l It all depends on your research model, i.e. if you have time series autocorrelation and multicollinearity occurs due to same reason, having time series day as independent variables. So, if you have time series apply autocorrelation first, multicollinearity will be solved as well.
l In general if not time series multicollinearity is the result of the research model, review your variables, maybe some of them are not necessary or maybe you can build indices or scales to aggregate some of the independent variables so that you get rid off the multicollinearity. However, in social sciences it almost impossible to avoid it, anyhow some of the independent variable will have correlation, 0.4 -0.5 is acceptable in social sciences.
l Heteroscedasticity is the case where residuals are not independent and correlated with dependent variable; this is one of the assumptions to use OLS. You can transform the dependent variable, apply non-linear regression or use MLE instead of OLS, or you can have mixed models where you assign probability to error distributions. This problem is more technical rather than research model driven,
I would be more concerned with the first two issues. Again, it depends on your domain (what is acceptable) and your research model. Your theory should lead the research model.
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