US Economic Data Analysis

This repository contains Jupyter notebooks that visually analyze US Economic data as provided by St. Louis Fed, the OECD, and Yahoo-Finance. The analysis is carried out using Pandas, Pandas datareader, and Matplotlib.

So far I created the following notebooks (following a given link lets you see the most recent run of its notebook, I aim to refresh results monthly):

• Analysis of CPI, Fed Funds Rate, Treasury rates and Inflation expectations, plus a similar analysis for the Eurozone by way of comparison
• Analysis of the evolution of the US Federal Public Debt
• Analysis of the evolution of the ownership structure of US Federal Debt
• Analysis of changes in M2, Real Personal Consumption Expenditures (PCE), Wage Inflation and CPI
• Analysis of Participation, Employment, Unemployment, Job-vacancy, and Unfilled Vacancies to Population Rates
• Analysis of US Money Supply, plus a similar analysis for the Eurozone by way of comparison
• Analysis of Quantitative Easing and Tapering by the Federal Reserve
• Analysis of US Treasury Yields' Spreads
• Analysis of US Past, Current, and Future Riskfree Rates, plus a similar analysis for the Eurozone
• Analysis of US Industrial Production
• Analysis of US GDP, its composition by industry, and trends in its make-up, plus a similar analysis for the Eurozone
• Analysis of US Labor productivity (incl. comparison with that in EU)
• Analysis of disposable income and savings rates of US individuals, plus a similar analysis for the Eurozone
• Analysis of US Stock Market

Requirements

You'll need python3 and pip. brew install python will do if you are on MacOS. You can even forgo installing anything and run these notebooks in Google cloud, as I outline below.

In case you opt for a local installation, the rest of the dependencies can be installed as follows:

python3 -m pip install -r requirements.txt

NB: I use Yahoo-Finance data in the Current_Riskfree_Rates.ipynb notebook. Unfortunately Yahoo recently changed their API, as a result the last official version of pandas-datareader fails when retrieving data from Yahoo-Finance. To overcome it, until a new version of pandas-datareader addresses this, I added a dependency on yfinance and adjusted the notebook to make a yfin.pdr_override() call in each notebook that requires data from Yahoo-Finance.

How to run locally

If you want to run the notebooks locally on your laptop, clone the repo and cd into its directory, e.g.:

git clone -l -s https://github.com/ilchen/US_Economic_Data_Analysis.git
cd US_Economic_Data_Analysis

run one of the below commands depending on which notebook you are interested in:

jupyter notebook CPI_and_Fed_Funds_Rates.ipynb

or

jupyter notebook Fed_Public_Debt_and_Fed_Tax_Revenue.ipynb

or

jupyter notebook Fed_Public_Debt_Holders.ipynb

or

jupyter notebook M2_PCE_and_CPI.ipynb

or

jupyter notebook Unemployment_and_Participation_Rates.ipynb

or

jupyter notebook Money_Supply.ipynb

or

jupyter notebook Quantitative_Easing_and_Tapering.ipynb

or

jupyter notebook Interest_Rate_Spreads.ipynb

or

jupyter notebook Current_Riskfree_Rates.ipynb

or

jupyter notebook Industrial_Production.ipynb

or

jupyter notebook GDP_Composition.ipynb

or

jupyter notebook Labor_Productivity.ipynb

or

jupyter notebook Disposable_Income_and_Savings.ipynb

or

jupyter notebook Stock_Market.ipynb

How to run in Google cloud

You can also run these notebooks in Google cloud. This way you don't need to install anything locally. This takes just a few seconds:

1. Go to Google Colaboratory in your browser
2. In the modal window that appears select GitHub
3. Enter the URL of this repository's notebook, e.g.: https://github.com/ilchen/US_Economic_Data_Analysis/blob/main/Fed_Public_Debt_and_Fed_Tax_Revenue.ipynb or https://github.com/ilchen/US_Economic_Data_Analysis/blob/main/CPI_and_Fed_Funds_Rates.ipynb or https://github.com/ilchen/US_Economic_Data_Analysis/blob/main/Fed_Public_Debt_Holders.ipynb or https://github.com/ilchen/US_Economic_Data_Analysis/blob/main/M2_PCE_and_CPI.ipynb or https://github.com/ilchen/US_Economic_Data_Analysis/blob/main/Unemployment_and_Participation_Rates.ipynb or https://github.com/ilchen/US_Economic_Data_Analysis/blob/main/Money_Supply.ipynb or https://github.com/ilchen/US_Economic_Data_Analysis/blob/main/Interest_Rate_Spreads.ipynb or https://github.com/ilchen/US_Economic_Data_Analysis/blob/main/Current_Riskfree_Rates.ipynb or https://github.com/ilchen/US_Economic_Data_Analysis/blob/main/Quantitative_Easing_and_Tapering.ipynb or https://github.com/ilchen/US_Economic_Data_Analysis/blob/main/Industrial_Production.ipynb or https://github.com/ilchen/US_Economic_Data_Analysis/blob/main/GDP_Composition.ipynb or https://github.com/ilchen/US_Economic_Data_Analysis/blob/main/Labor_Productivity.ipynb
4. Click the search icon
5. Enjoy
   In some of the notebooks I make use of additional python code I developed (e.g. Current_Riskfree_Rates.ipynb) or dependencies that are not by default provisioned in Google Colaboratory. When running these notebooks in Colaboratory, it's important to clone this repository and cd to it. I crated a commented out cell at the beginning of these notebooks to make it easier. Please don't forget to uncomment its content and run it first. E.g. here's one from Current_Riskfree_Rates.ipynb:

# Uncomment if running in Google Colaboratory, otherwise the import of the curves module in the cell below will fail
#!git clone -l -s https://github.com/ilchen/US_Economic_Data_Analysis.git cloned-repo
#%cd cloned-repo

# Install the latest version of pandas-datareader and yfinance
# !pip install pandas-datareader -U
# !pip install yfinance -U