Heteroscedasticity is the condition which refers to the violation of the Homoscedasticity condition of the linear regression model used in econometrics study. In simple words, it can be described as the situation which leads to increase in the variance of the residual terms with the increase in the fitted value of the variable. Copy the link given below and paste it in new browser window to get more information on Heteroscedasticity:- http://www.transtutors.com/homework-help/economics/heteroscedasticity.aspx

1. Heteroscedasticity

2. One of the important assumption of the regression
model is that all the disturbances have same variance,
2 . This condition is known as homoscedastic,
which implies that the errors scatter in similar fashion
regardless of the value of X. But there are instances
where the error scatter is different, depending on the
value of the independent variables. When this condition
prevails ,the error terms are said to
be heteroscedastic.
Introduction

3. Yi = 1 + 2Xi + Ui
Regression Model
Var(Ui) = 2Homoscedasticity:
Heteroscedasticity:
Var(Ui) = i
2
Or E(Ui
2) = 2
Or E(Ui
2) = i
2
i =1,2,… N

4. Reasons for violation of Homoscedasticity
condition are many, such as:
Data with outliners
Functional form is incorrect
Transformation is incorrect of the data
observations being mixed with different measures of
scale.
If any one of these present in the data, there is
Heteroscedasticity.
HETEROSCEDASTICITY

5. Heteroscedasticity, thus can be defined as
a condition where errors have different
variance.
When the variance of the error term differ
across the observation, it makes the reliability
of observation unequal, that gives a wrong or
inaccurate inference about the data.
HETEROSCEDASTICITY

6. Xi
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Income
Consumption
HOMOSCEDASTIC PATTERN OF ERRORS

7. Xi
Yi
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Income
Consumption
HETEROSCEDASTIC PATTERN OF ERRORS

8. .
Xi
X1 X2
f(Yi)
X3
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Income
rich people
poor people
THE HETEROSCEDASTIC CASE

9. In case of Heteroscedasticity Ordinary least
squares estimators are
• linear and unbiased.
• not efficient.
Also, the data shows
• incorrect standard variation
• wrong confidence of interval and hypothesis test
CONSEQUENCES OF HETEROSCEDASTICITY

10. Hey Friends,
This was just a summary on Heteroscedasticity. For more
detailed information on this topic, please type the link
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http://www.transtutors.com/homework-
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