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: 30 Oct 2026Showing 1–10 of 102 charts
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Headline Payrolls
1. Total Nonfarm Payrolls
Thousands, SA — All employees on nonfarm payrolls (FRED: PAYEMS)
Frequency: MonthlySeries: 1Last data: 1 Sept 2026
Source: FRED / BLS
@robomacroHeadline Payrolls
2. Total Private Payrolls
Monthly — Private sector payrolls excluding government (FRED: USPRIV)
Frequency: MonthlySeries: 1Last data: 1 Sept 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 Sept 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 Sept 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 Sept 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 Sept 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 Sept 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 Sept 2026
Source: FRED / BLS
@robomacroHeadline Payrolls
9. Multiple Jobholders
Monthly — workers holding more than one job (FRED: LNS12026620)
Frequency: MonthlySeries: 1Last data: 1 Sept 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 Sept 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
Oct 2026 NFP
BLS Employment Situation — Friday, November 6, 2026
5 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
76k
80% model range: 50k → 310k
Unemployment Rate
4.2%
80% model range: 4.1% → 5.6%
Part-Time Workers
4641k
80% model range: 940k → 4934k
Avg Hourly Earnings YoY
3.25%
80% model range: 3.06% → 3.54%
NFP model disagreement (σ): ±110k · Core range (80%): 50k – 310k · Full 10-model median: 72k
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
Sep 2026
✓ We beat street
Ensemble 58k off (58k too high)
Street 61k off (61k too high)
Aug 2026
✓ We beat street
Ensemble 96k off (96k too low)
Street 109k off (109k too low)
Jul 2026
✗ Street was closer
Ensemble 107k off (107k 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)
Econometric vs Machine Learning medians (first print): Econ beat street 3/4 · ML beat street 1/4 · Econ closer than ML 3/4
Sep 2026
Econometric closer
Econ median 48k off (48k too high)
ML median 99k off (99k too high)
Aug 2026
ML closer
Econ median 122k off (122k too low)
ML median 87k off (87k too low)
Jul 2026
Econometric closer
Econ median 96k off (96k too high)
ML median 117k off (117k too high)
Jun 2026
Econometric closer
Econ median 15k off (15k too high)
ML median 85k off (85k too high)
| Model | Oct 2026 NFP Awaiting first print | Sep 2026 First print +29k | Aug 2026 First print +162k Revised to +133k · not scored | Jul 2026 First print -23k Revised to -10k · not scored | Jun 2026 First print +57k Revised to +31k · not scored |
|---|---|---|---|---|---|
Did we beat street? Core 8-model median vs Dow Jones / FinanceFlowAPI — excludes VAR and LightGBM | Pending | ✓ We beat street Ensemble 58k off (58k too high) Street 61k off (61k too high) | ✓ We beat street Ensemble 96k off (96k too low) Street 109k off (109k too low) | ✗ Street was closer Ensemble 107k off (107k too high) Street 103k off (103k too high) | ✓ We beat street Ensemble 34k off (34k too high) Street 53k off (53k too high) |
STREETStreet Consensus Economist survey benchmark · first-print miss below | — | Forecast 61k above first print Forecast +90k ↑ First print +29k | 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 |
CORECore Ensemble (8 models) Excludes VAR + LightGBM · headline median · first-print miss below | Pre-release forecast+76kFirst print not yet released | Forecast 58k above first print Forecast +87k ↑ First print +29k ✓ We beat street Ensemble 58k off (58k too high) Street 61k off (61k too high) | Forecast 96k below first print Forecast +66k ↓ First print +162k ✓ We beat street Ensemble 96k off (96k too low) Street 109k off (109k too low) | Forecast 107k above first print Forecast +84k ↑ First print -23k ✗ Street was closer Ensemble 107k off (107k 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) |
10Full Ensemble (10 models) Includes VAR + LightGBM · reference only | Pre-release forecast+72kFirst print not yet released | Forecast 58k above first print Forecast +87k ↑ First print +29k | Forecast 96k below first print Forecast +66k ↓ First print +162k | Forecast 107k above first print Forecast +84k ↑ First print -23k | Forecast 34k above first print Forecast +91k ↑ First print +57k |
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+53kFirst print not yet released | Forecast 48k above first print Forecast +77k ↑ First print +29k ✓ We beat street Ensemble 48k off (48k too high) Street 61k off (61k too high) | Forecast 122k below first print Forecast +40k ↓ First print +162k ✗ Street was closer Ensemble 122k off (122k 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) |
