------------------------------------------------------------------------------------------------------ log: c:\Imbook\bwebpage\Section4\mma17p4duration.txt log type: text opened on: 19 May 2005, 15:25:00 . . ********** OVERVIEW OF MMA17P4DURATION.DO ********** . . * STATA Program . * copyright C 2005 by A. Colin Cameron and Pravin K. Trivedi . * used for "Microeconometrics: Methods and Applications" . * by A. Colin Cameron and Pravin K. Trivedi (2005) . * Cambridge University Press . . * Chapter 17.11 (pages 603-8) . * Duration regression with censored data example . * Provides . * (1) Data summary: Table 17.6 . * (2) List of Survivor Function and Cumulative Hazard Estimates: Table 17.7 . * (3) Various graphs describing the data . * (3A) K-M Survival Graph for all data (Figure 17.3: km_pt1.wmf) . * (3B) K-M Survival Graph by unemployment insurance (Figure 17.4: km_pt2.wmf) . * (3C) N-A Cumulative Hazard Graph for all data (Figure 17.5: na_pt1.wmf) . * (3D) N-A Cumulative Hazard Graph by unemployment insurance (Figure 17.6: na_pt2.wmf) . * (4) Coefficient Estimates of Some Parametric Models (Table 17.8) . * (4) Hazard Rate Estimates of Some Parametric Models (Table 17.9) . . * To run this program you need data file . * ema1996.dta . . ********** SETUP ********** . . set more off . version 8.0 . set scheme s1mono /* Used for graphs */ . set matsize 100 . . ********** DATA DESCRIPTION ********** . . * The data is from . * B.P. McCall (1996), "Unemployment Insurance Rules, Joblessness, . * and Part-time Work," Econometrica, 64, 647-682. . . * McCalls data set named ema_1996_pt_lastweek.dta . * has name changed to ema1996.dta . . * There are 3343 observations from the CPS Displaced Worker Surveys . * of 1986, 1988, 1990 and 1992 . * 1. spell is length of spell in number of two-week intervals . * 2. CENSOR1 = 1 if re-employed at full-time job . * 3. CENSOR2 = 1 if re-employed at part-time job . * 4. CENSOR3 = 1 if re-employed but left job: pt-ft status unknown . * 5. CENSOR4 = 1 if still jobless . * 6. ui (UI) = 1 if filed UI claim . * 7. reprate (RR) = eligible replacement rate . * 8. disrate (DR) = eligible disregard rate . * 9. tenure (TENURE) = years tenure in lost job . * 10. logwage (LOGWAGE) = log weekly earnings in lost job (1985$) . * 11.-43. other variables listed in McCall (1986) table 2 p.657 . . ********** READ DATA ********** . . use ema1996.dta (Sample for 1996 EMA paper: part-time= worked part-time last week) . sum Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- spell | 3343 6.247981 5.611271 1 28 censor1 | 3343 .3209692 .4669188 0 1 censor2 | 3343 .1014059 .3019106 0 1 censor3 | 3343 .1717021 .3771777 0 1 censor4 | 3343 .3754113 .4843014 0 1 -------------+-------------------------------------------------------- ui | 3343 .5527969 .4972791 0 1 reprate | 3343 .4544717 .1137918 .066 2.059 logwage | 3343 5.692994 .5356591 2.70805 7.600402 tenure | 3343 4.114867 5.862322 0 40 disrate | 3343 .1094376 .0735274 .002 1.02 -------------+-------------------------------------------------------- slack | 3343 .4884834 .4999421 0 1 abolpos | 3343 .1456775 .3528354 0 1 explose | 3343 .5025426 .5000683 0 1 stateur | 3343 6.5516 1.803825 2.5 13 houshead | 3343 .6120251 .4873617 0 1 -------------+-------------------------------------------------------- married | 3343 .5860006 .4926221 0 1 female | 3343 .3478911 .4763725 0 1 child | 3343 .4501944 .4975876 0 1 ychild | 3343 .1956327 .3967463 0 1 nonwhite | 3343 .1390966 .3460991 0 1 -------------+-------------------------------------------------------- age | 3343 35.44331 10.6402 20 61 schlt12 | 3343 .2811846 .4496446 0 1 schgt12 | 3343 .3356267 .4722797 0 1 smsa | 3343 .7241998 .4469835 0 1 bluecoll | 3343 .6036494 .489212 0 1 -------------+-------------------------------------------------------- mining | 3343 .029315 .1687132 0 1 constr | 3343 .1480706 .3552231 0 1 transp | 3343 .0646126 .2458778 0 1 trade | 3343 .1848639 .3882452 0 1 fire | 3343 .0514508 .2209484 0 1 -------------+-------------------------------------------------------- services | 3343 .1699073 .3756075 0 1 pubadmin | 3343 .0095722 .097383 0 1 year85 | 3343 .2677236 .442839 0 1 year87 | 3343 .2174693 .4125862 0 1 year89 | 3343 .1998205 .3999251 0 1 -------------+-------------------------------------------------------- midatl | 3343 .1088842 .3115405 0 1 encen | 3343 .1429853 .3501103 0 1 wncen | 3343 .0643135 .2453472 0 1 southatl | 3343 .2375112 .4256217 0 1 escen | 3343 .0532456 .2245564 0 1 -------------+-------------------------------------------------------- wscen | 3343 .1441819 .3513266 0 1 mountain | 3343 .1079868 .3104102 0 1 pacific | 3343 .0260245 .159232 0 1 . . * The following gives variables in same order as Table 2 p.657 of McCall (1996) . * which gives fuller names for the variables . sum spell censor1 censor2 censor3 censor4 age /* > */ ui reprate disrate logwage tenure slack abolpos explose bluecoll /* > */ houshead married child ychild female schlt12 schgt12 nonwhite smsa /* > */ midatl encen wncen southatl escen wscen mountain pacific /* > */ mining constr transp trade fire services pubadmin /* > */ year85 year87 year89 Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- spell | 3343 6.247981 5.611271 1 28 censor1 | 3343 .3209692 .4669188 0 1 censor2 | 3343 .1014059 .3019106 0 1 censor3 | 3343 .1717021 .3771777 0 1 censor4 | 3343 .3754113 .4843014 0 1 -------------+-------------------------------------------------------- age | 3343 35.44331 10.6402 20 61 ui | 3343 .5527969 .4972791 0 1 reprate | 3343 .4544717 .1137918 .066 2.059 disrate | 3343 .1094376 .0735274 .002 1.02 logwage | 3343 5.692994 .5356591 2.70805 7.600402 -------------+-------------------------------------------------------- tenure | 3343 4.114867 5.862322 0 40 slack | 3343 .4884834 .4999421 0 1 abolpos | 3343 .1456775 .3528354 0 1 explose | 3343 .5025426 .5000683 0 1 bluecoll | 3343 .6036494 .489212 0 1 -------------+-------------------------------------------------------- houshead | 3343 .6120251 .4873617 0 1 married | 3343 .5860006 .4926221 0 1 child | 3343 .4501944 .4975876 0 1 ychild | 3343 .1956327 .3967463 0 1 female | 3343 .3478911 .4763725 0 1 -------------+-------------------------------------------------------- schlt12 | 3343 .2811846 .4496446 0 1 schgt12 | 3343 .3356267 .4722797 0 1 nonwhite | 3343 .1390966 .3460991 0 1 smsa | 3343 .7241998 .4469835 0 1 midatl | 3343 .1088842 .3115405 0 1 -------------+-------------------------------------------------------- encen | 3343 .1429853 .3501103 0 1 wncen | 3343 .0643135 .2453472 0 1 southatl | 3343 .2375112 .4256217 0 1 escen | 3343 .0532456 .2245564 0 1 wscen | 3343 .1441819 .3513266 0 1 -------------+-------------------------------------------------------- mountain | 3343 .1079868 .3104102 0 1 pacific | 3343 .0260245 .159232 0 1 mining | 3343 .029315 .1687132 0 1 constr | 3343 .1480706 .3552231 0 1 transp | 3343 .0646126 .2458778 0 1 -------------+-------------------------------------------------------- trade | 3343 .1848639 .3882452 0 1 fire | 3343 .0514508 .2209484 0 1 services | 3343 .1699073 .3756075 0 1 pubadmin | 3343 .0095722 .097383 0 1 year85 | 3343 .2677236 .442839 0 1 -------------+-------------------------------------------------------- year87 | 3343 .2174693 .4125862 0 1 year89 | 3343 .1998205 .3999251 0 1 . . * The following creates a space-delimited data set with . * variables in same order as Table 2 p.657 of McCall (1996) . * Permits use by programs other than Stata . * Note that order has been changed a little from the original Stata data set . . outfile spell censor1 censor2 censor3 censor4 age /* > */ ui reprate disrate logwage tenure slack abolpos explose bluecoll /* > */ houshead married child ychild female schlt12 schgt12 nonwhite smsa /* > */ midatl encen wncen southatl escen wscen mountain pacific /* > */ mining constr transp trade fire services pubadmin /* > */ year85 year87 year89 using ema1996.asc, replace . . ********* ANALYSIS: UNEMPLOYMENT DURATION ********** . . * Stata st curves require defining the dependent variable . * and the censoring variable if there is one . stset spell, fail(censor1=1) failure event: censor1 == 1 obs. time interval: (0, spell] exit on or before: failure ------------------------------------------------------------------------------ 3343 total obs. 0 exclusions ------------------------------------------------------------------------------ 3343 obs. remaining, representing 1073 failures in single record/single failure data 20887 total analysis time at risk, at risk from t = 0 earliest observed entry t = 0 last observed exit t = 28 . stdes failure _d: censor1 == 1 analysis time _t: spell |-------------- per subject --------------| Category total mean min median max ------------------------------------------------------------------------------ no. of subjects 3343 no. of records 3343 1 1 1 1 (first) entry time 0 0 0 0 (final) exit time 6.247981 1 5 28 subjects with gap 0 time on gap if gap 0 time at risk 20887 6.247981 1 5 28 failures 1073 .3209692 0 0 1 ------------------------------------------------------------------------------ . . * (1) SUMMARIZE KEY VARIABLES (Table 17.6, p.603) . . sum spell censor1 censor2 censor3 censor4 ui reprate disrate tenure logwage Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- spell | 3343 6.247981 5.611271 1 28 censor1 | 3343 .3209692 .4669188 0 1 censor2 | 3343 .1014059 .3019106 0 1 censor3 | 3343 .1717021 .3771777 0 1 censor4 | 3343 .3754113 .4843014 0 1 -------------+-------------------------------------------------------- ui | 3343 .5527969 .4972791 0 1 reprate | 3343 .4544717 .1137918 .066 2.059 disrate | 3343 .1094376 .0735274 .002 1.02 tenure | 3343 4.114867 5.862322 0 40 logwage | 3343 5.692994 .5356591 2.70805 7.600402 . . * (2) LIST SURVIVAL CURVE AND CUMULATIVE HAZARD ESTIMATES (Table 17.7, p.605) . . * Kaplan-Meier Estimates of Survival Function . sts list failure _d: censor1 == 1 analysis time _t: spell Beg. Net Survivor Std. Time Total Fail Lost Function Error [95% Conf. Int.] ------------------------------------------------------------------------------- 1 3343 294 246 0.9121 0.0049 0.9019 0.9212 2 2803 178 304 0.8541 0.0062 0.8415 0.8659 3 2321 119 305 0.8103 0.0071 0.7960 0.8238 4 1897 56 165 0.7864 0.0076 0.7712 0.8008 5 1676 104 233 0.7376 0.0085 0.7206 0.7538 6 1339 32 111 0.7200 0.0088 0.7023 0.7369 7 1196 85 178 0.6688 0.0098 0.6492 0.6876 8 933 15 70 0.6581 0.0100 0.6380 0.6773 9 848 33 98 0.6325 0.0106 0.6113 0.6528 10 717 3 55 0.6298 0.0106 0.6086 0.6503 11 659 26 77 0.6050 0.0113 0.5825 0.6267 12 556 7 40 0.5974 0.0115 0.5744 0.6195 13 509 25 69 0.5680 0.0123 0.5434 0.5918 14 415 30 74 0.5270 0.0135 0.5001 0.5531 15 311 19 40 0.4948 0.0146 0.4658 0.5230 16 252 10 41 0.4751 0.0153 0.4449 0.5047 17 201 8 24 0.4562 0.0161 0.4245 0.4874 18 169 7 13 0.4373 0.0169 0.4040 0.4702 19 149 4 15 0.4256 0.0174 0.3912 0.4595 20 130 3 18 0.4158 0.0179 0.3804 0.4507 21 109 4 23 0.4005 0.0188 0.3635 0.4372 22 82 4 9 0.3810 0.0203 0.3412 0.4206 23 69 0 9 0.3810 0.0203 0.3412 0.4206 24 60 0 2 0.3810 0.0203 0.3412 0.4206 25 58 0 10 0.3810 0.0203 0.3412 0.4206 26 48 2 13 0.3651 0.0223 0.3214 0.4088 27 33 5 24 0.3098 0.0296 0.2528 0.3684 28 4 0 4 0.3098 0.0296 0.2528 0.3684 ------------------------------------------------------------------------------- . . * Nelson-Aalen