Macroeconomic data

Quantiacs provides historical macroeconomic datasets. Currently datasets from the U.S. Bureau of Labor Statistics are included, covering consumer prices, employment and unemployment, compensation, working conditions, and productivity.

  • Bureau of Labor Statistics data

Bureau of Labor Statistics data

The U.S. Bureau of Labor Statistics is the main U.S. government agency for labor economics and statistics. It publishes macroeconomic data on prices, employment and unemployment, compensation, working conditions, and productivity. Quantiacs hosts these datasets on its cloud and also makes them available for local use. For more on using the Quantiacs toolbox offline, see the local installation instructions.

Loading the data

To see what data is available:

import pandas as pd
import qnt.data as qndata
dbs = qndata.blsgov.load_db_list()
display(pd.DataFrame(dbs))

which returns the list of datasets:

datasets

Each dataset shows an identifier, a name, and the date of the last update. Each dataset contains several time series that can be used as indicators. For example, AP (Average Consumer Prices) covers household fuel, motor fuel, and food items from prices collected for the Consumer Price Index.

The time series contained in the AP dataset can be displayed using:

series_list = list(qndata.blsgov.load_series_list('AP'))
display(pd.DataFrame(series_list).set_index('id'))

which returns:

AP

To inspect the metadata, see the template on our GitHub page or in the Examples section of your user space.

Preprocessing the data

Macroeconomic data should be preprocessed. Continuing with the previous example, we can select global U.S. data by its area code:

us_series_list = [s for s in series_list if s['area_code'] == '0000']
display(pd.DataFrame(us_series_list).set_index('id'))

area code

and later selecting global U.S. time series which are more relevant for forecasting global financial markets. Let us select time series which are currently being updated and have at least 20 years of history:

actual_us_series_list = [s for s in us_series_list if s['begin_year'] <= '2000' and s['end_year'] == '2021' ]
display(pd.DataFrame(actual_us_series_list).set_index('id'))

The preprocessing leads to a subset of interesting time series:

refined

Finally, single time series can be loaded using for example (we consider fuel oil):

series_data = qndata.blsgov.load_series_data('APU000072511', tail = 30*365)

# convert to pandas.DataFrame
series_data = pd.DataFrame(series_data)
series_data = series_data.set_index('pub_date')

# remove yearly average data
series_data = series_data[series_data['period'] != 'M13']

series_data

single series