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New functionality: correlated discretized RVs #120
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The math for how to do this for the two-dimensional equiprobable approximation to the normal/lognormal, is at Chris Carroll's public lecture notes site in the MathFacts directory; click on "Equiprobable-Returns". The problem is solved and graphs produced by Mathematica code that is part of an archive here. So a straightforward way to do 2) and 3) would involve translating the Mathematica code to Econ-ARK/HARK flavored python. |
compumetrika's (NMP) comment on #119 is probably more relevant for this issue; I think he was responding to the last line of my original post in that issue. |
I see this marked as a 1.0.0 milestone, but I have less than a week left and Chris told me to prioritize the calibration stuff. |
ok, i just moved it because I see that #623 was marked for 1.0. I guess all this can be punted to 1.x |
Yes, it can be punted. Mateo will indeed need to do it eventually but the calibration stuff nailed down is definitely higher priority. |
After #640 this will depend on a representation of a correlated multivariate (e.g. lognormal) distribution. Currently, the LogNormal distribution takes parameters It would make sense for a multivariate lognormal distribution to take an ndarray for the covariance matrix. You might want to consider at that point making the multivariate lognormal a generalization of the univariate lognormal class, i.e. one that takes a wider range of inputs. The multivariate independent case that is currently supported looks like yet another artifact of now regretted coding. In my view, it would be ideal if all the inputs and outputs to the Distribution classes were |
It came up before I thought it would. See a first naive attempt at #948. |
I think Dolo has moved in this direction. Even a univariate distribution is characterized by a (degenerate) variance/covariance matrix. The issue still arises of "which interpolation method should we use. There are a lot of candidates, and I don't have the to differentiate between them. |
As is, our only method for automatically generating discretized joint distributions is
combineIndepDstns
. HARK.utilities should also contain methods for making discrete approximations to correlated multivariate distributions. Three requests:To start really simple: continuous uniform distribution with linear relationships.
Discretized multivariate normal distribution. Take in vector array of means and matrix array of varcov, return joint normal approximation.
Same as (2), but lognormal with underlying lognormal distribution as specified.
NB. We plan to move toward a system like that of dolo for discretizing distributions. The best way to do this task might be to figure out how to construct a front end to dolo's existing tools for doing this.
This is related to #119 and #121
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