Economic Analysis Use Case
Leverage FRED MCP Server for comprehensive economic analysis, from macroeconomic forecasting to sectoral performance evaluation.
Overview
Economic analysts use FRED data to:
- Monitor current economic conditions
- Analyze historical trends
- Identify relationships between indicators
- Support forecasting and projections
- Evaluate policy impacts
Macroeconomic Analysis
Business Cycle Analysis
Track key indicators across the cycle:
Leading Indicators
// Building permits
{ "series_id": "PERMIT" }
// Initial jobless claims
{ "series_id": "ICSA" }
// Yield curve spread
{ "series_id": "T10Y2Y" }
// Consumer expectations
{ "series_id": "UMCSENT" }
Coincident Indicators
// GDP
{ "series_id": "GDP" }
// Employment
{ "series_id": "PAYEMS" }
// Industrial production
{ "series_id": "INDPRO" }
// Retail sales
{ "series_id": "RSXFS" }
Lagging Indicators
// Unemployment
{ "series_id": "UNRATE" }
// Unit labor costs
{ "series_id": "ULCNFB" }
// Consumer credit
{ "series_id": "TOTALSL" }
GDP Decomposition
Analyze sources of growth:
// Total GDP
const gdp = {
"series_id": "GDP",
"observation_start": "2020-Q1",
"units": "pca"
};
// Components
const components = {
consumption: "PCEC", // Personal consumption (68%)
investment: "GPDI", // Gross private investment (17%)
government: "GCE", // Government spending (17%)
net_exports: "NETEXP" // Exports - Imports
};
GDP = C + I + G + (X - M)Track component contributions to identify growth drivers
Inflation Analysis
Multiple Measures
Compare different inflation indicators:
Consumer Prices
Producer Prices
PCE Deflator
Import/Export Prices
// Headline CPI
{
"series_id": "CPIAUCSL",
"units": "pc1"
}
// Core CPI (ex food & energy)
{
"series_id": "CPILFESL",
"units": "pc1"
}
// Services CPI
{
"series_id": "CUSR0000SAS",
"units": "pc1"
}
// Goods CPI
{
"series_id": "CUUR0000SAC",
"units": "pc1"
}
// Headline PPI
{
"series_id": "PPIACO",
"units": "pc1"
}
// Core PPI
{
"series_id": "PPILFE",
"units": "pc1"
}
// PCE Price Index (Fed's preferred)
{
"series_id": "PCEPI",
"units": "pc1"
}
// Core PCE
{
"series_id": "PCEPILFE",
"units": "pc1"
}
// Import prices
{
"series_id": "IR",
"units": "pc1"
}
// Export prices
{
"series_id": "IQ",
"units": "pc1"
}
Inflation Expectations
Monitor forward-looking indicators:
// University of Michigan 1-year ahead
{
"series_id": "MICH",
"observation_start": "2020-01-01"
}
// 5-year breakeven rate
{
"series_id": "T5YIE",
"observation_start": "2020-01-01"
}
// 10-year breakeven rate
{
"series_id": "T10YIE",
"observation_start": "2020-01-01"
}
Labor Market Analysis
Employment Dynamics
// Comprehensive labor market dashboard
const labor_dashboard = {
// Headline indicators
unemployment: "UNRATE",
payrolls: "PAYEMS",
participation: "CIVPART",
// Broader measures
u6_rate: "U6RATE",
employment_ratio: "EMRATIO",
part_time_economic: "LNS12032194",
// Flows
initial_claims: "ICSA",
continuing_claims: "CCSA",
job_openings: "JTSJOL",
quits: "JTSQUL",
// Wages
avg_hourly_earnings: "CES0500000003",
real_earnings: "CES0500000030",
unit_labor_costs: "ULCNFB"
};
Sectoral Employment
Track employment by industry:
Manufacturing
{ "series_id": "MANEMP" }
Construction
{ "series_id": "USCONS" }
Retail Trade
{ "series_id": "USTRADE" }
Healthcare
{ "series_id": "CES6500000001" }
Leisure & Hospitality
{ "series_id": "CES7000000001" }
Financial Conditions
Interest Rate Analysis
