Heteroskedasticity and Distribution of the Dependent Variable in Linear Models I am running a multivariate ols model where my dependent variable is Food Consumption Score, an index created by the weighted sum of the consumption occurrences of some given food categories.
Although I have tried different specifications of the model, scaled and/or log-transformed the predictors, the Breusch-Pagan test always detects strong heteroskedasticity.


*

*I exclude the usual cause of omitted variables;

*No presence of outliers, especially after log-scaling and normalizing;

*I use 3/4 indexes created by applying Polychoric PCA, however even excluding some or all of them from the OLS does not change the Breusch-Pagan output.

*Only few (usual) dummy variables are used in the model: sex, marital status;

*I detect high degree of variation occurring between regions of my sample, despite controlling with the inclusion of dummies for each region and gaining more 20% in terms of adj-R^2, the heteroskedasticity reamins.

*The sample has 20,000 observations.


I think that the problem is in the distribution of my dependent variable.
As far as I have been able to check, the normal distribution is the best approximation of the actual distribution of my data (maybe not close enough)
I attach here two qq-plot respectively with the dependent variable normalized and logarithmic transformed (in red the Normal theoretical quantiles).


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*Given the distribution of my variable, may the heteroskedasticity be caused by the non-normality in the dependent variable (which causes non-normality in the model's errors?)

*Should I transform the dependent variable? Should I apply a glm model? -I have tried with glm but nothing has changed in terms of BP-test output.

*Do I have more efficient ways to control for the between group variation and get rid of heteroskedasticity (random intercept mixed model)?


Thank you in advance.
EDIT 1:
I have checked in the technical manual of the Food Consumption Score, and it is reported that usually the indicator follows a "close to normal" distribution.
Indeed the Shapiro-Wilk Test rejects the null hypothesis that my variable is normally distributed ( I have been able to run the test on the first 5000 obs).
What I can see from the plot of the fitted against the residuals is that for lower values of fitted the variability in the errors decreases. I attach the plot here below. 
The plot comes from a Linear Mixed Model, to be precise a Random Intercept Model taking into account 398 different groups (Inter correlation coefficient = 0.32, groups' mean reliances not less than 0.80).
Althought I have taken into account for between-group variability, heteroskedasticity is still there.
I have also runned diverse quantile regressions. I was particularly interested in the regression on the 0.25 quantile, however no improvements in terms of equal variance of the errors.
I am now thinking to account for the diversity between quantiles and groups (geographical regions) at the same time by fitting a Random Intercept Quantile Regression. 
May be a good idea?
Further, the Poisson distribution looks like following the trend of my data, even if for low values of the variable it wanders a bit (a bit less than the normal). However, the problem is that fitting glm of the Poisson family requires postive integers, my variable is positive but does not have exclusively integers. I thus discarded the glm (or glmm) option. 

EDIT 2:
Most of your suggestions go in the direction of robust estimators. However, I think is just one of the solutions. 
Understanding the reason of heteroskedasticity in my data would improve the understanding of the relation I want to model. Here is clear that something is going on on the bottom of the error distribution - look at this qqplot of residuals from an OLS specification.
Any idea comes into your mind on how to further deal with this problem?
Should I investigate more with quantile regressions?

PROBLEM SOLVED ?
Following your suggestions I have finally runned a random intercept model tring to relate the technical problem to the theory of my field of study.
I have found a variable that if included in the random part of the model makes the error terms going to homoskedasticity.
Here I post 3 plots: 


*

*The first is computed from a Random Intercept Model with 34 groups (provinces) 

*The second comes from a Random Coefficient Model with 34 groups (provinces)

*Finally the third is the result of the estimation of a Random Coefficient Model with 398 groups (districts).


May I safely say that I am controlling for heteroskedasticity in the last specification?



 A: This is the solution for the problem above:
In brief, for my case, the heteroskedasticity is caused by at least two different sources:


*

*Group's differences which OLS and all the family of "mono-level" regression model hardly can account for;

*Wrong specification of the model functional form: more in detail (as suggested by @Robert Long in first place) the relation between the DV and the covariates is not linear.


For what concerns the group differences causing heteroskedasticity it has been of great help running analysis on truncated data for single groups, and acknowledge from the BP-test that heteroskedasticity was gone almost in all groups when considered singularly.
By fitting a random intercept model the error structure has improved, but as noted by the commentors above heteroskedasticity could still be detected. Even after including a variable in the random part of the equation which has been able to improve the error structure even more, the problem could not be considered solved. (This key variable, coping strategies, well describes habits of household in case of food shortages, indeed these habits usually vary much across geographical regions and ethnic groups.) 
Here comes the second point, the most important. The relation between DV (as it is originally) and covariates is not linear.
More options are available at this stage:


*

*Use a non linear model to explicitly take into account for the issue;

*Transform the DV, if you can find a suitable transformation. In my case square root of DV.

*Try using models that do not make any assumption on the distribution of the error term (glm family).


In my view, the first option complicates a bit the interpretation of the coefficients (is a personal project-dependent observation just because I want to keep things simple for this article) and at least from my (recent) experiences, needs more computational power which for complicated models with many random coefficients and observations could bring R to crash.
Transforming the DV is surely the best solution, if it works and if you are more lucky than me. What do I mean? In case of log transformed DV the interpretation would be in terms of percentage, but what about the square root transformation? How can I compare my results with other studies? Maybe a standardization of the transformed variable could help in interpreting the results in z-scores. In my opinion is just too much.
About the glm or glmm models I cannot say much, in my case none of those worked, glm does not properly account for random differences between groups and the output of glmm reported convergence problems.
Note that for my model the transformation of the DV does not work with OLS either for the same reason regarding glm above.
However, there is at least one option left: assigning weights to the regression in order to correct for the heteroskedasticity without transforming the DV. Ergo: simple interpretation of the coeff.s.
This is the result obtained by weigthing with DV_sqrt while using the un-transformed DV in a random coefficient model. 
At this stage I can compare my cofficients' standard errors with their counterpart from the robust estimator.

Regarding the direct use of robust estimators in case as mine without trying understanding the source of the problem, I would like to suggest this reading: G. King, M. E. Roberts (2014), "How Robust Standard Errors Expose Methodological Problems They Do Not Fix, and What to Do About It". 
