AgenticInvestor

Philippines GDP From Transport

Philippines · Quarterly series · EC-PH-GDPT

Latest, Q3 2026

219.38B PHP

+2.0% from Q2 2026

Open interactive chartPhilippines GDP From Transport in the app

266.3B148.7B31.16BQ1 2000Q3 2027
History through Q3 2026, the forecast path to Q3 2027 (dashed) and its 80% interval (shaded).

Forecast

Forecast for Q3 2027: 225.30B PHP, with an 80% interval of 218.05B PHP to 232.10B PHP.

Forecast path. Intervals are shown where available.
PeriodForecast80% low80% high
Q4 2026252.70B PHP239.64B PHP266.32B PHP
Q1 2027244.96B PHP238.04B PHP252.01B PHP
Q2 2027222.69B PHP219.50B PHP225.66B PHP
Q3 2027225.30B PHP218.05B PHP232.10B PHP

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
+19%Beats the no-change baseline over 8 tests (8 tests, 6 won)
Direction
75%No clear edge over a coin flip (24 of 32 calls right in 8 tests)
Turning points
12 of 16Anticipated most turns (16 turns in 8 tests, 0 without a turn)
Error vs baseline
19% lowerError ratio 0.81; 1.00 is a tie.
Tests won
6 / 8Complete rolling backtests; ties count as no win.
Test period
2018-12-01 to 2026-09-014 observations ahead per test.
80% band coverage
88%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

It is above 90% of the 39 earlier readings since Q4 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
Q3 2026219.38B PHP
Q2 2026215.08B PHP
Q1 2026236.67B PHP
Q4 2025248.06B PHP
Q3 2025211.46B PHP
Q2 2025206.77B PHP
Q1 2025225.64B PHP
Q4 2024237.29B PHP
Q3 2024202.48B PHP
Q2 2024189.71B PHP
Q1 2024205.98B PHP
Q4 2023216.44B PHP
Q3 2023190.53B PHP
Q2 2023165.27B PHP
Q1 2023195.35B PHP
Q4 2022197.81B PHP
Q3 2022169.95B PHP
Q2 2022141.18B PHP
Q1 2022170.43B PHP
Q4 2021165.52B PHP
Q3 2021136.73B PHP
Q2 2021110.48B PHP
Q1 2021135.08B PHP
Q4 2020139.24B PHP

Source

Data: public market data feeds.

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