KANDA DATA

  • Home
  • About Us
  • Contact
  • Sitemap
  • Privacy Policy
  • Disclaimer
  • Bimbingan Online Kanda Data
Menu
  • Home
  • About Us
  • Contact
  • Sitemap
  • Privacy Policy
  • Disclaimer
  • Bimbingan Online Kanda Data
Home/Data Analysis in R/How to Analyze Heteroskedasticity for Time Series Data in Multiple Linear Regression and Its Interpretation

Blog

700 views

How to Analyze Heteroskedasticity for Time Series Data in Multiple Linear Regression and Its Interpretation

By Kanda Data / Date Dec 14.2024 / Category Data Analysis in R

The heteroskedasticity test is one of the assumption tests in the Ordinary Least Squares (OLS) linear regression method, aimed at ensuring that the residual variance remains constant. If the multiple linear regression equation being tested shows non-constant residual variance, this is referred to as heteroskedasticity.

To obtain the Best Linear Unbiased Estimator (BLUE) for time series data, it is essential to ensure that the assumption of constant residual variance is met. Constant residual variance is also known as homoskedasticity.

In this article, I will provide a tutorial on how to perform a heteroskedasticity test in multiple linear regression using R Studio. Before performing the heteroskedasticity test in R Studio, you need to conduct a linear regression analysis.

Example Research Case Study

As a practice exercise, I will use a research case study aimed at analyzing the effects of inflation and unemployment rates on economic growth in a country.

For this case study, I use quarterly time series data with a total of 30 observations. The regression equation for this case study can be specified as follows:

π‘Œ=𝛽0+𝛽1𝑋1+𝛽2𝑋2+…+𝛽𝑛𝑋𝑛+πœ–

Where:

π‘Œ: Economic growth (%),

𝑋1: Inflation rate (%),

𝑋2: Unemployment rate (%),

𝛽0 : Intercept,

𝛽1 and 𝛽2: Regression coefficient,

πœ– : Error or residual.

After defining the regression equation, the next step is to input and tabulate the data. The collected data can be entered into Excel and then imported into R Studio. For a tutorial on importing data from Excel to R Studio, please refer to the article I wrote previously.

This is the time series data that we will use as a practice example in this article.

Steps for Conducting a Heteroskedasticity Test in R Studio and Its Interpretation

Once the data has been successfully imported into R Studio, the next step is to perform a multiple linear regression analysis. To conduct multiple linear regression analysis in R Studio, use the following command:

model <- lm(Economic_Growth ~ Inflation_Rate + Unemployment_Rate, data = data)

summary(model)

If the command is entered correctly, pressing Enter will display the R Studio output as follows:

Heteroskedasticity detection can be tested using the Breusch-Pagan test in R Studio. In this article, we will demonstrate how to detect heteroskedasticity using the Breusch-Pagan test.

If you have not installed the β€˜lmtest’ packages before, you need to install it first. If you already have the package installed, you can skip this step.

To install the lmtest package, enter the following command in R Studio:

install.packages(“lmtest”)

Once the lmtest packages has been installed successfully, the next step is to perform the Breusch-Pagan test in R Studio by entering the following command:

library(lmtest)

bptest(model)

If the command is entered correctly without any errors, pressing Enter will display the analysis output as follows:

studentized Breusch-Pagan test

data:Β  model

BP = 11.09, df = 2, p-value = 0.003906

Based on the output, we can see that the Breusch-Pagan value for the practice data is 11.09. Next, we observe the p-value, which is 0.003906.

Since the p-value is less than 0.05, we reject the null hypothesis (accept the alternative hypothesis). Thus, it can be concluded that heteroskedasticity exists in the tested regression equation. This indicates that the equation does not meet the assumptions required for OLS linear regression.

This concludes the tutorial on heteroskedasticity testing that I can share at this time. I hope this is useful and enhances the knowledge of those learning and using this analysis. Stay tuned for updates from Kanda Data next week, where we will discuss multicollinearity testing. See you next week!

Tags: Breusch-Pagan Test, Data Analysis Tutorial, Heteroskedasticity test, Kanda data, multiple linear regression, R programming tutorial, R Studio, Regression Analysis, Regression Assumptions, statistics, time series data

Related posts

How to Determine the Minimum Sample Size in Survey Research to Ensure Representativeness

Date Oct 02.2025

Regression Analysis for Binary Categorical Dependent Variables

Date Sep 27.2025

How to Sort Values from Highest to Lowest in Excel

Date Sep 01.2025

Categories

  • Article Publication
  • Assumptions of Linear Regression
  • Comparison Test
  • Correlation Test
  • Data Analysis in R
  • Econometrics
  • Excel Tutorial for Statistics
  • Multiple Linear Regression
  • Nonparametric Statistics
  • Profit Analysis
  • Regression Tutorial using Excel
  • Research Methodology
  • Simple Linear Regression
  • Statistics

Popular Post

October 2025
M T W T F S S
 12345
6789101112
13141516171819
20212223242526
2728293031  
« Sep    
  • How to Determine the Minimum Sample Size in Survey Research to Ensure Representativeness
  • Regression Analysis for Binary Categorical Dependent Variables
  • How to Sort Values from Highest to Lowest in Excel
  • How to Perform Descriptive Statistics in Excel in Under 1 Minute
  • How to Tabulate Data Using Pivot Table for Your Research Results
Copyright KANDA DATA 2025. All Rights Reserved