Linear Regression in Finance and Macroeconomics Using Python: A Complete Guide with Practical Applications

This article teaches how to use Python to build and interpret linear regression models in finance and macroeconomics, enabling data-driven forecasting and insights into economic relationships using real-world analytical techniques.

Introduction

In today's data-rich world, quantitative modeling has become indispensable for professionals working in finance and macroeconomics. Whether you're forecasting GDP growth, analyzing the effects of interest rates, or modeling stock returns, regression analysis provides a fundamental framework for data-driven decision-making.

Linear regression, in particular, is a widely used technique to model the relationship between a dependent variable and one or more independent variables. Its appeal lies in its simplicity, interpretability, and the broad scope of problems it can address.

This article provides a step-by-step walkthrough on how to implement linear regression in Python using pandas, numpy, matplotlib, seaborn, and scikit-learn. We will model the relationship between investment as a percentage of GDP and GDP growth rate to demonstrate its practical application in macroeconomic analysis.