Can this skill generate visual reports?

The skill generates structured Markdown reports and JSON data which can be easily converted into charts or used for further data visualization.

Do I need a FRED or BLS API key to use this skill?

No, this skill is designed to use public CSV endpoints and public API versions that do not require an API key for standard queries.

Why is the difference between CPI and PCE important?

CPI uses a fixed basket of goods while PCE uses dynamic weights. Because the Fed prefers PCE, understanding why it diverges from CPI helps predict monetary policy shifts.

What does 'low-volatility, high-weight' mean in this context?

It refers to spending categories that comprise a large portion of the PCE index but have stable prices; if these prices start rising, they pose a significant risk to the overall inflation target.

US Inflation CPI-PCE Comparator

Name: US Inflation CPI-PCE Comparator
Author: fatfingererr

byfatfingererr

Ciencia de Datos y ML

Analyzes and compares US CPI and PCE inflation metrics to identify underlying price acceleration risks and weighting discrepancies.

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This skill empowers users to bridge the gap between Consumer Price Index (CPI) and Personal Consumption Expenditures (PCE) data by identifying divergences in inflation signals. It specifically focuses on identifying 'low-volatility, high-weight' expenditure buckets to detect potential PCE re-acceleration that might be masked by cooling CPI figures. By automating data retrieval from FRED and BLS, it provides weighted contribution analysis and risk framing, making it an essential tool for macroeconomists, traders, and policy analysts tracking Federal Reserve inflation targets.

Características Principales

Identification of PCE upside risks in low-volatility, high-weight consumption categories.
Standardized JSON and Markdown reporting for seamless research integration.
Support for multiple measurement metrics including YoY, MoM SAAR, and QoQ SAAR.
Detailed attribution analysis of weighted contributions to inflation divergence.
Automated FRED and BLS data extraction without requiring private API keys.
0 GitHub stars

Casos de Uso

Performing historical macro-analysis of inflation weight shifts and momentum since 2020.
Monitoring Federal Reserve policy pivots by tracking PCE divergence from headline CPI.
Identifying hidden inflationary pressures in specific sectors like healthcare and financial services.

Acerca de

Características Principales

Identification of PCE upside risks in low-volatility, high-weight consumption categories.
Standardized JSON and Markdown reporting for seamless research integration.
Support for multiple measurement metrics including YoY, MoM SAAR, and QoQ SAAR.
Detailed attribution analysis of weighted contributions to inflation divergence.
Automated FRED and BLS data extraction without requiring private API keys.
0 GitHub stars

Casos de Uso

Performing historical macro-analysis of inflation weight shifts and momentum since 2020.
Monitoring Federal Reserve policy pivots by tracking PCE divergence from headline CPI.
Identifying hidden inflationary pressures in specific sectors like healthcare and financial services.