Labor Market Observatory
US Payrolls & Labor Market
100 models covering NFP payrolls, sectoral employment, wage growth, hours & productivity, unemployment, labor force, JOLTS, demographics, and recession signals — built from publicly available FRED data. First-print scoreboard methodology · vs FRED
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Cache: 17 Sept 2026Showing 1–10 of 102 charts
Page 1 of 11
Headline Payrolls
1. Total Nonfarm Payrolls
Thousands, SA — All employees on nonfarm payrolls (FRED: PAYEMS)
Frequency: MonthlySeries: 1Last data: 1 Aug 2026
Source: FRED / BLS
@robomacroHeadline Payrolls
2. Total Private Payrolls
Monthly — Private sector payrolls excluding government (FRED: USPRIV)
Frequency: MonthlySeries: 1Last data: 1 Aug 2026
Source: FRED / BLS
@robomacroHeadline Payrolls
3. Monthly Change in Nonfarm Payrolls
Thousands, SA — Net new jobs added each month (FRED: PAYEMS diff)
Frequency: MonthlySeries: 1Last data: 1 Aug 2026
Source: FRED / BLS
@robomacroHeadline Payrolls
4. Total NFP vs Private Payrolls
Total nonfarm vs private-sector employment — government job contribution
Frequency: MonthlySeries: 2Last data: 1 Aug 2026
Source: FRED / BLS
@robomacroHeadline Payrolls
5. Payrolls Recovery Since Feb 2020
Rebased to 100 at Feb 2020 — total vs private vs government
Frequency: MonthlySeries: 3Last data: 1 Aug 2026Rebased: Jan 2024 = 100
Source: FRED / BLS
@robomacroHeadline Payrolls
6. Goods-Producing vs Service-Providing Employment
Structural shift toward services economy (FRED: USGOOD + SRVPRD)
Frequency: MonthlySeries: 2Last data: 1 Aug 2026
Source: FRED / BLS
@robomacroHeadline Payrolls
7. Establishment vs Household Survey Employment
Payroll survey (PAYEMS) vs household survey (CE16OV) — divergence monitor
Frequency: MonthlySeries: 2Last data: 1 Aug 2026
Source: FRED / BLS
@robomacroHeadline Payrolls
8. Temporary Help Services vs Payrolls MoM Change
Monthly — temp help leads NFP turns by 3-6 months. NFP MoM change as bars on RHS.
Frequency: MonthlySeries: 2Last data: 1 Aug 2026
Source: FRED / BLS
@robomacroHeadline Payrolls
9. Multiple Jobholders
Monthly — workers holding more than one job (FRED: LNS12026620)
Frequency: MonthlySeries: 1Last data: 1 Aug 2026
Source: FRED / BLS
@robomacroSectoral Employment
10. Education & Health Services Employment
Monthly — largest sectoral driver of recent job growth (FRED: USEHS)
Frequency: MonthlySeries: 1Last data: 1 Aug 2026
Source: FRED / BLS
@robomacroShowing 1–10 of 102 charts
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Source: FRED (Federal Reserve Economic Data), BLS, Conference Board, Kansas City Fed.
Payrolls Forecasting Lab
EXPERIMENT10 independent models — time-series econometrics through gradient boosting — each forecasting Friday's BLS Employment Situation. Methodology
Sep 2026 NFP
BLS Employment Situation — Friday, October 2, 2026
15 days away
Core Ensemble Forecast — Median of 8 Models
Headline consensus excludes VAR and LightGBM (implementation-corrected but weak on recent prints). Full 10-model median shown below for reference.
Nonfarm Payrolls
96k
80% model range: 50k → 352k
Unemployment Rate
4.1%
80% model range: 4.0% → 5.6%
Part-Time Workers
4377k
80% model range: 940k → 4853k
Avg Hourly Earnings YoY
3.27%
80% model range: 3.10% → 3.83%
NFP model disagreement (σ): ±121k · Core range (80%): 50k – 352k · Full 10-model median: 96k
Monthly Track Record — Forecast vs First Print (Last 4 Releases)
All scoring uses the first BLS print (8:30am headline) — not revised figures. Beat/miss compares who was closer; direction shows whether each forecast was too high or too low vs that first print.