Estimates of Cumulative Hazard . sts list, na failure _d: censor1 == 1 analysis time _t: spell Beg. Net Nelson-Aalen Std. Time Total Fail Lost Cum. Haz. Error [95% Conf. Int.] ------------------------------------------------------------------------------- 1 3343 294 246 0.0879 0.0051 0.0784 0.0986 2 2803 178 304 0.1514 0.0070 0.1383 0.1658 3 2321 119 305 0.2027 0.0084 0.1869 0.2199 4 1897 56 165 0.2322 0.0093 0.2147 0.2512 5 1676 104 233 0.2943 0.0111 0.2733 0.3169 6 1339 32 111 0.3182 0.0119 0.2957 0.3424 7 1196 85 178 0.3893 0.0142 0.3624 0.4181 8 933 15 70 0.4053 0.0148 0.3774 0.4353 9 848 33 98 0.4443 0.0162 0.4135 0.4773 10 717 3 55 0.4484 0.0164 0.4174 0.4818 11 659 26 77 0.4879 0.0182 0.4536 0.5248 12 556 7 40 0.5005 0.0188 0.4650 0.5387 13 509 25 69 0.5496 0.0212 0.5096 0.5927 14 415 30 74 0.6219 0.0250 0.5748 0.6728 15 311 19 40 0.6830 0.0286 0.6291 0.7415 16 252 10 41 0.7227 0.0313 0.6639 0.7866 17 201 8 24 0.7625 0.0343 0.6982 0.8327 18 169 7 13 0.8039 0.0377 0.7333 0.8812 19 149 4 15 0.8307 0.0400 0.7559 0.9130 20 130 3 18 0.8538 0.0422 0.7750 0.9406 21 109 4 23 0.8905 0.0460 0.8048 0.9853 22 82 4 9 0.9393 0.0521 0.8426 1.0470 23 69 0 9 0.9393 0.0521 0.8426 1.0470 24 60 0 2 0.9393 0.0521 0.8426 1.0470 25 58 0 10 0.9393 0.0521 0.8426 1.0470 26 48 2 13 0.9809 0.0598 0.8705 1.1055 27 33 5 24 1.1325 0.0904 0.9685 1.3242 28 4 0 4 1.1325 0.0904 0.9685 1.3242 ------------------------------------------------------------------------------- . . * (3) VARIOUS GRAPHS (Figures 17.3-17.6) . . * (3A) Figure 17.3: Overall Survival Function (page 604) . * sts graph, gwood . * Nicer graphs and also confidence bands are bolder and easier to read . sts gen surv = s . sts gen lbsurv = lb(s) . sts gen ubsurv = ub(s) . sort spell . graph twoway (line ubsurv spell, msize(vtiny) mstyle(p2) c(J) clstyle(p1) clcolor(gs10)) /* > */ (line surv spell, msize(vtiny) mstyle(p1) c(J) clstyle(p1)) /* > */ (line lbsurv spell, msize(vtiny) mstyle(p2) c(J) clstyle(p1) clcolor(gs10)), /* > */ scale(1.2) plotregion(style(none)) /* > */ title("Overall Survival Function Estimate") /* > */ xtitle("Unemployment Duration in 2-week intervals", size(medlarge)) xscale(titlegap(*5)) /* > */ ytitle("Survival Probability", size(medlarge)) yscale(titlegap(*5)) /* > */ ylabel(0.00(0.25)1.00,grid)/* > */ legend(pos(1) ring(0) col(1)) legend(size(small)) /* > */ legend( label(1 "Upper 95% confidence band") label(2 "Survival Estimate") /* > */ label(3 "Lower 95% confidence band") ) . graph export km_pt1.wmf, replace (file c:\Imbook\bwebpage\Section4\km_pt1.wmf written in Windows Metafile format) . . * (3B) Figure 17.4: Survival Function by Treatment (here ui) (p.605) . * sts graph, by(ui) . sts graph, by(ui) /* > */ scale (1.2) plotregion(style(none)) /* > */ title("Survival Function Estimates by UI Status") /* > */ xtitle("Unemployment Duration in 2-week intervals", size(medlarge)) xscale(titlegap(*5)) /* > */ ytitle("Survival Probability", size(medlarge)) yscale(titlegap(*5)) /* > */ legend(pos(1) ring(0) col(1)) legend(size(small)) /* > */ legend(label(1 "No UI (UI = 0)") label(2 "Received UI (UI = 1)") ) failure _d: censor1 == 1 analysis time _t: spell . graph export km_pt2.wmf, replace (file c:\Imbook\bwebpage\Section4\km_pt2.wmf written in Windows Metafile format) . . * (3C) Figure 17.5: Overall Cumulative Hazard Function (p.606) . * sts graph, cna . * Nicer graphs and also confidence bands are bolder and easier to read . sts gen cumhaz = na . sts gen lbcumhaz = lb(na) . sts gen ubcumhaz = ub(na) . sort spell . graph twoway (line ubcumhaz spell, msize(vtiny) mstyle(p2) c(J) clstyle(p1) clcolor(gs10)) /* > */ (line cumhaz spell, msize(vtiny) mstyle(p1) c(J) clstyle(p1)) /* > */ (line lbcumhaz spell, msize(vtiny) mstyle(p2) c(J) clstyle(p1) clcolor(gs10)), /* > */ scale(1.2) plotregion(style(none)) /* > */ title("Overall Cumulative Hazard Estimate") /* > */ xtitle("Unemployment Duration in 2-week intervals", size(medlarge)) xscale(titlegap(*5)) /* > */ ytitle("Cumulative Hazard", size(medlarge)) yscale(titlegap(*5)) /* > */ ylabel(0.00(0.50)1.50,grid)/* > */ legend(pos(11) ring(0) col(1)) legend(size(small)) /* > */ legend( label(1 "Upper 95% confidence band") label(2 "Cumulative Hazard Estimate") /* > */ label(3 "Lower 95% confidence band") ) . graph export na_pt1.wmf, replace (file c:\Imbook\bwebpage\Section4\na_pt1.wmf written in Windows Metafile format) . . * (3D) Figure 17.6: Cumulative Hazard Function by Treatment (here ui) (p.606) . * sts graph, na by(ui) . sts graph, na by(ui) /* > */ scale (1.2) plotregion(style(none)) /* > */ title("Cumulative Hazard Estimates by UI Status") /* > */ xtitle("Unemployment Duration in 2-week intervals", size(medlarge)) xscale(titlegap(*5)) /* > */ ytitle("Cumulative Hazard", size(medlarge)) yscale(titlegap(*5)) /* > */ legend(pos(1) ring(0) col(1)) legend(size(small)) /* > */ legend(label(1 "No UI (UI = 0)") label(2 "Received UI (UI = 1)") ) failure _d: censor1 == 1 analysis time _t: spell . graph export na_pt2.wmf, replace (file c:\Imbook\bwebpage\Section4\na_pt2.wmf written in Windows Metafile format) . . * (4) VARIOUS PARAMETRIC MODELS: COEFFICIENTS (Table 17.8) . . * streg default is to report hazard rates ratehr than coeffcients . * streg with nohr option reports coefficients . . * Create regressors . gen RR = reprate . gen DR = disrate . gen UI = ui . gen RRUI = RR*UI . gen DRUI = DR*UI . gen LOGWAGE = logwage . . * Define $xlist = list of regressors used in subsequent regressions . global xlist RR DR UI RRUI DRUI LOGWAGE /* > */ tenure slack abolpos explose stateur houshead married /* > */ female child ychild nonwhite age