What are the assumptions of ordinary least squares (OLS) in econometrics
Ordinary Least Squares (OLS) is the simplest and most widely used estimation technique in econometrics. It provides the Best Linear Unbiased Estimators (BLUE) for the parameters in a linear regression model, provided a specific set of assumptions, known as the Classical Linear Model (CLM) assumptions, are met.
When these assumptions hold, the OLS estimates are considered reliable and trustworthy for statistical inference. When they are violated, the results can be misleading, biased, or inefficient.
Here are the nine core assumptions of OLS, grouped by their impact on your model.
Group 1: Assumptions for Linearity and Data Quality
These assumptions ensure the model is correctly specified and the data is appropriate.
1. Linearity in Parameters
The relationship between the dependent variable and the independent variables must be linear in the parameters.
- Formal Statement:
- Implication: This does not mean the variables themselves must be linear; you can use transformations like