Recession probability models translate noisy economic indicators into a single interpretable signal. In this case study I walk through how I built one in R, combining the Neftci (1982) sequential probability method with a Bayesian updating framework, FRED macro series, and Blue Chip survey-based forecasts.
The Neftci method is the academic foundation behind CME FedWatch-style probability tools. It sequentially updates the probability of a regime shift as new data arrives, based on the likelihood ratio between recession and expansion states.
The model pulls macro series directly from FRED (industrial production, employment, real income, and manufacturing/trade sales) and merges them with Blue Chip Financial Forecasts survey data stored in a MariaDB backend.