Closed sbenthall closed 4 months ago
For future reference of anyone who takes on this task, let me point you to some lecture notes and a corresponding Mathematica notebook that shows how to construct equiprobable distributions that allow for correlations between two independent variables. It shouldn't be that hard to translate the Mathematica code into python.
http://www.econ2.jhu.edu/people/ccarroll/public/lecturenotes/AssetPricing/Portfolio-Multi-CRRA/
@Mv77 since you are working with the Portfolio model closely, maybe you are the best person to take a look at this. Is there any way the Distribution classes could be expanded to make this sort of operation easier?
For reference, the combineIndepDstn
function:
https://github.com/econ-ark/HARK/blob/master/HARK/distribution.py#L906
For this issue, there needs to be an implementation of a more sophisticated way to discretize correlated normal distributions....
See #120
The easiest way to do this with current tools would be to add a "crash" state where income shocks and risky return draws would be very low. The crash state can happen with some probability p
, and otherwise shocks are drawn from their usual distributions.
I might take a look after I am done with the newborn shocks issue.
The current ConsPortfolioModel handles arbitrary shock distributions using DiscreteDistribution, including correlation between income and returns. Closing.
ConsPortfolioModel can be ran with a non-independent return distribution.
The code for this case has not yet been refactored to take into account DiscreteDistribution.
https://github.com/econ-ark/HARK/blob/master/HARK/ConsumptionSaving/ConsPortfolioModel.py#L707-L712
This case is probably missing from the automated test suite. This issue is for making the test and correcting this code.