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st: Regression based Shapley Value Decomposition rbdineq


From   Ruchika <ruchi.jnu@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   st: Regression based Shapley Value Decomposition rbdineq
Date   Mon, 26 Mar 2012 16:42:55 +0530

Dear Statalisters

I am running a regression based decomposition by using command
rbdineq. i have installed the DASP V2.1 in stata 10. when i use this
command with 3, 4 independent variables then i get the results as
given below but i actually need more independent variables then i
donot get the results even after 3-4 hours. it just get one line
repeatedly saying 6094missing values generated as i have mentioned
below the these results. Can anyone have any idea why i am not getting
results even after such a long time. how many maximum independent
variables this command can process? Is there any other way of doing
this regression based shapley value based decomposition?


rbdineq sector sex_head hh_size, dep(real_mpce_mrp) model(semilog)
hsize(per_wht) dregres(0)

    Regression-based inequality decomposition by predicted income
components(using the Shapley value).
    Execution  time       :  86.86     second(s)
    Inequality index      :  Gini index
    Estimated inequality  :  0.350435
    Household size        :  per_wht
  +---------------------------------------------------------------------+
  |          Sources   |      Income         Absolute        Relative   |
  |                    |       Share       Contribution    Contribution |
  |--------------------+------------------------------------------------|
  |1: _p_cons        |               .        0.000000        0.000000|
  |2: _p_sector      |               .        0.064459        0.183941|
  |3: _p_sex_head |               .        0.000016        0.000046|
  |4: _p_hh_size    |               .        0.041701        0.118997|
  |5: _p_resi          |               .        0.244259        0.697016|
  |--------------------+------------------------------------------------|
  |              Total |               .        0.350435        1.000000|
  +---------------------------------------------------------------------+

Marginal contributions:
---------------------------------------------------------------------------
        Source |    level_1     level_2     level_3     level_4     level_5
---------------+-----------------------------------------------------------
1: _p_cons        |   0.000000    0.000000    0.000000    0.000000    0.000000
2: _p_sector      |   0.020474    0.015706    0.011915    0.009101    0.007264
3: _p_sex_head |   0.000009    0.000004    0.000002    0.000001    0.000001
4: _p_hh_size    |   0.014967    0.010676    0.007363    0.005027    0.003667
5: _p_resi          |   0.059700    0.053299    0.047875    0.043428    0.039958
---------------------------------------------------------------------------

rbdineq social_group nic3grp_hh eduheadcate ageheadcate religioncode
state_club sector sex_head hh_size hhtype, dep(real_mpce_mrp)
model(semilog) hsize(per_wht) dregres(0)

(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
(6094 missing values generated)
and so on...............

Thanks & Regards
Ruchika
PhD Research Scholar
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