Pre-release forecast+72kFirst print not yet released | Forecast 68k above first print Forecast +97k ↑ First print +29k #6 | Forecast 82k below first print Forecast +80k ↓ First print +162k #2 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 | |
SARIMASARIMAX | Pre-release forecast+41kFirst print not yet released | Forecast 13k above first print Forecast +42k ↑ First print +29k #3 Beat street | Forecast 122k below first print Forecast +40k ↓ First print +162k #5 | 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 |
Pre-release forecast-208kFirst print not yet released | Forecast 13k above first print Forecast +42k ↑ First print +29k #2 Beat street | Forecast 404k below first print Forecast -242k ↓ 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 | |
Pre-release forecast+53kFirst print not yet released | Forecast 48k above first print Forecast +77k ↑ First print +29k #5 Beat street | Forecast 136k below first print Forecast +26k ↓ 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 | |
Pre-release forecast+135kFirst print not yet released | Forecast 93k above first print Forecast +122k ↑ First print +29k #7 | Forecast 25k below first print Forecast +137k ↓ First print +162k #1 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 | |
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+80kFirst print not yet released | Forecast 99k above first print Forecast +128k ↑ First print +29k ✗ Street was closer Ensemble 99k off (99k too high) Street 61k off (61k too high) | 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 117k above first print Forecast +94k ↑ First print -23k ✗ Street was closer Ensemble 117k off (117k 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) |
Pre-release forecast+348kFirst print not yet released | Forecast 136k above first print Forecast +165k ↑ First print +29k #9 | Forecast 190k above first print Forecast +352k ↑ First print +162k #8 | Forecast 229k above first print Forecast +206k ↑ First print -23k #10 | Forecast 64k above first print Forecast +121k ↑ First print +57k #6 | |
Pre-release forecast+80kFirst print not yet released | Forecast 25k above first print Forecast +54k ↑ First print +29k #4 Beat street | Forecast 105k below first print Forecast +57k ↓ First print +162k #4 Beat street | Forecast 83k above first print Forecast +60k ↑ First print -23k #4 Beat street | Forecast 30k above first print Forecast +87k ↑ First print +57k #3 Beat street | |
XGBXGBoost | Pre-release forecast+71kFirst print not yet released | Forecast 7k above first print Forecast +36k ↑ First print +29k #1 Beat street | Forecast 87k below first print Forecast +75k ↓ First print +162k #3 Beat street | Forecast 117k above first print Forecast +94k ↑ First print -23k #6 | Forecast 85k above first print Forecast +142k ↑ First print +57k #7 |
Pre-release forecast+58kFirst print not yet released | Forecast 99k above first print Forecast +128k ↑ First print +29k #8 | 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 | |
Pre-release forecast+293kFirst print not yet released | Forecast 211k above first print Forecast +240k ↑ First print +29k #10 | Forecast 163k below first print Forecast -1k ↓ First print +162k #7 | Forecast 223k above first print Forecast +200k ↑ First print -23k #9 | Forecast 176k above first print Forecast +233k ↑ First print +57k #8 | |
| All models beating street: | 5/10 | 4/10 | 5/10 | 5/10 | |
| Econometric models beating street: | 3/5 | 2/5 | 3/5 | 4/5 | |
| ML models beating street: | 2/5 | 2/5 | 2/5 | 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
72k
[-1494k – 1638k]
Dir. accuracy39%
Deep dive →
SARIMAEcon
SARIMAX
NFP Forecast
41k
[-267k – 349k]
Dir. accuracy38%
Deep dive →
VAREcon
Vector Autoregression
NFP Forecast
-208k
[-1570k – 1154k]
Dir. accuracy37%
Deep dive →
UCEcon
Unobserved Components
NFP Forecast
53k
[-196k – 302k]
Dir. accuracy32%
Deep dive →
MIDASEcon
Almon Distributed Lag
NFP Forecast
135k
[-1151k – 1422k]
Dir. accuracy48%
Deep dive →
Machine Learning Models
Data-driven methods with non-linear feature interactions
ENetML
ElasticNet Linear
NFP Forecast
348k
[-758k – 1455k]
Dir. accuracy46%
Deep dive →
RFML
Random Forest
NFP Forecast
80k
[-1303k – 1464k]
Dir. accuracy46%
Deep dive →
XGBML
XGBoost
NFP Forecast
71k
[-31k – 174k]
Dir. accuracy48%
Deep dive →
LGBMML
LightGBM + Regime
NFP Forecast
58k
[-1086k – 1202k]
Dir. accuracy41%
Deep dive →
MLPML
Neural Network (MLP)
NFP Forecast
293k
[-1116k – 1702k]
Dir. accuracy52%
Deep dive →
Median: 76k
Sorted by NFP point forecast. Dashed label shows consensus median.
Models last updated: 10/19/2026, 5:00:06 AM UTC · Source: FRED via RoboMacro