schlt12 schgt12 smsa bluecoll /* > */ mining constr transp trade fire services pubadmin /* > */ year85 year87 year89 midatl /* > */ encen wncen southatl escen wscen mountain pacific . . * Exponential regression . streg $xlist, nohr robust dist(exponential) failure _d: censor1 == 1 analysis time _t: spell Iteration 0: log pseudo-likelihood = -3012.4909 Iteration 1: log pseudo-likelihood = -2810.3791 Iteration 2: log pseudo-likelihood = -2701.8024 Iteration 3: log pseudo-likelihood = -2700.6911 Iteration 4: log pseudo-likelihood = -2700.6903 Iteration 5: log pseudo-likelihood = -2700.6903 Exponential regression -- log relative-hazard form No. of subjects = 3343 Number of obs = 3343 No. of failures = 1073 Time at risk = 20887 Wald chi2(40) = 565.24 Log pseudo-likelihood = -2700.6903 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ | Robust _t | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- RR | .4720235 .6005534 0.79 0.432 -.7050396 1.649087 DR | -.5756396 .7624489 -0.75 0.450 -2.070012 .9187327 UI | -1.424561 .2493917 -5.71 0.000 -1.91336 -.9357622 RRUI | .9655904 .6118408 1.58 0.115 -.2335956 2.164776 DRUI | -.1990635 1.019118 -0.20 0.845 -2.196498 1.798371 LOGWAGE | .3508005 .115598 3.03 0.002 .1242327 .5773684 tenure | -.0001462 .0064637 -0.02 0.982 -.0128147 .0125224 slack | -.2593666 .0759363 -3.42 0.001 -.4081991 -.1105342 abolpos | -.1550897 .0953306 -1.63 0.104 -.3419342 .0317549 explose | .198458 .0648354 3.06 0.002 .071383 .3255331 stateur | -.064626 .0229903 -2.81 0.005 -.1096862 -.0195659 houshead | .3812208 .0836602 4.56 0.000 .2172499 .5451918 married | .369552 .0786145 4.70 0.000 .2154705 .5236335 female | .1164067 .0852986 1.36 0.172 -.0507754 .2835888 child | -.0333008 .0794577 -0.42 0.675 -.1890352 .1224335 ychild | -.1449722 .1022781 -1.42 0.156 -.3454336 .0554892 nonwhite | -.6692066 .1188272 -5.63 0.000 -.9021037 -.4363095 age | -.0220821 .0039256 -5.63 0.000 -.0297762 -.0143879 schlt12 | -.1231414 .0966102 -1.27 0.202 -.3124939 .066211 schgt12 | .1114395 .082945 1.34 0.179 -.0511297 .2740087 smsa | .1922291 .0799904 2.40 0.016 .0354508 .3490075 bluecoll | -.2033718 .085129 -2.39 0.017 -.3702215 -.036522 mining | -.1205818 .1973575 -0.61 0.541 -.5073955 .2662319 constr | -.04475 .1081519 -0.41 0.679 -.2567237 .1672238 transp | -.1786694 .156034 -1.15 0.252 -.4844906 .1271517 trade | -.0345159 .1019152 -0.34 0.735 -.234266 .1652341 fire | .1120549 .1386716 0.81 0.419 -.1597365 .3838462 services | .1840002 .0983911 1.87 0.061 -.0088428 .3768432 pubadmin | .1090606 .2954211 0.37 0.712 -.4699541 .6880752 year85 | .2147661 .0888664 2.42 0.016 .0405911 .388941 year87 | .3541162 .0948499 3.73 0.000 .1682139 .5400186 year89 | .467082 .1104355 4.23 0.000 .2506325 .6835316 midatl | .0264112 .1465647 0.18 0.857 -.2608503 .3136727 encen | .0043916 .1502813 0.03 0.977 -.2901544 .2989375 wncen | .1724311 .1607689 1.07 0.283 -.1426703 .4875324 southatl | .2638807 .1183726 2.23 0.026 .0318747 .4958867 escen | .35414 .19317 1.83 0.067 -.0244664 .7327463 wscen | .3385896 .1433308 2.36 0.018 .0576664 .6195128 mountain | .0063693 .1538821 0.04 0.967 -.2952341 .3079727 pacific | .0770202 .2393505 0.32 0.748 -.3920982 .5461385 _cons | -4.079107 .8767097 -4.65 0.000 -5.797426 -2.360788 ------------------------------------------------------------------------------ . estimates store bexponential . . * Weibull regression . streg $xlist, nohr robust dist(weibull) failure _d: censor1 == 1 analysis time _t: spell Fitting constant-only model: Iteration 0: log pseudo-likelihood = -3012.4909 Iteration 1: log pseudo-likelihood = -3012.3543 Iteration 2: log pseudo-likelihood = -3012.3543 Fitting full model: Iteration 0: log pseudo-likelihood = -3012.3543 Iteration 1: log pseudo-likelihood = -2799.9064 Iteration 2: log pseudo-likelihood = -2688.7377 Iteration 3: log pseudo-likelihood = -2687.6004 Iteration 4: log pseudo-likelihood = -2687.5995 Iteration 5: log pseudo-likelihood = -2687.5995 Weibull regression -- log relative-hazard form No. of subjects = 3343 Number of obs = 3343 No. of failures = 1073 Time at risk = 20887 Wald chi2(40) = 501.65 Log pseudo-likelihood = -2687.5995 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ | Robust _t | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- RR | .4481156 .6381895 0.70 0.483 -.8027127 1.698944 DR | -.4269187 .8086983 -0.53 0.598 -2.011938 1.158101 UI | -1.496066 .2639679 -5.67 0.000 -2.013434 -.9786984 RRUI | 1.015226 .6455611 1.57 0.116 -.2500501 2.280503 DRUI | -.2988417 1.065384 -0.28 0.779 -2.386956 1.789272 LOGWAGE | .3655253 .12212 2.99 0.003 .1261745 .6048761 tenure | -.0011127 .0068716 -0.16 0.871 -.0145809 .0123554 slack | -.2652154 .0803214 -3.30 0.001 -.4226424 -.1077883 abolpos | -.1604227 .1012942 -1.58 0.113 -.3589557 .0381103 explose | .2075085 .0684715 3.03 0.002 .0733068 .3417103 stateur | -.0708745 .0242117 -2.93 0.003 -.1183286 -.0234204 houshead | .3976626 .0887192 4.48 0.000 .2237762 .571549 married | .3786057 .0830317 4.56 0.000 .2158665 .541345 female | .1260829 .0896987 1.41 0.160 -.0497233 .301889 child | -.0336778 .0839956 -0.40 0.688 -.1983061 .1309505 ychild | -.1613066 .108947 -1.48 0.139 -.3748389 .0522256 nonwhite | -.7025504 .12426 -5.65 0.000 -.9460956 -.4590052 age | -.0235823 .0041922 -5.63 0.000 -.0317989 -.0153658 schlt12 | -.1226759 .1022762 -1.20 0.230 -.3231335 .0777816 schgt12 | .1162848 .0880692 1.32 0.187 -.0563278 .2888973 smsa | .1999567 .0841129 2.38 0.017 .0350985 .3648149 bluecoll | -.1994925 .0899354 -2.22 0.027 -.3757626 -.0232223 mining | -.1015676 .2036644 -0.50 0.618 -.5007425 .2976073 constr | -.0253737 .1135609 -0.22 0.823 -.247949 .1972016 