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edited Nov 30, 2020 at 17:53

Here is a source out of the econometric literature to summarize some possible reasons for heteroskedasticity which include:

As people learn the error of their behavior, as in typing mistakes, there can be an improvement ( reductionreduction in errors) over time.
As income grows people have more choices on the disposition of their income.
As data collection techniques (as in automated data processing) improvedimprove, associatedassociated data collection errors are reduced.
Outliers can be a factor in heteroskedasticity.
A model specification error can contribute to heteroskedasticity as occurs in the emission of important variables.
Skewness in the distribution of a regressor variable could be a possible source for heteroskedasticity as occurs, for example, in the distribution of wealth and income, where there is a significant concentration in a few.
In modeling, possible errors related to heteroskedasticity could occur relating to inappropriate data transformations.
Or inappropriate functional form as in linear versus a log-linear model.
And finally, heteroskedasticity is more common in cross-sectional data than conventional time series analysis.

I hope this helps.

Here is a source out of the econometric literature to summarize some possible reasons for heteroskedasticity which include:

As people learn the error of their behavior, as in typing mistakes, there can be an improvement ( reduction in errors) over time.
As income grows people have more choices on the disposition of their income.
As data collection techniques (as in automated data processing) improved, associated data collection errors are reduced.
Outliers can be a factor in heteroskedasticity.
A model specification error can contribute to heteroskedasticity as occurs in the emission of important variables.
Skewness in the distribution of a regressor variable could be a possible source for heteroskedasticity as occurs, for example, in the distribution of wealth and income, where there is a significant concentration in a few.
In modeling, possible errors related to heteroskedasticity could occur relating to inappropriate data transformations.
Or inappropriate functional form as in linear versus a log-linear model.
And finally, heteroskedasticity is more common in cross-sectional data than conventional time series analysis.

I hope this helps.

Here is a source out of the econometric literature to summarize some possible reasons for heteroskedasticity which include:

As people learn the error of their behavior, as in typing mistakes, there can be an improvement (reduction in errors) over time.
As income grows people have more choices on the disposition of their income.
As data collection techniques (as in automated data processing) improve, associated data collection errors are reduced.
Outliers can be a factor in heteroskedasticity.
A model specification error can contribute to heteroskedasticity as occurs in the emission of important variables.
Skewness in the distribution of a regressor variable could be a possible source for heteroskedasticity as occurs, for example, in the distribution of wealth and income, where there is a significant concentration in a few.
In modeling, possible errors related to heteroskedasticity could occur relating to inappropriate data transformations.
Or inappropriate functional form as in linear versus a log-linear model.
And finally, heteroskedasticity is more common in cross-sectional data than conventional time series analysis.

I hope this helps.

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created Nov 30, 2020 at 17:21

Here is a source out of the econometric literature to summarize some possible reasons for heteroskedasticity which include:

As people learn the error of their behavior, as in typing mistakes, there can be an improvement ( reduction in errors) over time.
As income grows people have more choices on the disposition of their income.
As data collection techniques (as in automated data processing) improved, associated data collection errors are reduced.
Outliers can be a factor in heteroskedasticity.
A model specification error can contribute to heteroskedasticity as occurs in the emission of important variables.
Skewness in the distribution of a regressor variable could be a possible source for heteroskedasticity as occurs, for example, in the distribution of wealth and income, where there is a significant concentration in a few.
In modeling, possible errors related to heteroskedasticity could occur relating to inappropriate data transformations.
Or inappropriate functional form as in linear versus a log-linear model.
And finally, heteroskedasticity is more common in cross-sectional data than conventional time series analysis.

I hope this helps.