Build a Chain-Linked GDP Growth Contribution Explorer

An economist producing a quarterly growth decomposition.

Explain real GDP growth using published contribution measures. Prevent analysts from treating chained-dollar component levels as an additive accounting identity.

Key: release_id + quarter + component_line.

Fields: reported_growth_pct, contribution_pp, chained_level, reference_year, table_id.

Percentage points and percentage changes use separate fields.

Align release versions and component definitions.

Build contribution waterfalls from published contribution tables.

Calculate and disclose rounding residuals.

Export an explanatory chart with rejected invalid aggregations.

A contribution waterfall with table drilldown and a visible nonadditivity warning on chained-dollar views.

A release-linked decomposition dataset.

A chart export that identifies contribution units and rounding differences.

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.

Do not sum chained-dollar components to infer aggregate real GDP.

Contributions reconcile within the source rounding tolerance.

Changing reference years cannot create an unexplained economic growth effect.

Reject contribution rows from different release versions.

Two tables have identical labels but different vintages. The application must block their combined waterfall.

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

Full target: Acquire 40 quarters of published real growth, contribution tables, and chained-dollar levels from matching releases. Preserve table metadata and rounding precision.

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

pandas, DuckDB, Altair

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.

The fixture checks arithmetic reconciliation only, not the complete chain-index calculation.

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.