transp | -.1981522 .1672141 -1.19 0.236 -.5258858 .1295814 trade | -.0311361 .1079502 -0.29 0.773 -.2427146 .1804423 fire | .1262153 .1492527 0.85 0.398 -.1663145 .4187452 services | .2031673 .1038945 1.96 0.051 -.0004622 .4067968 pubadmin | .1117728 .3087374 0.36 0.717 -.4933415 .716887 year85 | .2374972 .093387 2.54 0.011 .054462 .4205325 year87 | .3787397 .1011782 3.74 0.000 .1804341 .5770454 year89 | .4920278 .1180472 4.17 0.000 .2606596 .7233959 midatl | .02465 .1542139 0.16 0.873 -.2776037 .3269036 encen | -.0014111 .1579065 -0.01 0.993 -.3109023 .30808 wncen | .1844363 .1694444 1.09 0.276 -.1476687 .5165413 southatl | .2740974 .1250481 2.19 0.028 .0290076 .5191872 escen | .367742 .2024771 1.82 0.069 -.0291058 .7645899 wscen | .3440005 .1527804 2.25 0.024 .0445563 .6434446 mountain | .0159627 .1620188 0.10 0.922 -.3015883 .3335136 pacific | .0849532 .2504077 0.34 0.734 -.4058368 .5757432 _cons | -4.357886 .9196792 -4.74 0.000 -6.160424 -2.555347 -------------+---------------------------------------------------------------- /ln_p | .1215314 .0194374 6.25 0.000 .0834348 .1596281 -------------+---------------------------------------------------------------- p | 1.129225 .0219492 1.087014 1.173075 1/p | .8855632 .0172131 .8524608 .9199511 ------------------------------------------------------------------------------ . estimates store bweibull . . * Gompertz regression . streg $xlist, nohr robust dist(gompertz) failure _d: censor1 == 1 analysis time _t: spell Fitting constant-only model: Iteration 0: log pseudo-likelihood = -3012.4909 Iteration 1: log pseudo-likelihood = -3002.0916 Iteration 2: log pseudo-likelihood = -3002.026 Iteration 3: log pseudo-likelihood = -3002.026 Fitting full model: Iteration 0: log pseudo-likelihood = -3002.026 Iteration 1: log pseudo-likelihood = -2796.0001 Iteration 2: log pseudo-likelihood = -2701.6693 Iteration 3: log pseudo-likelihood = -2700.6057 Iteration 4: log pseudo-likelihood = -2700.605 Iteration 5: log pseudo-likelihood = -2700.605 Gompertz regression -- log relative-hazard form No. of subjects = 3343 Number of obs = 3343 No. of failures = 1073 Time at risk = 20887 Wald chi2(40) = 529.75 Log pseudo-likelihood = -2700.605 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ | Robust _t | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- RR | .472405 .6033813 0.78 0.434 -.7102005 1.655011 DR | -.5627894 .7646131 -0.74 0.462 -2.061404 .9358247 UI | -1.428355 .2508349 -5.69 0.000 -1.919982 -.9367272 RRUI | .9689413 .6144464 1.58 0.115 -.2353514 2.173234 DRUI | -.2112495 1.021112 -0.21 0.836 -2.212593 1.790094 LOGWAGE | .3524722 .1162698 3.03 0.002 .1245876 .5803567 tenure | -.0002233 .0065002 -0.03 0.973 -.0129635 .0125168 slack | -.2593933 .0762829 -3.40 0.001 -.4089051 -.1098815 abolpos | -.1552595 .0958002 -1.62 0.105 -.3430244 .0325053 explose | .1991286 .0650876 3.06 0.002 .0715592 .326698 stateur | -.065244 .0231645 -2.82 0.005 -.1106456 -.0198424 houshead | .3822818 .0841671 4.54 0.000 .2173173 .5472464 married | .3700141 .0789107 4.69 0.000 .215352 .5246762 female | .1170987 .0856236 1.37 0.171 -.0507206 .2849179 child | -.0331425 .0798246 -0.42 0.678 -.1895958 .1233108 ychild | -.1466596 .102884 -1.43 0.154 -.3483085 .0549893 nonwhite | -.6720521 .1197092 -5.61 0.000 -.9066778 -.4374264 age | -.0222175 .0039787 -5.58 0.000 -.0300157 -.0144193 schlt12 | -.1228615 .097015 -1.27 0.205 -.3130075 .0672845 schgt12 | .1121295 .0831976 1.35 0.178 -.0509348 .2751938 smsa | .1925807 .0803478 2.40 0.017 .0351019 .3500596 bluecoll | -.203405 .0854986 -2.38 0.017 -.3709791 -.0358309 mining | -.1183683 .1976441 -0.60 0.549 -.5057435 .269007 constr | -.0423947 .1082891 -0.39 0.695 -.2546375 .169848 transp | -.1799724 .1570001 -1.15 0.252 -.487687 .1277422 trade | -.0341793 .1023611 -0.33 0.738 -.2348034 .1664447 fire | .1143611 .1398161 0.82 0.413 -.1596734 .3883955 services | .1854033 .0987923 1.88 0.061 -.0082261 .3790327 pubadmin | .1089298 .2965867 0.37 0.713 -.4723694 .690229 year85 | .2172389 .0890506 2.44 0.015 .0427028 .3917749 year87 | .3564181 .095298 3.74 0.000 .1696374 .5431988 year89 | .4690752 .1114266 4.21 0.000 .250683 .6874674 midatl | .026766 .1471298 0.18 0.856 -.2616031 .3151351 encen | .0043808 .15089 0.03 0.977 -.2913581 .3001198 wncen | .1735986 .1614007 1.08 0.282 -.142741 .4899382 southatl | .2647448 .1188746 2.23 0.026 .031755 .4977347 escen | .3560917 .1938142 1.84 0.066 -.0237772 .7359606 wscen | .3393956 .1442438 2.35 0.019 .0566829 .6221082 mountain | .0076507 .1545162 0.05 0.961 -.2951954 .3104969 pacific | .0778885 .2400495 0.32 0.746 -.3925999 .5483769 _cons | -4.09733 .8802997 -4.65 0.000 -5.822686 -2.371975 -------------+---------------------------------------------------------------- gamma | .002658 .0067759 0.39 0.695 -.0106225 .0159386 ------------------------------------------------------------------------------ . estimates store bgompertz . . * Weibull regression . stcox $xlist, nohr robust failure _d: censor1 == 1 analysis time _t: spell Iteration 0: log pseudo-likelihood = -7981.9304 Iteration 1: log pseudo-likelihood = -7731.2822 Iteration 2: log pseudo-likelihood = -7717.3198 Iteration 3: log pseudo-likelihood = -7717.2334 Iteration 4: log pseudo-likelihood = -7717.2334 Refining estimates: Iteration 0: log pseudo-likelihood = -7717.2334 Cox regression -- Breslow method for ties No. of subjects = 3343 Number of obs = 3343 No. of failures = 1073 Time at risk = 20887 Wald chi2(40) = 540.98 Log pseudo-likelihood = -7717.2334 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ | Robust _t | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- RR | .5222796 .5711698 0.91 0.361 -.5971926 1.641752 DR | -.752507 .72175 -1.04 0.297 -2.167111 .6620971 UI | -1.317719 .2372893 -5.55 0.000 -1.782798 -.8526409 RRUI | .8822462 .582115 1.52 0.130 -.2586783 2.023171 DRUI | -.0951357 .977774 -0.10 0.922 -2.011538 1.821266 LOGWAGE | .3352639 .1106483 3.03 0.002 .1183972 .5521306 tenure | .0008278 .0061286 0.14 0.893 -.0111841 .0128396 slack | -.247863 .0721173 -3.44 0.001 -.3892103 -.1065158 abolpos | -.1511638 .0905035 -1.67 0.095 -.3285475 .0262198 explose | .1865068 .0615742 3.03 0.002 .0658236 .30719 stateur | -.0590475 .022085 -2.67 0.008 -.1023334 -.0157616 houshead | .3601866 .0794827 4.53 0.000 .2044035 .5159698 married | .358819 .0746355 4.81 0.000 .2125362 .5051019 female | .1002758 .0813277 1.23 0.218 -.0591236 .2596753 child | -.0396054 .0755365 -0.52 0.600 -.1876542 .1084435 ychild | -.1276638 .0967856 -1.32 0.187 -.3173602 .0620325 nonwhite | -.6394475 .1151332 -5.55 0.000 -.8651043 -.4137906 age | -.0204623 .0037593 -5.44 0.000 -.0278305 -.0130942 schlt12 | -.1220585 .0920073 -1.33 0.185 -.3023895 .0582726 schgt12 | .1104817 .0783542 1.41 0.159 -.0430897 .2640531 smsa | .1864841 .0766075 2.43 0.015 .0363361 .3366321 bluecoll | -.2108023 .080867 -2.61 0.009 -.3692986 -.052306 mining | -.1238251 .1906352 -0.65 0.516 -.4974632 .249813 constr | -.054455 .1029488 -0.53 0.597 -.256231 .1473209 transp | -.1551657 .1466515 -1.06 0.290 -.4425973 .1322659 trade | -.0383252 .0968106 -0.40 0.692 -.2280706 .1514201 fire | .1097585 .1300779 0.84 0.399 -.1451895 .3647065 services | .1666262 .0939507 1.77 0.076 -.0175138 .3507662 pubadmin | .1022002 .2829817 0.36 0.718 -.4524336 .6568341 year85 | .204162 .084908 2.40 0.016 .0377454 .3705786 year87 | .3384229 .0899115 3.76 0.000 .1621997 .5146462 year89 | .4486559 .104937 4.28 0.000 .2429832 .6543286 midatl | .0342238 .140515 0.24 0.808 -.2411805 .3096282 encen | .0174597 .1438862 0.12 0.903 -.2645521 .2994716 wncen | .1650967 .1532559 1.08 0.281 -.1352795 .4654728 southatl | .2518023 .1127138 2.23 0.025 .0308874 .4727172 escen | .3450422 .1839818 1.88 0.061 -.0155554 .7056398 wscen | .3316752 .1359801 2.44 0.015 .0651591 .5981914 mountain | .009484 .1468626 0.06 0.949 -.2783613 .2973293 pacific | .0720292 .2263339 0.32 0.750 -.3715771 .5156355 ------------------------------------------------------------------------------ . estimates store bcox . . * Display Results for Table 17.8 (page 607) . estimates table bexponential bweibull bgompertz, t stats(N ll) b(%8.3f) /* > */ keep(RR DR UI RRUI DRUI LOGWAGE _cons) ----------------------------------------------- Variable | bexpon~l bweibull bgompe~z -------------+--------------------------------- RR | 0.472 0.448 0.472 | 0.79 0.70 0.78 DR | -0.576 -0.427 -0.563 | -0.75 -0.53 -0.74 UI | -1.425 -1.496 -1.428 | -5.71 -5.67 -5.69 RRUI | 0.966 1.015 0.969 | 1.58 1.57 1.58 DRUI | -0.199 -0.299 -0.211 | -0.20 -0.28 -0.21 LOGWAGE | 0.351 0.366 0.352 | 3.03 2.99 3.03 _cons | -4.079 -4.358 -4.097 | -4.65 -4.74 -4.65 -------------+--------------------------------- N | 3343.000 3343.000 3343.000 ll | -2.7e+03 -2.7e+03 -2.7e+03 ----------------------------------------------- legend: b/t . estimates table bcox, t stats(N ll) b(%8.3f) keep(RR DR UI RRUI DRUI LOGWAGE) ------------------------- Variable | bcox -------------+----------- RR | 0.522 | 0.91 DR | -0.753 | -1.04 UI | -1.318 | -5.55 RRUI | 0.882 | 1.52 DRUI | -0.095 | -0.10 LOGWAGE | 0.335 | 3.03 -------------+----------- N | 3343.000 ll | -7.7e+03 ------------------------- legend: b/t . . * (5) VARIOUS PARAMETRIC MODELS: HAZARD RATIOS (Table 17.9, page 608)) . . * streg default is to report hazard rates rather than coeffcients . * streg with nohr option reports coefficients . . * Exponential regression . streg $xlist, robust dist(exponential) failure _d: censor1 == 1 analysis time _t: spell Iteration 0: log pseudo-likelihood = -3012.4909 Iteration 1: log pseudo-likelihood = -2810.3791 Iteration 2: log pseudo-likelihood = -2701.8024 Iteration 3: log pseudo-likelihood = -2700.6911 Iteration 4: log pseudo-likelihood = -2700.6903 Iteration 5: log pseudo-likelihood = -2700.6903 Exponential regression -- log relative-hazard form No. of subjects = 3343 Number of obs = 3343 No. of failures = 1073 Time at risk = 20887 Wald chi2(40) = 565.24 Log pseudo-likelihood = -2700.6903 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ | Robust _t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- RR | 1.603235 .9628283 0.79 0.432 .494089 5.202226 DR | .5623451 .4287594 -0.75 0.450 .1261843 2.506112 UI | .2406141 .0600072 -5.71 0.000 .1475837 .3922867 RRUI | 2.626338 1.606901 1.58 0.115 .7916819 8.712654 DRUI | .8194978 .8351649 -0.20 0.845 .1111919 6.039799 LOGWAGE | 1.420204 .1641727 3.03 0.002 1.132279 1.781344 tenure | .9998539 .0064627 -0.02 0.982 .9872671 1.012601 slack | .7715401 .0585879 -3.42 0.001 .6648465 .8953557 abolpos | .8563384 .0816353 -1.63 0.104 .7103949 1.032264 explose | 1.219521 .0790681 3.06 0.002 1.073992 1.384769 stateur | .937418 .0215515 -2.81 0.005 .8961153 .9806243 houshead | 1.464071 .1224844 4.56 0.000 1.242655 1.724939 married | 1.447086 .1137619 4.70 0.000 1.240445 1.68815 female | 1.123453 .0958289 1.36 0.172 .9504921 1.327887 child | .9672475 .0768553 -0.42 0.675 .8277574 1.130244 ychild | .8650463 .0884753 -1.42 0.156 .7079133 1.057058 nonwhite | .5121147 .0608532 -5.63 0.000 .4057153 .6464176 age | .9781599 .0038399 -5.63 0.000 .9706627 .9857151 schlt12 | .8841386 .0854168 -1.27 0.202 .7316201 1.068452 schgt12 | 1.117886 .0927231 1.34 0.179 .9501554 1.315226 smsa | 1.211948 .0969443 2.40 0.016 1.036087 1.41766 bluecoll | .8159748 .0694631 -2.39 0.017 .6905813 .9641369 mining | .8864046 .1749386 -0.61 0.541 .6020616 1.305038 constr | .9562365 .1034188 -0.41 0.679 .7735819 1.182019 transp | .8363823 .1305041 -1.15 0.252 .6160109 1.135589 trade | .966073 .0984575 -0.34 0.735 .7911514 1.179669 fire | 1.118574 .1551145 0.81 0.419 .8523684 1.46792 services | 1.202016 .1182677 1.87 0.061 .9911962 1.457676 pubadmin | 1.11523 .3294624 0.37 0.712 .625031 1.989882 year85 | 1.239572 .1101563 2.42 0.016 1.041426 1.475418 year87 | 1.424921 .1351536 3.73 0.000 1.18319 1.716039 year89 | 1.595332 .1761812 4.23 0.000 1.284838 1.980861 midatl | 1.026763 .1504872 0.18 0.857 .7703962 1.368442 encen | 1.004401 .1509427 0.03 0.977 .7481481 1.348425 wncen | 1.18819 .191024 1.07 0.283 .8670399 1.628293 southatl | 1.301973 .1541179 2.23 0.026 1.032388 1.641953 escen | 1.424955 .2752586 1.83 0.067 .9758305 2.080787 wscen | 1.402967 .2010884 2.36 0.018 1.059362 1.858023 mountain | 1.00639 .1548654 0.04 0.967 .7443573 1.360664 pacific | 1.080064 .2585138 0.32 0.748 .6756378 1.726573 ------------------------------------------------------------------------------ . . * Weibull regression . streg $xlist, robust dist(weibull) failure _d: censor1 == 1 analysis time _t: spell Fitting constant-only model: Iteration 0: log pseudo-likelihood = -3012.4909 Iteration 1: log pseudo-likelihood = -3012.3543 Iteration 2: log pseudo-likelihood = -3012.3543 Fitting full model: Iteration 0: log pseudo-likelihood = -3012.3543 Iteration 1: log pseudo-likelihood = -2799.9064 Iteration 2: log pseudo-likelihood = -2688.7377 Iteration 3: log pseudo-likelihood = -2687.6004 Iteration 4: log pseudo-likelihood = -2687.5995 Iteration 5: log pseudo-likelihood = -2687.5995 Weibull regression -- log relative-hazard form No. of subjects = 3343 Number of obs = 3343 No. of failures = 1073 Time at risk = 20887 Wald chi2(40) = 501.65 Log pseudo-likelihood = -2687.5995 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ | Robust _t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- RR | 1.56536 .998996 0.70 0.483 .4481117 5.46817 DR | .6525166 .527689 -0.53 0.598 .1337292 3.183881 UI | .2240097 .0591314 -5.67 0.000 .1335294 .3757999 RRUI | 2.759988 1.781741 1.57 0.116 .7787618 9.781599 DRUI | .7416768 .7901705 -0.28 0.779 .0919091 5.985096 LOGWAGE | 1.441271 .176008 2.99 0.003 1.13448 1.831025 tenure | .9988879 .006864 -0.16 0.871 .9855249 1.012432 slack | .7670407 .0616098 -3.30 0.001 .6553129 .8978176 abolpos | .8517837 .0862808 -1.58 0.113 .6984053 1.038846 explose | 1.230608 .0842616 3.03 0.002 1.076061 1.407352 stateur | .9315788 .0225551 -2.93 0.003 .8884041 .9768517 houshead | 1.488342 .1320445 4.48 0.000 1.250791 1.771008 married | 1.460247 .1212469 4.56 0.000 1.240937 1.718316 female | 1.134376 .101752 1.41 0.160 .9514927 1.352411 child | .966883 .0812139 -0.40 0.688 .8201188 1.139911 ychild | .8510311 .0927173 -1.48 0.139 .6874 1.053613 nonwhite | .4953204 .0615485 -5.65 0.000 .388254 .6319119 age | .9766936 .0040945 -5.63 0.000 .9687014 .9847517 schlt12 | .8845503 .0904684 -1.20 0.230 .7238772 1.080887 schgt12 | 1.123316 .0989295 1.32 0.187 .9452293 1.334955 smsa | 1.22135 .1027313 2.38 0.017 1.035722 1.440247 bluecoll | .8191464 .0736702 -2.22 0.027 .6867654 .9770452 mining | .9034201 .1839945 -0.50 0.618 .6060805 1.346633 constr | .9749455 .1107157 -0.22 0.823 .7803997 1.21799 transp | .820245 .1371565 -1.19 0.236 .5910316 1.138352 trade | .9693436 .1046408 -0.29 0.773 .7844954 1.197747 fire | 1.134526 .1693311 0.85 0.398 .8467799 1.520053 services | 1.225277 .1272996 1.96 0.051 .9995379 1.501999 pubadmin | 1.118259 .3452483 0.36 0.717 .6105827 2.048048 year85 | 1.268072 .1184214 2.54 0.011 1.055972 1.522772 year87 | 1.460443 .147765 3.74 0.000 1.197737 1.780769 year89 | 1.63563 .1930814 4.17 0.000 1.297786 2.061422 midatl | 1.024956 .1580625 0.16 0.873 .757597 1.386668 encen | .9985899 .1576839 -0.01 0.993 .7327855 1.36081 wncen | 1.20254 .2037638 1.09 0.276 .8627169 1.67622 southatl | 1.315343 .1644812 2.19 0.028 1.029432 1.680661 escen | 1.444469 .292472 1.82 0.069 .9713137 2.148113 wscen | 1.410579 .2155089 2.25 0.024 1.045564 1.903025 mountain | 1.016091 .1646258 0.10 0.922 .7396425 1.395864 pacific | 1.088666 .2726104 0.34 0.734 .6664189 1.778452 -------------+---------------------------------------------------------------- /ln_p | .1215314 .0194374 6.25 0.000 .0834348 .1596281 -------------+---------------------------------------------------------------- p | 1.129225 .0219492 1.087014 1.173075 1/p | .8855632 .0172131 .8524608 .9199511 ------------------------------------------------------------------------------ . . * Gompertz regression . streg $xlist, robust dist(gompertz) failure _d: censor1 == 1 analysis time _t: spell Fitting constant-only model: Iteration 0: log pseudo-likelihood = -3012.4909 Iteration 1: log pseudo-likelihood = -3002.0916 Iteration 2: log pseudo-likelihood = -3002.026 Iteration 3: log pseudo-likelihood = -3002.026 Fitting full model: Iteration 0: log pseudo-likelihood = -3002.026 Iteration 1: log pseudo-likelihood = -2796.0001 Iteration 2: log pseudo-likelihood = -2701.6693 Iteration 3: log pseudo-likelihood = -2700.6057 Iteration 4: log pseudo-likelihood = -2700.605 Iteration 5: log pseudo-likelihood = -2700.605 Gompertz regression -- log relative-hazard form No. of subjects = 3343 Number of obs = 3343 No. of failures = 1073 Time at risk = 20887 Wald chi2(40) = 529.75 Log pseudo-likelihood = -2700.605 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ | Robust _t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- RR | 1.603847 .9677311 0.78 0.434 .4915456 5.233135 DR | .5696179 .4355373 -0.74 0.462 .1272752 2.549315 UI | .239703 .0601259 -5.69 