Model implementation corrections (Aug 2026)
Some models were producing invalid forecasts due to implementation errors (not weak signal). Historical rows below for corrected models reflect the old broken pipeline until forecasts are re-run.
- VAR — Corrected Aug 2026: VAR was fit on mixed units (NFP MoM in thousands vs ICSA ~200k raw claims), so companions dominated and forecasts were systematically wrong (e.g. −155k vs +57k first print in Jun 2026). Fix: per-column z-scoring before VAR estimation plus ±800k forecast cap. Re-run forecasts after this date reflect the corrected pipeline.
- LGBM — Corrected Aug 2026: (1) ADPMNUSNERSA was ingested at person scale (~132.7M) beside PAYEMS in thousands. (2) Regime features used raw claims level differences (~200k) beside MoM NFP. (3) LightGBM lacked StandardScaler and used subsample/colsample on tiny tuned feature sets, producing absurd forecasts (e.g. −1,259k Jun 2026). Fix: ADP unit normalization, StandardScaler on base features, percent-change regime signals, adaptive tree hyperparams for small feature sets, ±800k cap. Historical track-record rows before re-run reflect the old pipeline.
Core ensemble vs street on first print: 3/4 releases we beat consensus
Aug 2026
✓ We beat street
Ensemble 87k off (87k too low)
Street 109k off (109k too low)
Jul 2026
✗ Street was closer
Ensemble 104k off (104k too high)
Street 103k off (103k too high)
Jun 2026
✓ We beat street
Ensemble 34k off (34k too high)
Street 53k off (53k too high)
May 2026
✓ We beat street
Ensemble 85k off (85k too low)
Street 87k off (87k too low)
Econometric vs Machine Learning medians (first print): Econ beat street 2/4 · ML beat street 2/4 · Econ closer than ML 2/4
Aug 2026
ML closer
Econ median 121k off (121k too low)
ML median 81k off (81k too low)
Jul 2026
Econometric closer
Econ median 96k off (96k too high)
ML median 112k off (112k too high)
Jun 2026
Econometric closer
Econ median 15k off (15k too high)
ML median 85k off (85k too high)
May 2026
ML closer
Econ median 107k off (107k too low)
ML median 62k off (62k too low)
| Model | Sep 2026 NFP Awaiting first print | Aug 2026 First print +162k | Jul 2026 First print -23k Revised to +21k · not scored | Jun 2026 First print +57k Revised to +31k · not scored | May 2026 First print +172k Revised to +63k · not scored |
|---|---|---|---|---|---|
Did we beat street? Core 8-model median vs Dow Jones / FinanceFlowAPI — excludes VAR and LightGBM | Pending | ✓ We beat street Ensemble 87k off (87k too low) Street 109k off (109k too low) | ✗ Street was closer Ensemble 104k off (104k too high) Street 103k off (103k too high) | ✓ We beat street Ensemble 34k off (34k too high) Street 53k off (53k too high) | ✓ We beat street Ensemble 85k off (85k too low) Street 87k off (87k too low) |
STREETStreet Consensus Economist survey benchmark · first-print miss below | — | Forecast 109k below first print Forecast +53k ↓ First print +162k | Forecast 103k above first print Forecast +80k ↑ First print -23k | Forecast 53k above first print Forecast +110k ↑ First print +57k | Forecast 87k below first print Forecast +85k ↓ First print +172k |
CORECore Ensemble (8 models) Excludes VAR + LightGBM · headline median · first-print miss below | Pre-release forecast+96kFirst print not yet released | Forecast 87k below first print Forecast +75k ↓ First print +162k ✓ We beat street Ensemble 87k off (87k too low) Street 109k off (109k too low) | Forecast 104k above first print Forecast +81k ↑ First print -23k ✗ Street was closer Ensemble 104k off (104k too high) Street 103k off (103k too high) | Forecast 34k above first print Forecast +91k ↑ First print +57k ✓ We beat street Ensemble 34k off (34k too high) Street 53k off (53k too high) | Forecast 85k below first print Forecast +87k ↓ First print +172k ✓ We beat street Ensemble 85k off (85k too low) Street 87k off (87k too low) |
