Affiliation:
1. UCLA Anderson Graduate School of Management Los Angeles California USA
Abstract
AbstractThis paper proposes a method of forecasting US recessions beginning with data displays that contain the last 12 quarters of seven US expansions. These end‐of‐expansion displays allow observers to see for themselves what is different about the last year before recessions compared with the two earlier years. Using a statistical model that treats this historical data as draws from a 12‐dimensional multivariate normal distribution, the most recent data are probabilistically inserted into these images where the recent data are most like the historical data. This is a recession forecast based not on presumed patterns but on patterns revealed by the data.