Dear statalisters,
I am using linear regression to investigate factors influencing my right skewed dependent variable. Because of issues of heteroskedasticity in residuals after performing regression, I log-transformed the dv which works much better (I checked graphically with rvfplot and additionally used estat hettest and estat imtest).
Still I am concerned with issues of heteroskedasticity in the relationship of the residuals with the right-hand-side variables of the model. This is because heteroskedasticity associated with the regressors can make correct inference from a log linear model quite problematic. (see Blackburn 2007 or Manning/Mullahy 2001).
The only way I know of for performing such a test is to use estat hettest, rhs mtest || estat hettest, rhs mtest(b)
It produces quite different results depending on correction for multiple testing or not. If I use bonferroni correction I only see heteroskedasticity in one variable (out of 15), which makes me hope that I will get consistent estimates of marginal effects. But if I dont adjust p-values six variables are significant. I am not sure how to interpret this big difference and hope you have any suggestions.
Since I have a lot of binary variables I assume that it makes no sense to investigate this graphically (something comparable to rvfplot)?!
I would prefer not to transform the dep var and use poison or glm with log link as suggested in the papers cited above (or suggested on previous posts on Stata List). To my knowledge because of the asymptotic properties of these models they are not feasible for my rather small N of 150 cases. Please correct me if I am wrong with this!
I highly appreciate any comments!
lit: Blackburn, McKinley L. 2007: Estimating wage differentials without logarithms. In: Labour Economics 14;1: 73–98.
Manning, Willard G.; Mullahy, John 2001: Estimating log models: to transform or not to transform? In: Journal of Health Economics 20;4: 461–494.
I am using linear regression to investigate factors influencing my right skewed dependent variable. Because of issues of heteroskedasticity in residuals after performing regression, I log-transformed the dv which works much better (I checked graphically with rvfplot and additionally used estat hettest and estat imtest).
Still I am concerned with issues of heteroskedasticity in the relationship of the residuals with the right-hand-side variables of the model. This is because heteroskedasticity associated with the regressors can make correct inference from a log linear model quite problematic. (see Blackburn 2007 or Manning/Mullahy 2001).
The only way I know of for performing such a test is to use estat hettest, rhs mtest || estat hettest, rhs mtest(b)
It produces quite different results depending on correction for multiple testing or not. If I use bonferroni correction I only see heteroskedasticity in one variable (out of 15), which makes me hope that I will get consistent estimates of marginal effects. But if I dont adjust p-values six variables are significant. I am not sure how to interpret this big difference and hope you have any suggestions.
Since I have a lot of binary variables I assume that it makes no sense to investigate this graphically (something comparable to rvfplot)?!
I would prefer not to transform the dep var and use poison or glm with log link as suggested in the papers cited above (or suggested on previous posts on Stata List). To my knowledge because of the asymptotic properties of these models they are not feasible for my rather small N of 150 cases. Please correct me if I am wrong with this!
I highly appreciate any comments!
lit: Blackburn, McKinley L. 2007: Estimating wage differentials without logarithms. In: Labour Economics 14;1: 73–98.
Manning, Willard G.; Mullahy, John 2001: Estimating log models: to transform or not to transform? In: Journal of Health Economics 20;4: 461–494.
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