10Full Ensemble (10 models) Includes VAR + LightGBM · reference only | Pre-release forecast+96kFirst print not yet released | Forecast 87k below first print Forecast +75k ↓ First print +162k | Forecast 104k above first print Forecast +81k ↑ First print -23k | Forecast 34k above first print Forecast +91k ↑ First print +57k | Forecast 85k below first print Forecast +87k ↓ First print +172k |
Econometric Models Time-series methods grounded in macroeconomic theory · 5 models | |||||
ECONEconometric median (5) Median of econometric models · first-print miss below | Pre-release forecast+93kFirst print not yet released | Forecast 121k below first print Forecast +41k ↓ First print +162k ✗ Street was closer Ensemble 121k off (121k too low) Street 109k off (109k too low) | Forecast 96k above first print Forecast +73k ↑ First print -23k ✓ We beat street Ensemble 96k off (96k too high) Street 103k off (103k too high) | Forecast 15k above first print Forecast +72k ↑ First print +57k ✓ We beat street Ensemble 15k off (15k too high) Street 53k off (53k too high) | Forecast 107k below first print Forecast +65k ↓ First print +172k ✗ Street was closer Ensemble 107k off (107k too low) Street 87k off (87k too low) |
Pre-release forecast+93kFirst print not yet released | Forecast 81k below first print Forecast +81k ↓ First print +162k #4 Beat street | Forecast 96k above first print Forecast +73k ↑ First print -23k #5 Beat street | Forecast 15k above first print Forecast +72k ↑ First print +57k #2 Beat street | Forecast 107k below first print Forecast +65k ↓ First print +172k #4 | |
SARIMASARIMAX | Pre-release forecast+42kFirst print not yet released | Forecast 122k below first print Forecast +40k ↓ First print +162k #7 | Forecast 67k above first print Forecast +44k ↑ First print -23k #2 Beat street | Forecast 11k below first print Forecast +46k ↓ First print +57k #1 Beat street | Forecast 133k below first print Forecast +39k ↓ First print +172k #7 |
Pre-release forecast+88kFirst print not yet released | Forecast 317k below first print Forecast -155k ↓ First print +162k #10 | Forecast 122k above first print Forecast +99k ↑ First print -23k #7 | Forecast 396k below first print Forecast -339k ↓ First print +57k #10 | Forecast 218k below first print Forecast -46k ↓ First print +172k #8 | |
Pre-release forecast+99kFirst print not yet released | Forecast 121k below first print Forecast +41k ↓ First print +162k #6 | Forecast 83k above first print Forecast +60k ↑ First print -23k #3 Beat street | Forecast 32k above first print Forecast +89k ↑ First print +57k #4 Beat street | Forecast 58k below first print Forecast +114k ↓ First print +172k #2 Beat street | |
Pre-release forecast+123kFirst print not yet released | Forecast 25k below first print Forecast +137k ↓ First print +162k #2 Beat street | Forecast 130k above first print Forecast +107k ↑ First print -23k #8 | Forecast 37k above first print Forecast +94k ↑ First print +57k #5 Beat street | Forecast 21k below first print Forecast +151k ↓ First print +172k #1 Beat street | |
Machine Learning Models Data-driven methods with non-linear feature interactions · 5 models | |||||
MLML median (5) Median of machine-learning models · first-print miss below | Pre-release forecast+233kFirst print not yet released | Forecast 81k below first print Forecast +81k ↓ First print +162k ✓ We beat street Ensemble 81k off (81k too low) Street 109k off (109k too low) | Forecast 112k above first print Forecast +89k ↑ First print -23k ✗ Street was closer Ensemble 112k off (112k too high) Street 103k off (103k too high) | Forecast 85k above first print Forecast +142k ↑ First print +57k ✗ Street was closer Ensemble 85k off (85k too high) Street 53k off (53k too high) | Forecast 62k below first print Forecast +110k ↓ First print +172k ✓ We beat street Ensemble 62k off (62k too low) Street 87k off (87k too low) |
