FRED Data for Shipping Analysis

What FRED Is and Why It Exists

FRED -- the Federal Reserve Economic Data system -- is a free, public database maintained by the Federal Reserve Bank of St. Louis. It contains over 800,000 time series covering macroeconomics, finance, trade, labor, prices, and dozens of other categories, drawn from more than 100 government agencies, central banks, and international organizations. The Bureau of Labor Statistics, Bureau of Economic Analysis, Census Bureau, US Treasury, European Central Bank, World Bank, and International Monetary Fund all feed data into FRED.

The St. Louis Fed launched FRED in 1991 as a dial-up bulletin board system. It moved to the web in 1996 and has been free to use without registration ever since. You do not need an account to browse, chart, or download any data series. An API key (also free) is required only for programmatic access.

For shipping-price analysis, FRED matters because it is the single best place to pull the economic data you need to connect maritime freight rates to consumer prices. Oil prices, CPI components, producer prices, import price indices, trade balance data, inflation expectations, consumer sentiment -- all of it is available in one place, in one format, with consistent date alignment and unit labeling. If you are building a spreadsheet, a Python script, or a research paper that tracks how shipping disruptions transmit into the real economy, FRED is where you start.

Accessing FRED: Web Interface and API

The Web Interface

Navigate to fred.stlouisfed.org and type a series ID or keyword into the search bar. Every data series has a dedicated page showing a chart, the raw data in table form, metadata (source, release schedule, units, seasonal adjustment), and tools for transforming the data directly in the browser.

The web interface supports three transformations that matter for shipping analysis. First, you can change the units to "Percent Change from Year Ago" to see year-over-year inflation rates without downloading anything. Second, you can overlay multiple series on one chart -- for example, plotting DCOILBRENTEU (Brent crude) against CPIENGSL (CPI energy component) to visually inspect the lag between oil price movements and consumer energy prices. Third, you can set custom date ranges to isolate specific disruption events: the 2021 Suez blockage, the 2022 energy shock, the 2023-24 Red Sea crisis.

FRED also offers a "My Account" dashboard where you can save custom charts, build data lists, and set up email notifications when specific series are updated. Registration is free. None of this is required for basic use, but it saves time if you are monitoring the same dozen series week after week.

The FRED API

For programmatic access, the FRED API provides RESTful endpoints that return JSON or XML. You need an API key, which you can obtain for free at fred.stlouisfed.org/docs/api/api_key.html. Registration takes about 30 seconds and requires only an email address.

The core endpoint is GET /fred/series/observations, which returns the data points for a given series ID. You can specify date ranges, sort order, and output units (including percent change) directly in the API call. For example, requesting CPIAUCSL with units=pc1 returns year-over-year percent change rather than the raw index value -- the API does the math for you.

Python users can skip the raw API entirely and use the fredapi package (installable via pip) or the pandas-datareader library, both of which wrap the API and return pandas DataFrames directly. R users have the fredr package. Excel users can pull FRED data through the built-in web query function or third-party add-ins.

The Series That Matter for Shipping-Price Analysis

FRED has 800,000 series. Most are irrelevant to shipping economics. The following fifteen are the ones you will actually use. Each series ID is a permanent identifier -- bookmark it, hardcode it in your scripts, cite it in your research. These IDs do not change.

A few notes on this list. CPIAUCSL is the seasonally adjusted CPI. The non-adjusted version is CPIAUCNS. For most shipping-price analysis, use the seasonally adjusted series because you want to isolate disruption effects from normal seasonal patterns (heating oil demand in winter, gasoline demand in summer). The one exception is when you specifically want to study seasonal shipping patterns -- then the unadjusted series preserves the seasonality you care about.

DCOILWTICO and DCOILBRENTEU deserve separate attention. WTI is the US domestic benchmark, priced at Cushing, Oklahoma -- a pipeline hub with no ocean access. Brent is priced at a North Sea loading terminal and better reflects global seaborne crude markets. When the Strait of Hormuz is under threat, Brent reacts first and WTI follows with a lag. The spread between them (Brent minus WTI) is itself a useful indicator of maritime risk: a widening spread often signals that seaborne crude is becoming more expensive relative to pipeline-delivered crude.

