Heteroskedasticity

Heteroskedasticity is a statistical phenomenon in which the variance of a variable is not constant across different values of another variable. This can cause problems in statistical analysis, as it can lead to biased and inaccurate results.

There are a number of different causes of heteroscedasticity, including:

• Non-linear relationships between variables
• Heterogeneity in the data
• Measurement error
• Omitted variable bias

Heteroskedasticity can be detected using a number of statistical tests, such as the Breusch-Pagan test and the White test. Once heteroscedasticity has been detected, it can be corrected using a number of different methods, such as weighted least squares regression and generalized least squares regression.

It is important to correct for heteroscedasticity in statistical analysis, as it can lead to biased and inaccurate results. By using the appropriate statistical tests and correction methods, you can ensure that your results are accurate and reliable.