How Our Stagflation Scenario Probabilities are Derived Using Multinomial Logistic Regression
What is Logistic Regression (LR)? What it does: It predicts the probability of an outcome (like yes/no, default/no default) from input features (like income, age, credit score). Instead of drawing a straight line through the data, it fits an S‑shaped curve that maps any input to a probability between 0 and 1. Interpreting the image above Feature space: Think of each axis as one standardized input (e.g., core CPI z-score vs. unemployment z-score). Real models use more than 2 features; this is a 2D illustration. Multinomial logistic regression: For each scenario k, it learns a linear score Score_k = w_k · x + b_k. Applying softmax to all four scores yields a probability surface per class. Boundaries: The class decision boundaries are linear (straight lines in 2D), but probabilities change smoothly across them, which is the S-curve behavior generalizing to multiple classes. Why 4 classes but only 2 features in the chart The model has four scenarios (classes): Inflation-first, Soft-l...