Recession Probability Models: How Economists Forecast Downturns

Recession probability models convert current economic data into numerical probability estimates of recession over a specified time horizon (typically 12 months). The models combine one or more leading indicators with statistical or econometric techniques to produce probability outputs that financial-media coverage and policy discussions widely cite. The most widely tracked models include the Federal Reserve Bank of New York's yield-curve probit model, Bloomberg's monthly economist consensus survey, internal models from major investment banks (Goldman Sachs, JPMorgan, Bank of America), and the Atlanta Fed's GDPNow current-quarter GDP nowcast. As of April 2026, the major models cluster in the 25-40 percent range for 12-month-ahead recession probability.

The convergence around 30 percent is elevated above historical norms (the long-run average across these models is approximately 15-20 percent) but well below the 60-80 percent readings that have characterised the months immediately preceding actual recession starts. The dispersion across models is itself useful information: when models cluster tightly at high or low readings, the consensus signal is stronger; when models diverge, the underlying economic conditions are more ambiguous. The current 25-40 percent cluster is a relatively tight range, suggesting reasonable consensus about elevated but not imminent recession risk.

The NY Fed Yield-Curve Probit Model

The Federal Reserve Bank of New York's recession-probability model is based on the 10-year minus 3-month Treasury spread, originally developed by economists Arturo Estrella and Frederic Mishkin in research published through the 1990s. The model is published monthly at the New York Fed website and is widely cited in financial-media coverage of recession risk. The methodology is publicly documented and reproducible, distinguishing it from many proprietary investment-bank models.

The technical specification uses a probit regression to convert the monthly average yield spread into a probability estimate of recession within the next 12 months. The estimation is based on monthly data since 1959. The yield-spread coefficient is the dominant input; the model produces a baseline probability of approximately 5 percent when the spread is positive and at its long-run average (approximately 1.5 percentage points), and rising probabilities as the spread narrows or inverts. At the 2023 peak inversion of approximately negative 180 basis points, the model read approximately 70 percent recession probability for the following 12 months.

The current 28 percent reading reflects the post-2025 yield-curve un-inversion. The 10Y-3M spread is now positive at approximately +20 basis points, well above the inversion threshold but still below the long-run average. The model has been declining steadily since the 2023 peak as the curve re-steepened. The probability remains elevated above historical norms but has moved out of the immediate recession-watch zone that the deep 2022-23 inversion produced.

The model's historical track record is reasonable but not perfect. The model exceeded 30 percent recession probability in advance of every postwar US recession since 1968. Average lead-time has been approximately 12 months. The 2024-25 episode (sustained model readings above 30 percent for an extended period without an actual recession on the historical schedule) is the largest false-signal episode in the model's history, comparable to the broader yield-curve false signal of the 2022-25 inversion period.

The Bloomberg Economist Consensus

Bloomberg surveys approximately 75 professional economists each month, asking each economist for a 12-month-ahead recession probability estimate. The published consensus is the median of the responses (with mean and quartile data also reported). The economists range from major Wall Street investment banks (Morgan Stanley, Goldman Sachs, JPMorgan, Bank of America, Citigroup) to academic economists at universities, to economists at the IMF, World Bank, and OECD.

The survey provides a useful aggregation of professional judgement that incorporates information beyond what any single quantitative model captures. Economists draw on multiple inputs (yield curve, jobless claims, manufacturing PMI, consumer confidence, geopolitical conditions, fiscal-policy stance, credit markets) and their estimates reflect both quantitative analysis and qualitative judgement about how the various factors interact. The current 35 percent consensus is moderately elevated, reflecting the mixed signals across underlying indicators.

The historical performance of the Bloomberg consensus is reasonable but not exceptional. Economist surveys tend to be conservative, avoiding extreme readings (above 70 percent or below 10 percent) even when conditions might warrant them. The consensus also tends to be backward-looking, adjusting estimates after the underlying economic conditions have changed rather than anticipating changes ahead of time. The 2007-09 recession was preceded by Bloomberg consensus readings around 30 percent (relatively elevated for the survey) but the consensus did not exceed 50 percent until the recession was clearly underway.

Investment Bank Internal Models

Major investment banks maintain proprietary recession-probability models that combine multiple inputs in firm-specific frameworks. Goldman Sachs Research publishes its model output through periodic notes to clients (with a roughly monthly publication cadence). The current Goldman model reads approximately 30 percent. JPMorgan Asset Management publishes a similar model output with the current reading at approximately 33 percent. Bank of America publishes its model with a current reading of approximately 32 percent. The investment-bank models tend to cluster around the same range as the published Fed and Bloomberg measures.

The proprietary character of investment-bank models limits external evaluation of methodology. The general framework typically involves multiple inputs (yield curve, jobless claims, ISM manufacturing PMI, consumer confidence, credit spreads, financial conditions) combined through regression or factor-model techniques. The exact weights and time-horizon specifications vary by firm. The models are typically updated monthly or quarterly with revised input data.

The dispersion across investment-bank models tends to be modest (typically within 5 to 10 percentage points), reflecting that the underlying economic conditions are reasonably similar to the inputs that the public Fed and Bloomberg models use. Substantial divergence between investment-bank models would suggest either methodological differences mattering or proprietary information being incorporated; in practice, both occur to a limited degree but the headline outputs typically cluster.

The Atlanta Fed GDPNow Nowcast

The Federal Reserve Bank of Atlanta publishes the GDPNow nowcast, a current-quarter GDP estimate updated continuously as new data becomes available throughout the quarter. The nowcast is not a recession-probability model in the traditional sense (it does not produce a probability over a future horizon) but rather a real-time estimate of the GDP figure that will eventually be reported for the current quarter.

