Brazil Macro Bundle: SELIC, IPCA, USD/BRL
2020–2026
$12.9
Fixed price
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Source, license and coverage
Supplier documentation. These claims are separate from the automated sample score. A listing edit date is not a data freshness date.
- License
- Not documented — confirm reuse terms with the seller
- Source / creator
- Supplier-cited: Banco Central do Brasil SGS
- Collection method
- Supplier description: Combined Brazil macro series, monthly, 2020-01 to 2026-09 (81 months): SELIC monthly average (%), IPCA monthly inflation (%), and USD/BRL monthly average. Sourced from Banco Central do Brasil SGS (public domain). One row per month - ideal for macro analysis, interest-rate and inflation modeling agents without needing to merge multiple series.
- Coverage / snapshot
- 2020–2026
- Data last updated
- Not documented
- Update schedule
- Not documented
Source attribution is supplier-reported. The source link identifies publisher documentation; it does not independently verify this uploaded extract. Coverage is stated by the supplier; it has not been verified against every record in the full file.
Sample structure score: 90 / 100
This automated check describes the inspected sample, not factual accuracy, legal rights, representativeness, or the quality of the entire dataset. It is not a customer rating.
Assessed 10 sample records (CSV) on 2026-10-09. All records in the provided sample were checked.
| Check | Points | Evidence |
|---|---|---|
| Populated cells | 40 / 50 | 32 of 40 top-level cells contain a value. Null, absent and blank values count as missing; zero and false count as populated. |
| Consistent value types | 30 / 30 | 32 of 32 populated cells match their column's most common observed type. Types are inferred, not checked against real-world truth. |
| Consistent record shape | 20 / 20 | 10 of 10 records have the expected fields. CSV/TSV use the header width; JSON uses the union of observed keys. |
Field-level findings and improvements
Check missing cells and mixed types below. Document intentional missing values or mixed types in your field descriptions. Do not fill legitimate unknowns with invented values just to increase this score.
| Field | Missing cells | Most common type | Other populated types |
|---|---|---|---|
| mes | 0 / 10 | string | 0 / 10 |
| selic_media_pct | 0 / 10 | number | 0 / 10 |
| ipca_mensal_pct | 1 / 10 | number | 0 / 9 |
| usd_brl_media | 7 / 10 | number | 0 / 3 |
About this data
Monthly Brazilian macroeconomic series comprising SELIC rate, IPCA inflation, and USD/BRL exchange rate from Banco Central do Brasil SGS. One row per month, suitable for macro analysis and interest-rate modeling.
Retrieve with your agent or Python
Create an account and configure DATABAZAAR_API_KEY. This example retrieves free or already purchased data; it never makes a purchase. For a multi-file dataset, choose a file index from the manifest.
Download the Python examplepython3 retrieve-dataset.py e9526094-c433-4199-be79-9b5646fd9b94 --output dataset.bin
Full supplier documentation
Schema
| Name | Type | Description |
|---|---|---|
| mes | string | |
| selic_media_pct | string | |
| ipca_mensal_pct | string | |
| usd_brl_media | string |
Sample Data
Download a free sample to inspect the data before purchasing.
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For AI Agents
# 1. Add to your agent's MCP config (claude_desktop_config.json or similar):
{
"mcpServers": {
"databazaar": { "command": "npx", "args": ["databazaar-mcp"] }
}
}
# 2. Your agent can then call:
search_datasets({ query: "Brazil Macro Bundle: SELIC, IP" })
// Found: e9526094-c433-4199-be79-9b5646fd9b94
buy_now({ dataset_id: "e9526094-c433-4199-be79-9b5646fd9b94" }) // uses your saved card# Authenticate with your API key:
curl https://api.databazaar.io/datasets/e9526094-c433-4199-be79-9b5646fd9b94 \
-H "Authorization: Bearer YOUR_API_KEY"
# Purchase:
curl -X POST https://api.databazaar.io/datasets/e9526094-c433-4199-be79-9b5646fd9b94/buy \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"dataset_id": "e9526094-c433-4199-be79-9b5646fd9b94"}'