Multiple Linear Regression in Economics Using R: A Step-by-Step Guide to Analysing Economic Relationships

This article provides a complete workflow for applying multiple linear regression in R to analyse complex economic relationships, equipping economists and analysts with the tools to interpret, forecast, and communicate data-driven insights for informed decision-making.

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Article Outline:

1. Introduction

   • The growing role of quantitative modelling in economics

   • Why multiple linear regression is fundamental for analysing economic systems

   • The advantages of using R for economic data analysis, modelling, and visualisation

2. Understanding Multiple Linear Regression in Economics

   • What is multiple linear regression?

   • Key components: intercept, coefficients, residuals, adjusted R-squared

   • Examples of economic applications:

     • Modelling GDP as a function of investment, consumption, and government expenditure

     • Estimating inflation using money supply, interest rates, and unemployment

     • Analysing determinants of wage levels

3. Preparing Economic Data in R

   • Loading and preparing economic datasets (e.g., with tidyverse)

   • Exploring, cleaning, and visualising economic variables

   • Checking for correlations and data quality issues before modeling

4. Building a Multiple Linear Regression Model in R

   • Using lm() to relate an economic outcome (e.g., GDP) to multiple predictors

   • Interpreting regression coefficients, p-values, and adjusted R-squared

   • Extracting fitted values, residuals, and making predictions

5. Interpreting and Evaluating the Model

   • Understanding the economic meaning of each coefficient

   • Assessing overall model fit with adjusted R-squared and residual analysis

   • Checking regression assumptions: linearity, multicollinearity, normality, homoscedasticity

6. Forecasting and Scenario Analysis

   • Predicting economic outcomes for hypothetical scenarios

   • Generating confidence intervals for forecasts

   • Practical tips for presenting results to stakeholders

7. Visualising Multiple Regression Models in R

   • Scatter plots, partial regression plots, and coefficient plots using ggplot2

   • Visualising residuals and diagnostics

   • Creating publication-quality graphics for economic reports

8. Best Practices and Extensions

   • Handling multicollinearity and overfitting

   • Interpreting interaction terms and polynomial effects

   • Extensions: time-series regression, panel data, and machine learning approaches

9. Conclusion

   • Recap of the value of multiple linear regression for economic analysis

   • How R enables transparent, reproducible modelling

   • Next steps for advancing economic modelling and forecasting

1. Introduction

In the era of big data and complex economic challenges, quantitative analysis has never been more critical for economists, policymakers, and financial analysts. Understanding the underlying relationships among economic variables—such as growth, inflation, investment, and unemployment—is fundamental to crafting effective policies and driving informed business decisions.

Multiple linear regression is one of the most versatile and widely used methods for examining how several independent variables jointly influence a dependent variable. While simple linear regression relates just one predictor to an outcome, multiple regression allows us to measure the effect of each predictor while controlling for others. This power to isolate and quantify relationships is invaluable in economics, where variables rarely act in isolation.

R, a leading platform for statistical computing and data visualisation, offers an accessible and powerful environment for regression modelling. With packages like tidyverse, ggplot2, and broom, analysts can efficiently manage data, fit models, and communicate insights.

This article provides a step-by-step guide to applying multiple linear regression in economics using R. From data preparation and visualisation to model fitting, interpretation, and reporting, you’ll gain the practical and conceptual tools to unlock actionable economic insights.

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