Aaron

Understand OLS Linear Regression with a bit of mathImage by AuthorHeteroskedasticity and Autocorrelation are unavoidable issues we need to address when setting up a linear regression. In this article, let’s dive deeper into what are Heteroskedasticity and Autocorrelation, what are the Consequences, and remedies to handle issues.A typical linear regression takes the form as follows. The response variable (i.e., Y) is explained as a linear combination of explanatory variables (e.g., the intercept, X1, X2, X3, …) and ε is…

Understand OLS Linear Regression with a bit of mathImage by AuthorThe OLS estimator is known to be unbiased, consistent and BLUE (Best Linear Unbiased Estimator). But what do these properties mean? Why are they important for a linear regression model? In this article, we will discuss these properties.A typical linear regression looks like something as follows. The response variable (i.e., Y) is explained as a linear combination of explanatory variables (e.g., the intercept, X1, X2, X3, …) and ε is the error term (i.e., a…