US Macroeconomic Datasets

Production Data

The following are links to csv files containing selected data from the US National Income and Product and Product Accounts (NIPA) and a constructed measure of the US capital stock:

The series included from the NIPA are: GDP, consumption, private domestic investment, government consumption, exports, imports, net exports, and total labor hours. The labor statistic is in terms of billions of hours per year and all other series are in terms of constant billions of US dollars.

Figure: Annual growth rates in real GDP, TFP, labor hours, and the capital stock for the US from 1949.
Source: Federal Reserve Economic Data - FRED.

The data also use contain a measure of the US capital stock also in terms of billions of constant dollars. I constructed the capital stock series from the GDP and private domestic investment series using the perpetual inventory method (see Hall and Jones (1999) or Timothy Kehoe's notes for a description of the method). The Python code used to construct the datasets is available from this Github repository.

Inflation Forecasts

Inflation expectations have an important role in macroeconomic theory but most people do not have the expertise to form precise mathematical expectations of future inflation rates.

Below is a link to a dataset containing historical forecasts of the one-year ahead inflation rate:

The data are annual from 1970 and contains the forecast one-year ahead GDP deflator inflation rate, the actual one-year ahead GDP deflator inflation rate, and the one-year T-Bill rate.

Figure: Forecast and actual one-year ahead annual inflation rate for the US.
Source: Federal Reserve Bank of Philadelphia Survey of Professional Forecasters.

I constructed the dataset using data from the Federal Reserve Bank of Philadelphia Survey of Professional Forecasters and Federal Reserve Economic Data - FRED. I used the median (as opposed to the mean) forecast values reported. Find the code used to generate the dataset in this Github repository.