Quantitative Methods: Multicollinearity, Dummy Variables, Autocorrelation, Fixed vs Random Effects Models

Quantitative Methods

TABLE OF CONTENTS
QUESTION 1..................................................................................................................................2
Presenting whether the model suffer from multicolienraity problem..........................................2
QUESTION 2..................................................................................................................................3
QUESTION 3..................................................................................................................................3
a) Dummy variable and purpose of it..........................................................................................3
b) Test..........................................................................................................................................3
c) Interpretation...........................................................................................................................4
QUESTION 4..................................................................................................................................4
a) meaning and cause of autocorrelation.....................................................................................4
b) Drawbacks of Durbin Watson test...........................................................................................5
QUESTION 5..................................................................................................................................6
Major difference between fixed effects and random effects models...........................................6
REFERENCES................................................................................................................................8

QUESTION 1
Presenting whether the model suffer from multicolienraity problem
While deriving results by using regression analysis, it has been identified that there are
chances of different errors and this can be termed as a multicollearity. Yes, the above model and
generated outcome faces multicollinearity problem because the independent variable is highly
correlated with more than other independent variables as per the given regression equation. As a
result, it makes hard to interpret the model and sometimes lead to overfitting a problem (Daoud,
2017). In the context of given results, it can be analysed that demand for passenger cars is
depend upon five different variables which include new car consumer price index, consumer
price index, personal disposable income, interest rate, employed civilian labour force. Therefore,
one predictor variable which is mainly used to predict other that create redundant information
that somehow affect the results in negative manner. On the other side, this can be detected
through a model which is known as variance inflation factor that help to determine predictable
variable.
In addition to this, it can be stated that multicollinearity does not affect the model’s
predictive accuracy but affect results. That is why, there is a need to remove this problem, as it
leads to difficulty in testing individual regression coefficient due to fluctuated standard error. As
a result, statistician does not able to declare X has a significant and has strong relationship with
Y. On the other hand, it has been identified that there is a high correlation between all the
predictor variables and this in turn create redundant information that skew the results in opposite
manner (Weaving and et.al., 2019). That is why, as per the results generated, it has been
identified that the value of R square is 0.85 which means that there is 85% chances where the
dependent variable affected from another one. Thus, changes in the overall result affect the
demand of car which cause adverse impact over results. Thus, to fix the issue, there is a need to
remove some of highly correlated independent variables that helps to provide effective results.
Also, perform the analysis for highly correlated variable which include a principal components
analysis. This in turn causes positive impact upon results and remove the problem of multi-
collinearity.
Another solution that can be used to solve the problem includes linearly combine all the
independent variable so that they all provide effective results. In the context of defined question,