Many of you have seen this image on the Internet — I’ve seen it myself a few times on LinkedIn lately. People say it depicts the “Dunning-Kruger” effect… But did you know this is actually an internet meme with little to do with the original paper? Here is one of the recent examples, a screenshot … Read more
Sometimes I see people referring to a “deterministic” forecast, and I have some personal issues with this. Because if you apply a model to data then there is nothing deterministic about your forecasts! In many contexts, “deterministic” has a precise meaning: no randomness, no uncertainty. A deterministic solution to an optimisation problem (e.g. linear programming) … Read more
Teaching statistics as a flipped classroom with the help of AI? You heard that right! That’s exactly what I tried this year – and here are the results. Attached to this post is the student evaluation score for the module. Yes, the number of responses is quite low (only 50% of the cohort), but it … Read more
Everything is random! Your data, your model, its parameter estimates, the forecasts it produces, and even the minimum of the loss function you used. There is no such thing as a “deterministic” forecast – everything is stochastic! Whenever you work with data, you are working with a sample from a population. In some cases, this … Read more
Image above depicts a fashion model making a presentation about a forecasting method. I like the forecast for the final period in that image… Over the last few years, I’ve seen phrases like “LightGBM model” or “Neural Network model” on LinkedIn many times, and the statistician in me shivers every time. So, I figured it’s … Read more
One thing that bothers me when I read posts on social media or papers in peer-reviewed journals is the claim that a proposed approach is “assumption-free.” In forecasting, this is never true. Such an approach is like a spherical unicorn in a vacuum (see image above). Here’s why. Every model is a simplification of reality, … Read more
One of the core ideas in statistics, which extends to many other fields including forecasting, is the concept of structure versus noise. You’ve probably heard of it, but it’s often overlooked by those without a strong quantitative background. So, let’s discuss. The core of the idea is that any data consists of two fundamental parts: … Read more
Back in 2022, my father asked me to help him in amending and editing a monograph he wrote on the topic of “Complex-Valued Econometrics”. The original book focused on dynamic models, but after looking through the material and a thorough discussion, we decided to write something more fundamental. The monograph is based on the research … Read more
While there is a lot to say about multistep losses, I’ve decided to write the final post on one of them and leave the topic alone for a while. Here it goes. Last time, we discussed MSEh and TMSE, and I mentioned that both of them impose shrinkage and have some advantages and disadvantages. One … Read more
As we discussed last time, there are two possible strategies in forecasting: recursive and direct. The latter aligns with the estimation of a model using a so-called multistep loss function, such as Mean Squared Error for h-steps-ahead forecast (MSEh). But this is not the only loss function that can be efficiently used for model estimation. … Read more