Answer:
The independent variables are exogenous, i.e. they are not caused by the variables.
The independent variables are not related to each other.
Error follows a normal distribution with constant variance.
Step-by-step explanation:
The OLS estimator can generally be used when the following conditions are met:
- The independent variables are exogenous, i.e. they are not caused by the variables.
-The independent variables are not related to each other.
-Error follows a normal distribution with constant variance.
If any of these conditions is false, you may be concerned about identification. Discrimination refers to the ability to predict the outcome of independent variables.
If the independent variable is not exogenous, it may be associated with time error, which can distort the estimate. If the independent variables are related, the OLS estimator will be one, that is, the coefficients are not unique. If the error is not normally distributed, the OLS estimator will not work well.
There are many things you can do to fix authentication issues. One is to add more controls to the model.
This will help reduce the association between individual variables and time errors. Another is to use a different estimation method, such as measured variance or two-stage least squares.
If you are unsure whether there is a validation issue, it is best to see a validation professional.
Here are some tips for using the OLS estimation:
- Use packages that provide tests for OLS regression. Note the limitations of the OLS regression, such as its sensitivity to outliers and its inability to manage nonlinear relationships.
- Use OLS regression as a starting point for your analysis, but be willing to consider other methods if the assumptions of OLS regression are not met or the outcome needs to be estimated.