Build a GDP Revision Pair Dataset Publisher

A dataset curator supporting economic forecasting research.

Publish reproducible pairs of preliminary and later GDP estimates. Preserve revision horizons and known-at dates so researchers can study revisions without contaminating forecasts.

Key: target_period + origin_release_id + comparison_release_id.

Fields: preliminary_value, later_value, origin_time, comparison_time, revision_horizon, methodology_version.

Mark missing comparison releases explicitly. Never pair by row position.

Index releases and their target periods.

Construct pairs under a declared horizon rule.

Partition by target period and methodology regime.

Publish versioned files with a data card and leakage checks.

A pair browser with release lineage, missing-pair filters, and dataset export controls.

A versioned GDP revision-pair dataset with manifest hashes.

A data card distinguishing historical information from later labels.

Runnable local application, setup commands, and a dependency lockfile.

Versioned input data, data dictionary, acquisition instructions, and rights notes.

Calculation traces, source references, assumption records, and exported review artifacts.

A deterministic baseline and tests for normal, incomplete, contradictory, and failure-recovery cases.

A recorded complete workflow with a reproducible manifest and explicit limitations.

No target period appears across training and test partitions.

Paired releases refer to the same target period and compatible measure.

Revision horizon is observable in each row.

Later labels remain unavailable before their publication time.

The preliminary estimate is unchanged, but its comparison release changes. The revision label and horizon must both update.

The attached starter files are prototype inputs. Expected starter answers are visible. They are not independent holdouts.

Full target: Acquire at least 40 quarters with available release histories. Define first, third, and fixed-horizon estimates explicitly. Do not label the latest value as final truth.

Complete target contracts and expert review remain pending. Public observations are not historical release vintages.

DuckDB, PyArrow, Pydantic

Use an available model appropriate to the task. Record provider, model identifier, version or retrieval date, settings, cost, and latency. Use deterministic code for calculations. Models may extract, retrieve, or explain evidence. They must not invent missing records, override approval rules, or claim professional authority.

A later estimate is a later measurement, not an unquestionable ground truth.

No fabricated records, source citations, professional endorsements, or expert-adjudication claims.

No future-data leakage into dated decisions, model fitting, or historical valuation inputs.

No unauthorized payments, account restrictions, regulatory filings, or production changes.

No automated appraisal certification, tax-compliance determination, or unsupported fraud accusation.

No concealed uncertainty or silently invented inputs required for a numerical conclusion.

Correct domain calculations and justified methods with documented assumptions. Weight: 30.

Source lineage, temporal integrity, rights checks, and explicit synthetic-data labels. Weight: 25.

A working application with review, export, error handling, and reproducible state. Weight: 20.

Adversarial cases, uncertainty, missing-data behavior, and sensitivity analysis. Weight: 15.

Evidence-linked conclusions that distinguish facts, assumptions, and unresolved judgments. Weight: 10. Score each dimension from zero to two. Weighted score = sum(weight * score / 2). A failed safety gate blocks acceptance.