1. Multicollinearity
Multi-collinearity is a type of disturbance in data, where the independent variables are related to each other making the statistical inference redundant.
2. Heteroscedasticity
Heteroscedasticity or Heteroskedasticity occurs when the size of the error term varies across the values of the independent variable.
3. Auto correlation
It measures the relationship between a variables’ current value and past value.
4. Model Specification Errors
This error occurs due to misspecification of assumption or variable while setting the model.
5. Non-stationarity of Time Series
It has dynamic variable and mean over time in contrast to stationary time series.