Economics Ml

Economics ML Skill

Use this skill for macroeconomic nowcasting, central-bank policy diagnostics, economics-oriented ML calibration, and validation reports.

Interface

Treat this as an agent skill first. A runtime installation should include this file, requirements.txt, main.py, backtest_engine.py, and src/. Use the bundled Python scripts when the user asks for a live run, fresh backtest, regenerated dashboard snapshot, or reproducible artifact. For interpretive questions, answer from the latest available report, snapshot, user-supplied numbers, or cited public data.

Operating Principles

• Do not expose private keys, local machine identifiers, personal names, or unpublished private data in generated reports or dashboard text.
• Keep the structural economics model as the primary estimate.
• Use ML as an auxiliary calibration, measurement, nuisance-estimation, heterogeneity, or validation layer.
• Do not describe ML calibration as the main forecast.
• Keep causal language conservative. Separate measurement, prediction, association, identification, and policy evaluation.
• Treat boundary and discontinuity designs as nonparametric econometrics unless a specific ML method is actually used.

Default Agent Behavior

When answering a user request, produce an economist-style readout rather than a raw script result. Include:

• Forecast or policy target period.
• Data-through date and release-lag assumptions.
• Sources used and sources missing.
• Structural baseline result.
• Data-enhanced result when available, shown separately.
• Driven-factor decomposition.
• Directional interpretation of the largest positive and negative factors.
• Backtest window, observations, R2/RMSE where available.
• Leakage and overfit checks.
• Limitation note for revised-data, pseudo-real-time, or non-causal results.

Non-Script Analysis Mode

Use this mode when the user asks what the results mean, why a country changed, whether an enhancement is credible, or which factors are driving a forecast. Do not simply tell the user to run a command.

Process:

• Start from the latest available report, dashboard snapshot, or values supplied by the user.
• Identify the baseline model result before discussing the enhanced result.
• Attribute the enhanced result to observable factors and state the sign of each material factor.
• Explain whether the enhanced layer changes the policy or forecast interpretation.
• Separate evidence from inference. Use cautious language when the data are revised, sparse, or pseudo-real-time.
• State what additional data would be needed before making a stronger claim.

Output should be concise but diagnostic:

Bottom line:
Baseline signal:
Data-enhanced signal:
Main driven factors:
Validation check:
Interpretation:
What not to conclude:

Supported Analysis Layers

1. Measurement layer: convert text, news, disclosures, patents, images, audio, web traces, and other raw inputs into structured variables.
2. Nuisance-function estimation: estimate selection probabilities, propensity scores, conditional expectations, control functions, or counterfactual outcomes.
3. Causal and policy evaluation: DID, DML, causal forests, QTE, policy heterogeneity, and targeting rules.
4. Structural and dynamic models: value functions, policy functions, state distributions, and equilibrium objects.
5. Interpretable multimodal prediction: use graph, time-series, text, audio, or video signals only when the prediction remains auditable.
6. Domain-specific economics: climate and energy, finance, labor automation, innovation, disclosure, platform governance, and supply chains.
7. Validation and auditing: construct validity, annotator reliability, leakage checks, calibration, and external validity.
8. Boundary and nonparametric caution: do not relabel identification designs as ML when the core contribution is econometric.

Report Templates

GDP Nowcast

Use this structure:

Target period:
Data through:
Baseline bridge nowcast:
ML auxiliary calibration:
Final calibrated nowcast:
Driven factors:
- Activity:
- Labor:
- Prices:
- Financial conditions:
- External demand:
Validation:
Limitations:

Rules:

• State that ML is auxiliary calibration, not the main predictor.
• Report US and Canada separately when both are available.
• Do not compare calibrated and baseline results without the same validation window.
• Mention release-lag filtering when monthly data are used for current-quarter evaluation.

Policy Rate Diagnostics

Use this structure:

Central bank:
Data through:
Current policy rate:
Base Taylor rate:
Data-Enhanced Taylor rate:
Gap versus actual:
Driven factors:
- Activity gap:
- Inflation pressure:
- Financial conditions:
- External pressure:
- Labor cooling:
Policy interpretation:
Validation and limitations:

Rules:

• Base Taylor is the structural signal.
• Data-Enhanced Taylor is a learned historical residual adjustment.
• Do not tune parameters by hand to match official projections.
• Explain whether the enhancement moves the estimate closer to or farther from the current policy rate.
• Treat the result as a diagnostic, not a mechanical recommendation.

Backtest Review

Use this structure:

Window:
Observations:
Baseline R2 / RMSE:
ML-calibrated R2 / RMSE:
RMSE gain:
Leakage controls:
Residual risk:

Rules:

• Confirm that calibration uses only prior rows in rolling validation.
• Call out revised-data limitations.
• Do not report gains without baseline metrics.

Commands

Install:

pip install -r requirements.txt

Run policy diagnostics:

python main.py policy --country US
python main.py policy --country Canada

Run GDP nowcast:

python main.py gdp --country US
python main.py gdp --country Canada

Run backtest:

python backtest_engine.py

Data Requirements

• Set FRED_API_KEY in the environment before running live data workflows.
• If live data are missing, report the missing source explicitly.
• Dashboard snapshot values should be generated from the Python workflow or clearly labeled as a static example.