Understanding the causes and implication of heteroskedasticity
Nov 30, 2020 · 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 …
r - Best way to deal with heteroscedasticity? - Cross Validated
Apr 19, 2015 · Using the method posted on Stack Overflow here: Regression with Heteroskedasticity Corrected Standard Errors Which would be the best method to use to deal …
least squares - How does heteroskedasticity affect the validity of R ...
Jun 9, 2019 · In standard OLS, homoskedasticity is not a requirement of unbiasedness. Hence, under heteroskedasticity, the coefficient estimates will still be unbiased. The standard errors …
Difference between heteroscedasticity and ARCH effects?
Jun 15, 2020 · What is the difference between heteroscedasticity and ARCH effects? For example in R you can do a Breusch-Pagan Test to test for heteroscedasticity, and a Lagrange Multiplier …
Clarification on Using Robust vs. Clustered Standard Errors
Feb 11, 2025 · Then there are standard errors that are robust to heteroskedasticity AND serial correlation: HAC standard errors or Newey-West standard errors, for example. These would …
terminology - Why are there two spellings of "heteroskedastic" or ...
May 22, 2015 · I frequently see both the spellings "heteroskedastic" and "heteroscedastic", and similarly for "homoscedastic" and "homoskedastic". There seems to be no difference in …
How to deal with Heteroskedasticity in a GAM model
Feb 25, 2024 · This seems to me to be a clear case of heteroskedasticity, meaning that I cannot use a Gaussian distribution for this model. However, I do not understand how to handle this …
Heteroskedasticity - residual plot interpretation - Cross Validated
Aug 8, 2015 · I am plotting a residual plot to test for heteroskedasticity. The Breusch-Pagan test is significant and therefore I am suspecting there is evidence on heteroskedasticity. The question …
Heteroscedasticity in linear mixed effects models (lmer)
Mar 19, 2024 · I am computing the following model in R, using lme4::lmer: m3 = lmer(e ~ (X*Y*Z) + (1|ID/R), data = data_transform) e is a continuous variable. X, Y, and Z are categorical …
heteroscedasticity - What are the consequences of having non …
Oct 17, 2016 · One of the assumptions of linear regression is that there should be a constant variance in the error terms and that the confidence intervals and hypothesis tests ...