The Smoothed U.S. Recession Probabilities (SURP) is a noteworthy indicator that quantifies the likelihood of the United States being in a recession during a given month.
By analysing key economic variables through a dynamic-factor Markov-switching model, the SURP provides a percentage probability of recession, offering a digestible yet robust measure of the nation’s economic health.
Its historical performance has shown a credible correlation with actual recessionary phases, making it a valuable tool for economists, policymakers, and market analysts.
No indication of Recession ❌
The Background
The Smoothed U.S. Recession Probabilities (SURP) is the brainchild of economists Marcelle Chauvet and Jeremy Piger, who designed a method to assess the recessionary state of the U.S. economy.
This method involves a dynamic-factor Markov-switching model applied to specific monthly coincident variables. It’s a sophisticated yet practical approach that encapsulates the multifaceted nature of the economy, translating complex data into a single percentage probability of recession.
The core of this model is its focus on four crucial economic variables:
- Non-farm payroll employment
- The index of industrial production
- Real personal income excluding transfer payments
- Real manufacturing and trade sales.
These variables have been chosen due to their sensitivity to economic conditions and their capability to reflect changes in the economy accurately.
By analyzing these variables, the SURP sheds light on the underlying economic activities and provides a quantitative measure of recession probabilities.
Over time, the SURP has proved to be a reliable indicator, aligning well with historical recessionary periods in the U.S., thus gaining recognition and adoption within the economic and financial communities.
The metrics
| Indicator | Current Status | R? |
|---|---|---|
| Probability over 5% | Less than 1% | ❌ |
The primary metrics fed into this model include Non-farm Payroll Employment, the Index of Industrial Production, Real Personal Income Excluding Transfer Payments, and Real Manufacturing and Trade Sales.
These metrics are chosen due to their sensitivity to economic conditions and their ability to reflect changes in the economy accurately.
- Broad Economic Representation:
- These metrics cover a broad spectrum of economic activity including employment, industrial production, personal income, and sales in manufacturing and trade. They encapsulate various sectors of the economy, providing a well-rounded view of economic performance.
- Sensitivity to Economic Changes:
- These variables are sensitive to changes in economic conditions. For instance, employment levels react to business cycle fluctuations, and industrial production is closely tied to demand levels. Their sensitivity makes them suitable for detecting early signs of economic shifts.
- Leading or Coincident Indicators:
- The chosen metrics are either leading or coincident indicators, meaning they either precede or move in tandem with overall economic trends. This characteristic is crucial for a model aiming to provide early warnings or timely insights into recessionary conditions.
- Historical Correlation with Recessions:
- Historically, changes in these variables have correlated with economic recessions. Their collective behavior has shown a pattern of change in line with the onset and duration of recessionary periods, validating their selection for the model.
The model combines the selected metrics to generate a single probability measure.
The specific weights assigned to each metric and the detailed mathematical mechanics of how these metrics are combined within the Dynamic-Factor Markov-Switching Model aren’t explicitly outlined in the sources.
The model dynamically adjusts, recognising shifts in economic conditions based on the data from these metrics and transitions between economic regimes accordingly.
Interpreting the SURP entails observing the percentage value it outputs. A higher percentage indicates a higher likelihood of a recession.
For instance, a SURP value of 0.80% in July of 2023 suggests a relatively low probability of a recession, whereas a value nearing or surpassing a pre-determined threshold (e.g., 5%) would signal a significantly heightened probability of recession.
Importantly, this is a heightened probability, it does not have a 100% hit rate.
The Narrative
Each of these variables is a pulse point of the economy, reflecting different facets of economic activity whose collective behaviour paints a picture of the economic terrain, revealing signs of potential recessions on the horizon.
A dip in Non-farm Payroll Employment signifies less income for households, which in turn can lead to decreased consumer spending—a critical driver of economic growth.
This is where you might experience tighter budgets, delay you big purchases, or even a struggle to meet essential expenses. A decline in the Index of Industrial Production can indicate reduced demand, reflecting a downturn in business activity that often precedes economic downturns.
Industries might slow down production due to lower orders, and employees in these sectors might experience job insecurity.
A Lower Real Personal Income (Excluding Transfer Payments) in the real world can constrict our spending further, affecting businesses and, by extension, the broader economy.
You’ll likely find yourself with less disposable income, impacting purchasing power and living standards. Reduced Real Manufacturing and Trade Sales reflect dwindling consumer confidence and demand, often a precursor to economic recessions.
Businesses might see lower sales, and the ripple effect can lead to cost-cutting measures, including layoffs.
As these figures are all combined as a snapshot there is no sequentiality to this model.
One metric could move a lot in the direction of a recession, whilst another keeps the total probability low.
Where are we now?
We’re currently under 1%, which is a baseline that shouts no recession.
This number can jump up quickly, going from <1% to over 5% in a number of months, so we will be watching for any rises carefully.
The average amount of time it takes for the indicator to cross the 5% line, and for a recession to begin (retrospectively announced) is 2.4months. The mode is 1 month.
Credit
The creation and ongoing maintenance of the Smoothed U.S. Recession Probabilities (SURP) model is a significant contribution to the field of economics and recession analysis.
All acknowledgement is directed towards economists Marcelle Chauvet and Jeremy Piger, whose expertise led to the inception of the SURP model.
More of Marcelle: https://sites.google.com/site/marcellechauvet/home
More of Jeremy: https://jeremypiger.com/