The GDPNow methodology uses a Bayesian state-space framework that updates the GDP estimate as new monthly indicators are released (PCE deflator, retail sales, construction spending, ISM manufacturing data, factory orders, employment situation). The estimate evolves through the quarter from an initial broad estimate to a more precise estimate as more data accumulates. The final GDPNow estimate (typically published a few days before the official BEA advance estimate) has a strong historical track record of approximately matching the BEA initial GDP estimate.

As of April 2026, the GDPNow estimate for Q2 2026 real GDP growth stands at approximately 1.8 percent (annualised). The reading is positive, suggesting the current quarter is not contracting. Recession-monitoring application of GDPNow involves watching for sharp downward revisions during a quarter (which would suggest a developing contraction) and for two consecutive quarters of negative GDPNow estimates (which would partially satisfy the two-quarter rule even before official BEA data is published). Neither pattern is currently present.

The Sahm Rule as Real-Time Coincident Indicator

The Sahm rule is a coincident indicator rather than a probability model. It triggers a binary signal (recession yes or no) when the three-month moving average of the US unemployment rate rises 0.50 percentage points or more above the lowest three-month average from the prior twelve months. The current April 2026 reading is 0.47, just below the trigger threshold.

Some practitioners interpret the Sahm rule reading as roughly equivalent to a probability estimate (a 0.40 reading might be interpreted as a 40 percent recession-imminent reading). Sahm herself has cautioned against this interpretation, noting that the rule is designed to function as a binary trigger rather than a continuous probability. The cleanest framing is that the Sahm rule and the probability models are complementary: the probability models forecast the chance of recession over a forward window, while the Sahm rule and other coincident indicators (jobless claims, manufacturing employment, real personal income) provide real-time monitoring of whether the recession that the probability models are forecasting has actually begun.

The combination of probability-model readings (around 30 percent) and Sahm rule reading (0.47, just below trigger) provides a reasonably consistent picture: elevated recession risk over the coming year, but not yet recession-imminent. If the Sahm rule crosses 0.50 in coming months, the probability-model readings would likely rise toward 50-60 percent as economists update their judgement. If the Sahm rule stalls or declines, the probability-model readings would likely drift back toward 20-25 percent.

Why the Models Disagree

Different models use different inputs, different time horizons, and different statistical methodologies, all of which can produce different numerical estimates from the same underlying economic conditions. The NY Fed yield-curve model uses a single input (the 10Y-3M spread) with a long historical track record but is purely backward-looking econometric estimation. Bloomberg consensus aggregates judgement that incorporates many inputs but is less mechanically reproducible. Internal investment-bank models combine multiple inputs in proprietary frameworks. The Atlanta Fed GDPNow nowcast tracks current-quarter GDP estimates rather than predicting future recession.

The dispersion across models is itself useful information. When models cluster tightly, the consensus signal is stronger and the confidence in the underlying assessment is higher. When models diverge, the underlying economic conditions are more ambiguous and individual model readings should be interpreted with greater caution. The current 25-40 percent cluster is moderately tight, suggesting reasonable consensus about elevated but not imminent recession risk. The dispersion was much wider in 2022-23 (when yield-curve-based models read 60-70 percent while other models read 30-40 percent), reflecting the central interpretive question of how much weight to assign to the unusually deep yield-curve inversion.

The 2024-25 False-Signal Episode

The 2024-25 cycle has been the most challenging period for all major recession-probability models. High recession-probability readings through 2023-24 (NY Fed model peaked above 70 percent; Bloomberg consensus reached 50 percent; investment-bank models clustered at 50-60 percent) were followed by no actual recession on the historical schedule. By 2026, the probability readings have come back down to the 25-40 percent range without a recession having occurred in the predicted window.

The false-signal episode is the largest in the modern history of these models. The yield-curve-based models were particularly affected because the 2022-25 yield-curve inversion was the longest in the modern monitoring period. The Bloomberg consensus and investment-bank models were less acutely affected because they incorporate broader inputs that did not all align with the yield-curve signal. The Atlanta Fed GDPNow has not produced false signals because it tracks current-quarter conditions rather than forecasting future recession.

The implications for future model interpretation are significant. If the 2024-25 episode is a one-off (driven by the unusual COVID-era cyclical dynamics that have not occurred before), the models may continue to function reliably going forward. If the episode reflects structural changes in the modern services-dominant US economy (manufacturing weight in the LEI, service-sector resilience to monetary tightening, household-balance-sheet buffers from COVID-era stimulus), the models may need methodological refinements to function as effectively in future cycles. Either interpretation will be testable as more data accumulates through 2026 and beyond.

Practical Application of Probability Readings

Probability readings are inputs to decisions, not decisions themselves. A 30 percent recession-probability reading is meaningful but does not by itself determine appropriate household, business, or policy responses. The relevant question is what action thresholds different stakeholders should apply. For households, modestly elevated recession probability might motivate maintaining or modestly increasing emergency-fund balances, accelerating high-interest debt repayment, and reviewing employment portability and skill-development priorities. For businesses, elevated readings might motivate maintaining cash reserves, postponing major capacity expansion, and ensuring credit-line availability. For investors, elevated readings might motivate rebalancing toward defensive asset classes for those near retirement, but not for younger investors with long-horizon dollar-cost-averaging strategies.

The 25-40 percent current cluster is in the range where prudent moderate adjustments are warranted but aggressive defensive positioning is not. If the readings rose to 60-70 percent in coming months, more substantial adjustments would be appropriate. If the readings declined to 15-20 percent, the probability framework would be back to baseline historical norms.

For the underlying single-indicator deep dives, see the Sahm rule, the yield curve indicator, initial jobless claims, ISM manufacturing PMI, and the Conference Board LEI. For the live current 2026 dashboard, see current probability 2026.