// Yield curve
const yield_curve = {
one_month: "DGS1MO",
three_month: "DGS3MO",
six_month: "DGS6MO",
one_year: "DGS1",
two_year: "DGS2",
five_year: "DGS5",
ten_year: "DGS10",
thirty_year: "DGS30"
};
// Spreads
const spreads = {
ten_two: "T10Y2Y",
ten_three_month: "T10Y3M",
bbb_treasury: "BAMLC0A4CBBB"
};
Financial Stress Indicators
// St. Louis Fed Financial Stress Index
{
"series_id": "STLFSI2",
"observation_start": "2020-01-01"
}
// TED Spread
{
"series_id": "TEDRATE",
"observation_start": "2020-01-01"
}
// VIX (market volatility)
{
"series_id": "VIXCLS",
"observation_start": "2020-01-01"
}
Sectoral Analysis
Housing Market
const housing_metrics = {
// Supply
starts: "HOUST",
permits: "PERMIT",
under_construction: "UNDCONTSA",
// Demand
existing_sales: "EXHOSLUSM495S",
new_sales: "HSN1F",
// Prices
case_shiller: "CSUSHPISA",
fhfa_index: "USSTHPI",
median_price: "MSPUS",
// Affordability
mortgage_rate: "MORTGAGE30US",
affordability_index: "FIXHAI"
};
Manufacturing Sector
// Production
{
"series_id": "IPMAN",
"observation_start": "2020-01-01",
"units": "pc1"
}
// Orders
{
"series_id": "AMTMNO",
"observation_start": "2020-01-01"
}
// Capacity utilization
{
"series_id": "MCU",
"observation_start": "2020-01-01"
}
// PMI
{
"series_id": "MANEMP",
"observation_start": "2020-01-01"
}
Regional Analysis
State Comparisons
// Compare unemployment across states
const states = ["CA", "TX", "NY", "FL", "IL"];
states.forEach(state => {
fred_get_series({
series_id: `${state}UR`,
observation_start: "2020-01-01"
});
});
// State GDP
states.forEach(state => {
fred_get_series({
series_id: `${state}NGSP`,
observation_start: "2020-Q1"
});
});
Metro Area Analysis
// Major metro unemployment rates
const metros = {
nyc: "LAUMT364190",
la: "LAUMT064910",
chicago: "LAUMT121410",
dallas: "LAUMT481910",
houston: "LAUMT482610"
};
Recession Analysis
NBER Dating
// Official recession indicator
{
"series_id": "USREC",
"observation_start": "2000-01-01"
}
// Sahm Rule Recession Indicator
{
"series_id": "SAHMREALTIME",
"observation_start": "2000-01-01"
}
Recession Depth and Duration
Identify Peak
Find pre-recession highs in key indicators
Track Trough
Identify lowest points during recession
Measure Recovery
Calculate time to return to pre-recession levels
Compare Recessions
Analyze differences in depth, duration, and recovery shape
International Comparisons
Global Indicators
// OECD data
const global = {
// Composite leading indicators
us_cli: "USALOLITONOSTSAM",
oecd_cli: "USALOLITONOSTSAM",
// GDP
g7_gdp: "NAEXKP01",
china_gdp: "CHNNGDP",
eu_gdp: "CLVMNACSCAB1GQEU272020",
// Trade
global_trade: "WLDXTIMVA"
};
Best Practices
Don’t rely on single metrics. Cross-validate with complementary series.
Economic data undergoes revisions. Use vintage data for historical analysis.
Understand Seasonal Patterns
Use SA data for trends, NSA for actual seasonal patterns.
Account for reporting lags and indicator relationships.
Record transformation choices and analysis assumptions.
Analysis Workflow
Define Question
Clearly state what you’re analyzing
Identify Indicators
Select relevant series for your analysis
Gather Data
Retrieve series with appropriate transformations
Analyze Trends
Look for patterns, correlations, and anomalies
Validate Findings
Cross-check with alternative indicators
Document Results
Record methodology and conclusions
Next Steps