Pre-release forecast+353kFirst print not yet released | ✓ Hit first print Forecast +163k ✓ First print +162k #1 Beat street | Forecast 417k above first print Forecast +394k ↑ First print -23k #10 | Forecast 64k above first print Forecast +121k ↑ First print +57k #6 | Forecast 62k below first print Forecast +110k ↓ First print +172k #3 Beat street | |
Pre-release forecast+62kFirst print not yet released | Forecast 93k below first print Forecast +69k ↓ First print +162k #5 Beat street | Forecast 86k above first print Forecast +63k ↑ First print -23k #4 Beat street | Forecast 30k above first print Forecast +87k ↑ First print +57k #3 Beat street | Forecast 120k below first print Forecast +52k ↓ First print +172k #6 | |
XGBXGBoost | Pre-release forecast+53kFirst print not yet released | Forecast 81k below first print Forecast +81k ↓ First print +162k #3 Beat street | Forecast 112k above first print Forecast +89k ↑ First print -23k #6 | Forecast 85k above first print Forecast +142k ↑ First print +57k #7 | Forecast 108k below first print Forecast +64k ↓ First print +172k #5 |
Pre-release forecast+233kFirst print not yet released | Forecast 217k above first print Forecast +379k ↑ First print +162k #9 | Forecast 12k above first print Forecast -11k ↑ First print -23k #1 Beat street | Forecast 388k above first print Forecast +445k ↑ First print +57k #9 | Forecast 235k above first print Forecast +407k ↑ First print +172k #9 | |
Pre-release forecast+351kFirst print not yet released | Forecast 163k below first print Forecast -1k ↓ First print +162k #8 | Forecast 261k above first print Forecast +238k ↑ First print -23k #9 | Forecast 176k above first print Forecast +233k ↑ First print +57k #8 | Forecast 268k above first print Forecast +440k ↑ First print +172k #10 | |
| All models beating street: | 5/10 | 5/10 | 5/10 | 3/10 | |
| Econometric models beating street: | 2/5 | 3/5 | 4/5 | 2/5 | |
| ML models beating street: | 3/5 | 2/5 | 1/5 | 1/5 | |
“Off” = absolute miss vs first print (lower is better). Green We beat street = core ensemble was closer than consensus. Street row shows how far consensus missed; core ensemble row shows how far we missed. Models marked FIXED AUG 2026 had implementation errors corrected — see note above.
Econometric Models
Time-series methods grounded in macroeconomic theory
AREcon
Naive Autoregressive (AR)
NFP Forecast
93k
[-1476k – 1661k]
Dir. accuracy39%
Deep dive →
SARIMAEcon
SARIMAX
NFP Forecast
42k
[-267k – 351k]
Dir. accuracy38%
Deep dive →
VAREcon
Vector Autoregression
NFP Forecast
88k
[-1277k – 1452k]
Dir. accuracy37%
Deep dive →
UCEcon
Unobserved Components
NFP Forecast
99k
[-151k – 348k]
Dir. accuracy32%
Deep dive →
MIDASEcon
Almon Distributed Lag
NFP Forecast
123k
[-1166k – 1411k]
Dir. accuracy47%
Deep dive →
Machine Learning Models
Data-driven methods with non-linear feature interactions
ENetML
ElasticNet Linear
NFP Forecast
353k
[-756k – 1461k]
Dir. accuracy46%
Deep dive →
RFML
Random Forest
NFP Forecast
62k
[-1320k – 1445k]
Dir. accuracy46%
Deep dive →
XGBML
XGBoost
NFP Forecast
53k
[-46k – 152k]
Dir. accuracy48%
Deep dive →
LGBMML
LightGBM + Regime
NFP Forecast
233k
[-912k – 1379k]
Dir. accuracy41%
Deep dive →
MLPML
Neural Network (MLP)
NFP Forecast
351k
[-1089k – 1792k]
Dir. accuracy52%
Deep dive →
Median: 96k
Sorted by NFP point forecast. Dashed label shows consensus median.
Models last updated: 9/16/2026, 5:00:06 AM UTC · Source: FRED via RoboMacro
