Review:
Generalized Least Squares
overall review score: 4.2
⭐⭐⭐⭐⭐
score is between 0 and 5
Generalized least squares (GLS) is a regression technique for estimating the unknown parameters in a linear regression model. It is used when the assumptions of ordinary least squares (OLS) regression are violated, such as when the errors are heteroscedastic or correlated.
Key Features
- Estimates unknown parameters in a linear regression model
- Handles heteroscedastic and correlated errors
- Provides more efficient estimates compared to OLS in certain situations
Pros
- Can handle violations of OLS assumptions
- Provides more efficient estimates in certain cases
Cons
- Can be computationally intensive in some cases
- Requires knowledge of statistical theory to implement correctly