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ECONOMICS 2021
QUESTION A1 (15 marks)
(a) Briefly explain two shortcomings or weaknesses of econometrics. (4)
Econometrics depends on economic theory to provide the variables involved, the
direction of causality and the nature of the functional form.
Econometrics cannot resolve theoretical differences between different schools of
thought. Causality depends only on theory.
Econometrics can only determine correlation, which is the strength and nature of a
relationship. It cannot say anything about causality.
(b) Explain the meaning of an estimator (�̂) in a regression equation. How does it differ
from �? (3)
is the true (population) parameter and � is the estimated (sample) para
relationship between the Xs and Y where the εs take care of deviations in Y not explained by
the Xs. Note that the βs are unknown.
(c) Explain the meaning of R2 and adjusted R2. Refer to their respective shortcomings
and advantages. (Hint: Include the formula of the adjusted R2.) (4)
�2 measures the quality of fit of a regression equation, for the cases of one and more than
one independent variables. It can be represented with the following equation:
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, t
Adjusted �2 denotes by �2 is a modified version of R-squared that has been adjusted for
the number of predictors in the model. It is given by the equation below:
(d) Explain the difference between the error term (��) and the residual term (��). (4)
The error term (��) is the difference between the true line and the observed value of Y. The
residual term (��) is the difference between the observed value and estimated value from the
regression equation.
QUESTION A2 (15 marks)
a) Besides the variation in the dependent variable (Y) that is caused by the independent
variable (X), there is almost always variation that comes from other sources as well.
This additional variation comes in part from omitted explanatory variables (e.g., X2
and X3). However, even if these extra variables are added to the equation, there still is
going to be some variation in Y that simply cannot be explained by the model. This
variation probably comes from sources such as omitted influences, measurement error,
incorrect functional form, or purely random and totally unpredictable occurrences. By
random we mean something that has its value determined entirely by chance.
This assumption of normality is not required for OLS estimation.
Question A2B
The error term can be thought of as the composite of a number of minor influences
or errors. As the number of these minor influences gets larger, the distribution of the
error term tends to approach the normal distribution. This tendency is called the
Central Limit Theorem. The t-test and F-test are not applicable unless the error term
is normal distributed.This assumption of normality is not required for OLS estimation.
Its major application is in hypothesis testing, which uses the estimated regression
coefficient to investigate hypotheses about economic behavior. One example of such
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ECONOMICS 2021
QUESTION A1 (15 marks)
(a) Briefly explain two shortcomings or weaknesses of econometrics. (4)
Econometrics depends on economic theory to provide the variables involved, the
direction of causality and the nature of the functional form.
Econometrics cannot resolve theoretical differences between different schools of
thought. Causality depends only on theory.
Econometrics can only determine correlation, which is the strength and nature of a
relationship. It cannot say anything about causality.
(b) Explain the meaning of an estimator (�̂) in a regression equation. How does it differ
from �? (3)
is the true (population) parameter and � is the estimated (sample) para
relationship between the Xs and Y where the εs take care of deviations in Y not explained by
the Xs. Note that the βs are unknown.
(c) Explain the meaning of R2 and adjusted R2. Refer to their respective shortcomings
and advantages. (Hint: Include the formula of the adjusted R2.) (4)
�2 measures the quality of fit of a regression equation, for the cases of one and more than
one independent variables. It can be represented with the following equation:
t
, t
Adjusted �2 denotes by �2 is a modified version of R-squared that has been adjusted for
the number of predictors in the model. It is given by the equation below:
(d) Explain the difference between the error term (��) and the residual term (��). (4)
The error term (��) is the difference between the true line and the observed value of Y. The
residual term (��) is the difference between the observed value and estimated value from the
regression equation.
QUESTION A2 (15 marks)
a) Besides the variation in the dependent variable (Y) that is caused by the independent
variable (X), there is almost always variation that comes from other sources as well.
This additional variation comes in part from omitted explanatory variables (e.g., X2
and X3). However, even if these extra variables are added to the equation, there still is
going to be some variation in Y that simply cannot be explained by the model. This
variation probably comes from sources such as omitted influences, measurement error,
incorrect functional form, or purely random and totally unpredictable occurrences. By
random we mean something that has its value determined entirely by chance.
This assumption of normality is not required for OLS estimation.
Question A2B
The error term can be thought of as the composite of a number of minor influences
or errors. As the number of these minor influences gets larger, the distribution of the
error term tends to approach the normal distribution. This tendency is called the
Central Limit Theorem. The t-test and F-test are not applicable unless the error term
is normal distributed.This assumption of normality is not required for OLS estimation.
Its major application is in hypothesis testing, which uses the estimated regression
coefficient to investigate hypotheses about economic behavior. One example of such
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