In the United States, the Federal Reserve Bank of St. Louis maintains a huge set of economic related time series data, mostly covering the U.S., but some are international. This collection is referred to as FRED. FRED is your friend, he knows a lot.
He knows the Consumer Price Index For All Urban Consumers, by month. FRED knows the Civilian Unemployment Rate and Real Median Household Income. He knows the Estimate of People of All Ages in Poverty for United States and the number of internet users in the world.
There are over 470,000 U.S. and international time series from over 80 different sources in FRED. FRED has useful tools such as a Microsoft Excel Add-in and some mobile apps for your smartphone. FRED even has a forecasting game you can play called FREDcast. FRED is located at https://fred.stlouisfed.org/.
FRED has a geospatial component called GeoFRED. You can use it to create a map version of the time series data or to download shapefiles. The following screenshot is an example showing the 2014 90% Confidence Interval Lower Bound of Estimate of Median Household Income by County:
FRED has three developer APIs:
- FRED APIs: This can be used to programmatically retrieve time series data from the FRED datasets.
- FRASER APIs: This API can be used to retrieve metadata from the Federal Reserve Archival System for Economic Research (FRASER). FRASER holds historical documents and archives.
- GeoFRED APIs: This allows programmatic access to GeoFRED data and shapefiles.
You will need to obtain an API key from the FRED website to use them. Another way to grab FRED data is from using an R package such as Quandl. Install the package from RStudio GUI or the install.packages function. You can then use it to get data from several financial data sources including FRED. The following is some sample R code that gets the U.S. GDP time series data:
#load Quandl library
library(Quandl)
#Grab US GDP time series data from FRED
gdp_ts = Quandl("FRED/GDP")
#Display
View(gdp_ts)
Using code libraries in R allows you to automate the process to incorporate the data on a regular basis into your analytics environment.