Forecasting Non-Farm Payrolls With Linear Regression

A Step-by-Step Guide to Building a Non-farm Payrolls Regression Model

Linear regression is sometimes looked down upon due to its simplicity and its linear-dependent outputs. However, a lot of complex forecasting tasks can be solved using linear regression. I was once told by a very successful hedge fund manager that one of their sophisticated trading models relied on a simple linear regression model.

This article will show how to forecast employement data from the United States using a simple linear regression in Python.

What Are Non-Farm Payrolls?

Non-farm payrolls (NFP) is a critical and widely watched economic indicator in the United States, serving as a key barometer for the health of the country’s labor market. This monthly employment report provides a comprehensive snapshot of the employment situation in the United States, excluding jobs in the agricultural sector, private households, and nonprofit organizations.

The NFP report reveals the net change in the total number of paid employees in the United States, excluding these specific sectors, during the previous month. It is particularly significant because it offers insights into the overall strength and direction of the U.S. economy. By analyzing the data, economists, policymakers, investors, and businesses can gauge the health of the labor market, track employment trends, and make informed decisions regarding economic policies, investments, and hiring practices.

Moreover, the NFP report’s influence extends beyond the financial markets, as it often impacts currency exchange rates, interest rate decisions, and other financial instruments. Economists try to forecast the upcoming number every month. Let’s try to apply our own model and see what it tells us.

We will evaluate our forecasts using the directional accuracy and the RMSE. Here’s what they refer to:

• The directional accuracy is simply a binary up or down measure that compares the number of the correct forecasts (NFP up versus NFP down) to the number of the predictions.
• The RMSE stands for root mean squared error. It is a measure of the average magnitude of…