A statistical look at the world.
Michael Klaster: Leadership and Econometrics – An Unexpected Bridge
After 27 years working as an econometrician I have learned that models often reveal more about the assumptions of the builder than about reality itself. We simplify, abstract, adopt a linear approach and hope that the residuals don't betray us. Yet time and again we see that the real difference between success or failure often lies outside the model. And one factor keeps recurring: leadership.
Simpson's paradox: when more data leads you astray
Imagine you are evaluating the effect of a job training program across genders. In both men and women separately, the program seems to help; but when you pool the data, the effect vanishes or reverses. That’s not just annoying, it can completely mislead your inference if you don’t look out for lurking variables. Welcome to the simpson’s paradox, a really weird and frustrating statistical phenomenon.
The Balance Within Chaos
Imagine a simple pendulum: a weight hanging from a fixed point. When it hangs straight down, it is perfectly still. This is its point of stable equilibrium, the position it naturally returns to whenever it is moved. Push it gently to one side, and it swings back and forth, trading height for speed, before slowly coming to rest again. Even while moving, it stays in balance because the forces acting on it work together.
Why negative feedback is better for improvement (or is it?)
In the years after World War II, a group of Israeli Air Force instructors believed they had discovered a hard truth about human nature. They noticed something strange during flight training. When a pilot executed a perfect maneuver and received praise, the next attempt was usually worse. But when a pilot performed badly and was harshly criticized, his next flight tended to improve. It felt like solid evidence. Praise seemed to weaken people, while criticism toughened them up. Many instructors became convinced that tough feedback was the only way to improve performance. The conclusion was neat, logical, and entirely wrong.
The Edge of Logic: Reflections on Gödel’s Incompleteness Theorems
In the early 20th century, a young Austrian logician named Kurt Gödel did something remarkable. He proved a pair of theorems that would shake the foundations of logic and forever alter how we think about truth. These findings, now known as Gödel’s Incompleteness Theorems, revealed something deeply unsettling: in any reasonable formal system powerful enough to describe basic arithmetic, there will always be true statements that cannot be proven within the system itself.
Forecasting the End: Brandon Carter’s Doomsday Argument and Its Econometric Implications
In the world of econometrics, we're accustomed to predicting recessions, estimating causal impacts, and modeling long-term growth. But what if the tools of probabilistic reasoning could also offer insight into humanity's own lifespan? This is the bold proposal behind physicist Brandon Carter’s Doomsday Argument, a theory that, while grounded in philosophical probability, has clear implications for statistical reasoning and long-term forecasting.
Looking back at the VII VESTING Lustrum: Voyage
I hope you all had an incredible time during the lustrum week this past April. For those who couldn’t be there, this is the perfect chance to catch up on all the unforgettable activities that took place. And for those who were there—it’s time to relive some of the memories that made the week so special.
Will Machine Learning Replace Econometrics?
Econometrics used to be the gold standard for making sense of economic data, relying on carefully structured models, causal inference, and transparent relationships. But machine learning doesn’t play by those rules. It skips the theory, digests mountains of data, and delivers startlingly accurate predictions. As industries crave speed and scale, one question is becoming harder to ignore: do we still need econometrics at all?
Can Machine Learning Predict the Champions League Winner?
Every year, football fans around the world passionately debate which club will win the UEFA Champions League. Experts argue over tactics, fans praise their favourite players, and bookmakers adjust the odds after every match. But what if we could go beyond opinion and use data to make predictions? That’s exactly what machine learning tries to do.
Study Harder or Smarter?
Every student has faced the dilemma: do I pull an all-nighter before an exam and reread the lecture slides for the third time? Or do I make sure to get a good night’s sleep? What is more important? Does more study time lead to higher results? Or does the way I study matter more? Let us explore whether the key to academic success is studying harder or smarter.