Our machine learning based calculation of Monthly Recession Probabilities for the United States are obtained from a Support Vector Machine applied to four monthly coincident variables: non-farm payroll employment, the index of industrial production, real personal income excluding transfer payments, and real manufacturing and trade sales. Our work was inspired by Chauvet (1998) who developed the Smoothed U.S. Recession Probabilities, and is published by The Federal Reserve of St. Louis. Our probability index is an alternative using statistical learning methods. This index of recession probabilities is built using Support Vector Machines over the same monthly data on coincident indicators.
Numbers represent search interest relative to the highest point on the chart for the given region and time. A value of 100 is the peak popularity for the term. A value of 50 means that the term is half as popular. Likewise, a score of 0 means the term was less than 1% as popular as the peak.
See the location in which bitcoin was most popular during the specified time frame. Values are calculated on a scale from 0 to 100, where 100 is the location with the most popular as a fraction of total searches in that location, a value of 50 indicates a location which is half as popular, and a value of 0 indicates a location where the term was less than 1% as popular as the peak.
Note: A higher value means a higher proportion of all queries, not a higher absolute query count. So a tiny country where 80% of the queries are for “bitcoin” will get twice the score of a giant country where only 40% of the queries are for “bitcoin”.
The following graph is made possible by Brad Merfeld of Cal Poly Pomona, it exhibits the growth in prices (statewide) from 2012-2017 based on Housing Price Index.