ECS4863
ASSIGNME
NT 1 MEMO
| DUE 16
MAY 2025
,NO PLAGIARISM
[DATE]
[COMPANY NAME]
[Company address]
,Exam (elaborations)
ECS4863 Assignment 1 Memo | Due 16 May
2025
Course
Advanced Econometrics (ECS4863)
Institution
University Of South Africa (Unisa)
Book
Introductory Econometrics
ECS4863 Assignment 1 Memo | Due 16 May 2025. All questions fully
answered.
Question 1: (15 marks) 1.1 Explain the concept of omitted variable bias and
distinguish between positive and negative bias ( 4)
Question 1.1: Omitted Variable Bias (15 marks)
Definition of Omitted Variable Bias:
Omitted Variable Bias (OVB) occurs in statistical analysis, particularly in regression models,
when a relevant explanatory variable is left out of the model. This missing variable must be
correlated with both the dependent variable and one or more of the included independent
variables. When this happens, the estimated coefficients of the included variables may be biased
and inconsistent, leading to incorrect conclusions.
In simpler terms, OVB arises when a factor that influences the outcome is not included in the
analysis, and its absence causes the effects of the included variables to be either overstated or
understated.
Positive vs Negative Bias:
Positive Bias:
This occurs when the omission of a variable causes the estimated coefficient of an
included variable to be higher than its true value. This happens when:
o The omitted variable is positively correlated with both the dependent variable and
the included independent variable.
o Or, the omitted variable is negatively correlated with both the dependent variable
and the included independent variable.
, Example: If you are estimating the effect of education on income but omit "ability"
(which is positively correlated with both education and income), the coefficient on
education will be biased upward (positively biased).
Negative Bias:
This occurs when the omission causes the estimated coefficient to be lower than its true
value. This happens when:
o The omitted variable is positively correlated with one variable but negatively
correlated with the other (dependent or independent variable).
Example: If you're estimating the effect of exercise on health outcomes but omit "stress
level" (which negatively affects health but may be positively related to exercise), the
estimated effect of exercise on health will be biased downward (negatively biased).
Conclusion:
Omitted Variable Bias undermines the reliability of regression results by distorting the estimated
relationships between variables. Whether the bias is positive or negative depends on the direction
of the correlations between the omitted variable and both the dependent and independent
variables.
Question 1.1 (15 marks)
Explain the concept of omitted variable bias and distinguish between positive and negative
bias.
Omitted Variable Bias (OVB):
Omitted variable bias occurs in statistical analysis, particularly in regression models, when a
relevant explanatory variable is left out of the model. This omitted variable must both influence
the dependent variable and be correlated with one or more of the included independent variables.
When this happens, the estimated coefficients of the included variables become biased and
ASSIGNME
NT 1 MEMO
| DUE 16
MAY 2025
,NO PLAGIARISM
[DATE]
[COMPANY NAME]
[Company address]
,Exam (elaborations)
ECS4863 Assignment 1 Memo | Due 16 May
2025
Course
Advanced Econometrics (ECS4863)
Institution
University Of South Africa (Unisa)
Book
Introductory Econometrics
ECS4863 Assignment 1 Memo | Due 16 May 2025. All questions fully
answered.
Question 1: (15 marks) 1.1 Explain the concept of omitted variable bias and
distinguish between positive and negative bias ( 4)
Question 1.1: Omitted Variable Bias (15 marks)
Definition of Omitted Variable Bias:
Omitted Variable Bias (OVB) occurs in statistical analysis, particularly in regression models,
when a relevant explanatory variable is left out of the model. This missing variable must be
correlated with both the dependent variable and one or more of the included independent
variables. When this happens, the estimated coefficients of the included variables may be biased
and inconsistent, leading to incorrect conclusions.
In simpler terms, OVB arises when a factor that influences the outcome is not included in the
analysis, and its absence causes the effects of the included variables to be either overstated or
understated.
Positive vs Negative Bias:
Positive Bias:
This occurs when the omission of a variable causes the estimated coefficient of an
included variable to be higher than its true value. This happens when:
o The omitted variable is positively correlated with both the dependent variable and
the included independent variable.
o Or, the omitted variable is negatively correlated with both the dependent variable
and the included independent variable.
, Example: If you are estimating the effect of education on income but omit "ability"
(which is positively correlated with both education and income), the coefficient on
education will be biased upward (positively biased).
Negative Bias:
This occurs when the omission causes the estimated coefficient to be lower than its true
value. This happens when:
o The omitted variable is positively correlated with one variable but negatively
correlated with the other (dependent or independent variable).
Example: If you're estimating the effect of exercise on health outcomes but omit "stress
level" (which negatively affects health but may be positively related to exercise), the
estimated effect of exercise on health will be biased downward (negatively biased).
Conclusion:
Omitted Variable Bias undermines the reliability of regression results by distorting the estimated
relationships between variables. Whether the bias is positive or negative depends on the direction
of the correlations between the omitted variable and both the dependent and independent
variables.
Question 1.1 (15 marks)
Explain the concept of omitted variable bias and distinguish between positive and negative
bias.
Omitted Variable Bias (OVB):
Omitted variable bias occurs in statistical analysis, particularly in regression models, when a
relevant explanatory variable is left out of the model. This omitted variable must both influence
the dependent variable and be correlated with one or more of the included independent variables.
When this happens, the estimated coefficients of the included variables become biased and
