1. The expected value or the mean of the error term is said to be zero
However, in some other cases, the mean of the residual or error term is not exactly zero due to factors like rounding off.
2. The variance of the error term is constant.
When the variance of the error term is constant, this is the assumption of homoskedasticity. However, if the variance is not constant, that is the case of heteroskedasticity (a violation of the OLS assumption) of homoskedasticity. Therefore, the variance of the error term should be constant. The problem of heteroskedasticity is common in cross-sectional data.
3. The assumption of Normality
The error term is assumed to follow a normal distribution with a mean of zero and a variance which is constant.
4. There is a linear relationship between the dependent and the independent variables that is Y= a + Bx + e. thus the relationship between X and Y is linear in the OLS parameters ‘a’ and ‘B’
5. Assumption of no multicollinearity
Multicollinearity is a situation in which the independent variables (x1, x2 ….) are correlated. It is also a violation of OLS assumption. If there is a problem of multicollinearity, it means that we cannot obtain the values of the parameters a, B1 and B2 hence there should not be a problem of multicollinearity.
6. Assumption of zero correlation between the independent variable and error term. That is the error term and the independent variable should not be correlated.
7. The assumption of zero auto correlation
The error term in period (i) and the error term in period (j) should not be correlated. Thus, there should not be auto correlation also known as serial correlation. The problem of auto correlation is therefore a violation of OLS assumption and is common in time series data.
8. No outliers in the data
An outlier is a value that is very large or very small in relation to the rest of the other observations.
Dullayo answered the question on April 22, 2018 at 09:25
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