Dear users I want to estimate the concentration index using equation 1 with wagstaff 2005 bounds. Then, with probit regression specification, I want to decompose the contribution of each predictor xrank and others. example data is given below.
\[ C = \frac{2}{n \cdot \bar{y}} \sum_{i=1}^{n} y_i R_i - 1 \] (eq-1)
Part -1 is done and result is given below.
My query is how to implement Part-2?
\[ CI_n = \left( \frac{\beta_{\text{rank}} X_{\text{rank}}}{\bar{Y}} \right) CI_r + \sum_k \left( \frac{\beta_k X_k}{\bar{Y}} \right) CI_k + \frac{GC \epsilon}{\bar{Y}} \] (eq-2)
I am using conindex (version *! conindex 1.5 18 July 2018) syntax and Stata version 15.1
\[ C = \frac{2}{n \cdot \bar{y}} \sum_{i=1}^{n} y_i R_i - 1 \] (eq-1)
Part -1 is done and result is given below.
My query is how to implement Part-2?
\[ CI_n = \left( \frac{\beta_{\text{rank}} X_{\text{rank}}}{\bar{Y}} \right) CI_r + \sum_k \left( \frac{\beta_k X_k}{\bar{Y}} \right) CI_k + \frac{GC \epsilon}{\bar{Y}} \] (eq-2)
I am using conindex (version *! conindex 1.5 18 July 2018) syntax and Stata version 15.1
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input byte y float xrank byte(xk1 xk2 xk3) float xk5 1 677.8285 0 0 0 146.51355 1 175.72597 1 1 0 578.2192 0 245.46584 1 0 0 586.5716 1 908.6988 1 0 1 673.6869 0 645.7861 1 1 1 753.4822 1 108.27734 1 0 0 978.2668 0 191.3244 1 1 1 564.6703 1 697.1516 1 1 1 390.6608 0 104.55543 1 1 1 815.6676 1 244.72725 0 0 1 343.749 0 593.8604 1 0 1 495.0743 0 722.7057 0 0 0 170.61075 1 686.7651 1 0 1 122.81567 0 301.84238 1 0 0 966.3835 1 740.9613 0 0 0 852.3821 0 313.5242 1 0 1 726.3768 1 392.8597 0 1 0 468.05765 1 771.8423 1 0 0 255.9649 1 684.6696 1 1 0 240.79333 0 864.3011 0 0 1 325.2186 1 691.8516 1 0 1 594.304 0 611.4777 0 0 0 743.1364 1 184.3073 1 1 1 694.1776 1 430.9442 0 1 1 351.9405 1 338.6821 0 1 1 959.3787 1 319.5907 1 1 1 764.1072 1 975.7095 1 0 0 598.91864 1 453.788 0 1 0 650.5486 1 902.8419 1 0 1 477.64 1 668.0248 1 0 1 322.9579 0 815.3302 1 1 0 420.3754 0 552.3734 0 1 0 782.0615 1 619.2135 0 1 1 112.95414 1 543.2659 0 1 0 204.4654 1 275.7187 0 1 1 141.40237 0 750.2069 0 1 1 136.65593 1 352.6951 0 1 0 869.9146 0 121.88437 0 1 0 733.2921 0 680.925 0 0 1 526.7565 1 259.3996 0 1 1 188.05075 0 946.4127 1 1 0 542.4543 0 958.5357 0 0 1 526.1246 1 923.3779 1 1 0 255.8817 1 433.1428 1 0 1 490.4665 1 113.91096 1 0 0 458.6543 1 935.4867 0 1 0 654.2651 1 485.3657 0 0 0 671.5843 0 969.9893 0 0 1 140.7736 1 967.258 0 0 1 437.1514 1 867.7085 1 0 0 663.2739 0 365.004 0 1 1 552.82263 1 446.58795 0 0 0 870.8409 0 866.023 0 1 0 692.8243 1 385.2298 0 0 1 246.641 0 252.54347 0 0 1 163.51187 1 601.12115 1 0 0 678.1774 1 942.5393 0 0 1 123.86018 0 726.4268 1 1 1 627.198 0 613.0551 0 1 0 946.2072 1 187.45885 1 0 0 617.9268 0 653.5065 0 0 1 449.3529 0 991.0485 0 1 0 678.9594 1 226.0756 1 0 0 512.4276 0 566.4967 1 0 1 591.0551 0 889.6357 1 0 0 947.3183 1 766.6918 0 1 0 447.4924 1 727.3141 1 1 1 965.0715 0 732.2357 0 1 1 914.8156 1 423.542 0 0 0 276.21204 1 364.2327 1 0 0 162.42517 1 828.425 1 1 1 190.7002 1 829.1021 0 1 1 116.39964 0 880.3651 0 1 1 184.99866 1 921.9165 1 1 1 714.7061 0 560.2081 1 0 0 164.0698 1 551.3647 1 1 1 387.0781 1 818.4656 0 0 1 860.3878 1 684.9675 0 1 0 120.94474 0 731.7702 0 0 1 833.0216 1 816.2134 0 1 0 353.6693 0 901.0048 0 1 1 206.34834 1 404.19565 0 1 1 727.0635 0 438.0247 1 1 1 666.0486 1 184.58374 0 0 0 889.7248 0 620.4521 0 1 0 761.564 0 132.34805 0 0 1 823.1328 1 519.0382 1 0 0 353.8311 0 588.3802 0 0 0 259.6956 1 357.8871 0 0 0 775.5533 1 631.7499 1 1 0 826.1512 1 127.45023 0 0 0 991.4547 1 133.61337 0 0 0 471.3559 1 840.3405 0 0 1 434.8163 1 424.1716 0 1 1 798.7717 1 214.35446 0 1 0 406.7232 1 570.0189 1 1 1 937.6816 1 792.9942 1 1 1 872.5715 1 294.23892 1 0 1 486.0946 1 660.6014 0 0 1 775.7839 0 176.8127 0 1 0 779.0886 end label var y "outcome dummy" label var xrank "rank continuous" label var xk1 "xk1 dummy" label var xk2 "xk2 dummy" label var xk3 "xk3 dummy" label var xk5 "xk5 continuous"
Code:
. conindex y , rankvar( xrank ) limits(0 1) bounded wagstaff ------------------------------------------------------------------------------+ Index: | No. of obs. | Index value | Std. error | p-value | -------------------+-------------+-------------+-------------------+----------| Wagstaff norm. CI | 100 | -.0999571 |.12036793 | 0.4083 | ------------------------------------------------------------------------------+
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