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Question 1
In simple terms, multicollinearity can be defined as a state of high correlations among independent variables in a multiple regression model. Discuss possible consequences of multicollinearity for Ordinary Least Squares (OLS) estimators (word limit: 600 words).
Question 2
Explain the meaning of a “random walk”. Discuss the implications of finding that a series is a random walk.

Explanation & Answer

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Question 1
Multicollinearity is a common problem in statistics because it often makes it difficult
for one to define and interpret the variables effectively. Singh & Kumar (2021) define it as
significant intercorrelations amongst two or more independent variables in a multiple
regression model. Generally, multicollinearity can result in wider CIs (confidence intervals)
that generate unreliable probabilities regarding the effect on independent variables in a model.
On the other hand, OLS (ordinary least squares) estimators predict and estimate the relationship
between one or more independent variables against a dependent variable. From a general point
of view, multicollinearity between estimators does not affect the OLS assumptions.
Nevertheless, it can complicate a regression (Singh & Kumar, 2021). Further, multicollinearity
enhances or increases the variance of the regression coefficients and thus makes them unstable.
The more variance they have, the much difficult...