Computing Month-over-Month and Year-over-Year Changes

Raw index values are almost never what you want to report or chart. The CPI was 314.069 in March 2024. That number means nothing to most readers. What matters is the rate of change: how fast prices are rising, and whether that rate is accelerating or decelerating. FRED supports two standard transformations.

Both transformations are available directly in the FRED interface without downloading or computing anything yourself. The API unit codes are: lin (levels, no transformation), chg (change from previous period), pch (percent change), pc1 (percent change from year ago), pca (compounded annual rate of change), and log (natural log).

Building Comparison Charts

The most useful FRED charts for shipping-price analysis overlay two or more series on the same time axis to reveal lead-lag relationships. The web interface makes this straightforward: open any series, click "Edit Graph," then use the "Add Line" button to include additional series. You can set each series to its own axis (left or right) if the scales differ, and apply independent unit transformations to each line.

Three comparison charts are particularly informative for shipping analysis.

Each of these charts can be saved to your FRED account, embedded on a website, or exported as an image or CSV. FRED generates stable URLs for custom charts, making them suitable for research papers and articles that need reproducible, linkable data visualizations.

Using FRED for Research and Citation

FRED is a secondary source. The data originates from primary sources -- the Bureau of Labor Statistics for CPI and PPI, the Energy Information Administration for oil prices, the Census Bureau for trade balance data. FRED aggregates and standardizes these sources into a common format.

For academic papers, the correct citation format references both the primary source and FRED as the access point. The St. Louis Fed provides suggested citations on every series page. A typical citation reads: "U.S. Bureau of Labor Statistics, Consumer Price Index for All Urban Consumers: All Items in U.S. City Average [CPIAUCSL], retrieved from FRED, Federal Reserve Bank of St. Louis." Include the retrieval date, because FRED data is subject to revision.

For journalism and market commentary, FRED provides the credibility of government-source data without requiring you to navigate each agency's individual data portal. It is considered a reliable, non-partisan source by editors, fact-checkers, and peer reviewers. The St. Louis Fed does not editorialize the data; it hosts and serves it.

FRED also offers GeoFRED (geographic data mapped at the county and metro level), FRASER (historical documents and discontinued data series), and ALFRED (archival data showing what each series value was as of a specific vintage date -- useful for studying data revisions). For shipping-price work, ALFRED is occasionally useful when you want to know what the CPI reading was on the day of a disruption event, before subsequent revisions changed the number.

Limitations: Lag Times, Revisions, and Gaps

FRED data is only as current as the source agencies that publish it. This creates publication lags that matter when you are trying to track the real-time effects of a shipping disruption.

Revisions are the second limitation. The BLS revises CPI data once annually (in February, revising the prior five years of seasonally adjusted data). Trade balance data from the Census Bureau is revised monthly for two months after initial publication. PPI undergoes annual revisions. Import price data is revised in the month after initial release. The first published number for any monthly series is a preliminary estimate, not a final figure. If you are building a model that reads from FRED in real-time, you need to account for the possibility that the number you pulled today will be different next month.

The third limitation is coverage gaps. FRED does not carry container freight rate indices (no Freightos Baltic Index, no Shanghai Containerized Freight Index, no Drewry World Container Index). It does not carry the Baltic Dry Index. It does not carry AIS vessel tracking data or port throughput statistics. For the shipping-specific half of any shipping-price analysis, you need data sources outside FRED -- Yahoo Finance for BDI, Freightos for container rates, MarineTraffic or VesselFinder for AIS data. FRED covers the price side of the equation. The shipping side requires separate sources that you then join to FRED data by date.

Finally, FRED's geographic scope is US-centric. CPI, PPI, and import prices are US measures. If you are studying how shipping disruptions affect European or Asian consumer prices, you will need Eurostat for HICP (European inflation), Japan's Statistics Bureau for Japanese CPI, or the relevant national statistics office. FRED does carry some international series (Brent crude, for instance, and selected OECD data), but the depth of coverage drops sharply outside the United States.

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