity via firm characteristics & debt covenants, all whilst handling correlated outcomes and estimation uncertainty, without the overparameterization of pooled interaction models. Presentation available @ bit.ly/4hcBQ1M

National averages hide the story — smoking and risky drinking don't look the same in Puglia as they do in Trentino! Giovanni Capelli et al. (Istituto Superiore di Sanità) map Italy's regional health divides using PASSI surveillance data: weighted regional prevalences via Stata's svycommand, visualized through thematic maps with quintile-based classificationinstead of fixed thresholds — sharpening geographic contrast and revealing territorial inequalities that national benchmarks tend to mask. Read more at bit.ly/4ha1qVn

Raw Kaplan-Meier curves can be misleading in observational studies: a treatment group might just look worse because it's older or sicker at baseline, not because the treatment itself is harmful. Rino Bellocco (Univ. Milano-Bicocca & Karolinska) currently walking us through fixing this in Stata: from unadjusted KM curves, to Cox-adjusted hazard ratios, to fully standardized survival curves that compare "what if everyone got treated" vs. "what if no one did" — giving a real, interpretable causal contrast on the survival scale, not just a hazard ratio. Presentation available at bit.ly/4yO2Vjf