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.

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