AgenticInvestor

All Employees, Total Nonfarm

Panama · Monthly series · EC-PAYEMS.FRED

Latest, October 2026

159,293,000.0 pts

+0.1% from September 2026

Open interactive chartAll Employees, Total Nonfarm in the app

160.6M145.5M130.4MNovember 2016October 2027
History through October 2026, the forecast path to October 2027 (dashed) and its 80% interval (shaded).

Forecast

Forecast for October 2027: 160,080,928.0 pts, with an 80% interval of 159,602,829.1 pts to 160,552,221.7 pts.

Forecast path. Intervals are shown where available.
PeriodForecast80% low80% high
November 2026159,263,296.0 pts159,047,067.2 pts159,482,812.8 pts
December 2026159,294,176.0 pts159,036,602.4 pts159,574,143.4 pts
January 2027159,354,400.0 pts159,267,798.0 pts159,446,474.1 pts
February 2027159,416,608.0 pts159,160,422.9 pts159,681,746.2 pts
March 2027159,510,880.0 pts159,248,405.3 pts159,786,354.9 pts
April 2027159,596,544.0 pts159,377,566.9 pts159,819,406.6 pts
May 2027159,625,152.0 pts159,396,387.6 pts159,856,654.1 pts
June 2027159,700,672.0 pts159,468,805.7 pts159,937,391.2 pts
July 2027159,848,976.0 pts159,553,215.8 pts160,148,325.6 pts
August 2027159,915,216.0 pts159,618,956.4 pts160,212,112.8 pts
September 2027160,037,952.0 pts159,576,864.9 pts160,478,527.4 pts
October 2027160,080,928.0 pts159,602,829.1 pts160,552,221.7 pts

Model: Agentic Investor TSFM (foundation model). Forecast generated 2026-11-15.

AI confidence

Supported evidence

The point forecast beat the no-change baseline overall and in a majority of the evaluated tests.

Model: Agentic Investor TSFM (foundation model)

Accuracy vs naive
+49%Beats the no-change baseline over 12 tests (12 tests, 11 won)
Direction
94%Beats a coin flip (136 of 144 calls right in 12 tests)
Turning points
2 of 17Anticipated some turns (17 turns in 12 tests, 8 without a turn)
Error vs baseline
49% lowerError ratio 0.51; 1.00 is a tie.
Tests won
11 / 12Complete rolling backtests; ties count as no win.
Test period
2014-11-01 to 2026-10-0112 observations ahead per test.
80% band coverage
81%Observed outcomes inside the band across all stored test windows.
How this assessment is made

AI confidence describes historical evidence for this forecast, not a probability of being correct. Each backtest fits on data available before its test window. The baseline carries the last value forward. Supported evidence requires at least six complete, non-overlapping tests, an error ratio below 0.90 (rounded to two decimals), and wins in a majority of tests. Ratios from 0.90 to below 1.10 are on par; 1.10 or above is weak evidence. Inconsistent wins produce mixed evidence. For a selected model, only the recent tests excluded from model selection determine this label. These are historical simulations using the available data, not a live trading track record; revisions and foundation-model pretraining can limit how closely they reproduce a past decision. Past performance can change.

Replay past tests

Model comparison

Comparison pendingA new ticker first receives its serving model's forecast and evaluation. An eligible nightly batch can compare both candidates, subject to call budgets and retry limits.

Reading

That is the highest of the 120 readings since November 2016.

Written from the figures by a fixed template; no AI model is involved.

Recent history (24 observations)
The 24 most recent observations, newest first.
PeriodValue
October 2026159,293,000.0 pts
September 2026159,128,000.0 pts
August 2026159,068,000.0 pts
July 2026158,882,000.0 pts
June 2026158,892,000.0 pts
May 2026158,861,000.0 pts
April 2026158,798,000.0 pts
March 2026158,650,000.0 pts
February 2026158,436,000.0 pts
January 2026158,592,000.0 pts
December 2025158,432,000.0 pts
November 2025158,449,000.0 pts
October 2025158,408,000.0 pts
September 2025158,548,000.0 pts
August 2025158,472,000.0 pts
July 2025158,542,000.0 pts
June 2025158,478,000.0 pts
May 2025158,498,000.0 pts
April 2025158,485,000.0 pts
March 2025158,377,000.0 pts
February 2025158,310,000.0 pts
January 2025158,268,000.0 pts
December 2024158,316,000.0 pts
November 2024158,079,000.0 pts

Source

Data: public market data feeds.

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