0.000 .1466096 .3919084 RRUI | 2.635153 1.61916 1.58 0.115 .7902931 8.786655 DRUI | .809572 .8266639 -0.21 0.836 .1094166 5.990014 LOGWAGE | 1.42258 .165403 3.03 0.002 1.132681 1.786676 tenure | .9997767 .0064987 -0.03 0.973 .9871202 1.012595 slack | .7715195 .0588538 -3.40 0.001 .6643773 .8959403 abolpos | .856193 .0820234 -1.62 0.105 .7096209 1.033039 explose | 1.220339 .079429 3.06 0.002 1.074182 1.386383 stateur | .9368388 .0217014 -2.82 0.005 .895256 .9803531 houshead | 1.465625 .1233575 4.54 0.000 1.242738 1.728487 married | 1.447755 .1142433 4.69 0.000 1.240298 1.689912 female | 1.12423 .0962607 1.37 0.171 .9505442 1.329653 child | .9674007 .0772224 -0.42 0.678 .8272934 1.131236 ychild | .8635879 .0888493 -1.43 0.154 .7058811 1.056529 nonwhite | .5106596 .0611307 -5.61 0.000 .4038637 .6456961 age | .9780275 .0038913 -5.58 0.000 .9704303 .9856841 schlt12 | .8843861 .0857988 -1.27 0.205 .7312444 1.0696 schgt12 | 1.118658 .0930697 1.35 0.178 .9503406 1.316786 smsa | 1.212374 .0974117 2.40 0.017 1.035725 1.419152 bluecoll | .8159478 .0697624 -2.38 0.017 .6900584 .9648035 mining | .8883688 .1755808 -0.60 0.549 .603057 1.308664 constr | .9584913 .1037942 -0.39 0.695 .7751974 1.185125 transp | .8352933 .1311411 -1.15 0.252 .614045 1.13626 trade | .9663982 .0989216 -0.33 0.738 .7907263 1.181098 fire | 1.121157 .1567557 0.82 0.413 .8524222 1.474613 services | 1.203704 .1189167 1.88 0.061 .9918076 1.460871 pubadmin | 1.115084 .3307191 0.37 0.713 .6235232 1.994172 year85 | 1.242641 .110658 2.44 0.015 1.043628 1.479605 year87 | 1.428205 .1361051 3.74 0.000 1.184875 1.721505 year89 | 1.598515 .1781172 4.21 0.000 1.284903 1.988673 midatl | 1.027127 .1511211 0.18 0.856 .7698165 1.370444 encen | 1.00439 .1515525 0.03 0.977 .747248 1.35002 wncen | 1.189578 .1919987 1.08 0.282 .8669786 1.632215 southatl | 1.303098 .1549053 2.23 0.026 1.032265 1.644991 escen | 1.427739 .276716 1.84 0.066 .9765033 2.087486 wscen | 1.404099 .2025325 2.35 0.019 1.05832 1.862851 mountain | 1.00768 .1557029 0.05 0.961 .7443861 1.364103 pacific | 1.081002 .2594941 0.32 0.746 .6752989 1.730442 -------------+---------------------------------------------------------------- gamma | .002658 .0067759 0.39 0.695 -.0106225 .0159386 ------------------------------------------------------------------------------ . . * Cox regression . stcox $xlist, robust failure _d: censor1 == 1 analysis time _t: spell Iteration 0: log pseudo-likelihood = -7981.9304 Iteration 1: log pseudo-likelihood = -7731.2822 Iteration 2: log pseudo-likelihood = -7717.3198 Iteration 3: log pseudo-likelihood = -7717.2334 Iteration 4: log pseudo-likelihood = -7717.2334 Refining estimates: Iteration 0: log pseudo-likelihood = -7717.2334 Cox regression -- Breslow method for ties No. of subjects = 3343 Number of obs = 3343 No. of failures = 1073 Time at risk = 20887 Wald chi2(40) = 540.98 Log pseudo-likelihood = -7717.2334 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ | Robust _t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- RR | 1.685866 .962916 0.91 0.361 .5503545 5.164209 DR | .4711838 .3400769 -1.04 0.297 .1145079 1.938854 UI | .2677452 .0635331 -5.55 0.000 .168167 .4262877 RRUI | 2.416321 1.406577 1.52 0.130 .7720714 7.562264 DRUI | .9092495 .8890406 -0.10 0.922 .1337828 6.179678 LOGWAGE | 1.398309 .1547206 3.03 0.002 1.125691 1.73695 tenure | 1.000828 .0061337 0.14 0.893 .9888782 1.012922 slack | .7804668 .0562851 -3.44 0.001 .6775918 .8989608 abolpos | .8597068 .0778065 -1.67 0.095 .7199688 1.026567 explose | 1.205033 .0741989 3.03 0.002 1.068038 1.359599 stateur | .942662 .0208187 -2.67 0.008 .9027285 .9843619 houshead | 1.433597 .1139461 4.53 0.000 1.226793 1.675262 married | 1.431638 .106851 4.81 0.000 1.236811 1.657154 female | 1.105476 .0899059 1.23 0.218 .9425903 1.296509 child | .9611687 .0726033 -0.52 0.600 .8289013 1.114542 ychild | .8801492 .0851858 -1.32 0.187 .7280685 1.063997 nonwhite | .5275839 .0607424 -5.55 0.000 .4210076 .6611394 age | .9797456 .0036832 -5.44 0.000 .9725532 .9869912 schlt12 | .8850966 .0814354 -1.33 0.185 .7390501 1.060004 schgt12 | 1.116816 .0875072 1.41 0.159 .9578255 1.302197 smsa | 1.205005 .0923125 2.43 0.015 1.037004 1.400224 bluecoll | .8099341 .0654969 -2.61 0.009 .6912189 .9490384 mining | .8835344 .1684327 -0.65 0.516 .6080713 1.283785 constr | .9470011 .0974926 -0.53 0.597 .7739632 1.158726 transp | .8562733 .1255737 -1.06 0.290 .6423659 1.141412 trade | .9623999 .0931706 -0.40 0.692 .796068 1.163485 fire | 1.116009 .1451681 0.84 0.399 .8648584 1.440091 services | 1.181313 .1109851 1.77 0.076 .9826387 1.420155 pubadmin | 1.107605 .313432 0.36 0.718 .6360783 1.928677 year85 | 1.226497 .1041394 2.40 0.016 1.038467 1.448572 year87 | 1.402734 .1261218 3.76 0.000 1.176095 1.673046 year89 | 1.566206 .1643529 4.28 0.000 1.275047 1.92385 midatl | 1.034816 .1454072 0.24 0.808 .7856998 1.362918 encen | 1.017613 .1464205 0.12 0.903 .7675496 1.349146 wncen | 1.179507 .1807665 1.08 0.281 .8734718 1.592767 southatl | 1.286342 .1449884 2.23 0.025 1.031369 1.604348 escen | 1.41205 .2597913 1.88 0.061 .984565 2.025142 wscen | 1.3933 .1894611 2.44 0.015 1.067329 1.818826 mountain | 1.009529 .148262 0.06 0.949 .7570232 1.346259 pacific | 1.074687 .243238 0.32 0.750 .6896459 1.674702 ------------------------------------------------------------------------------ . . * Display results for Table 17.9 page 608 . * Not possible here as estimates table gives coefficients not hazard rates . * Instead need to use output for each model . * Not sure why t-statistics differ somewhat from those in Table 17.9 . . ********** CLOSE OUTPUT ********** . log close log: c:\Imbook\bwebpage\Section4\mma17p4duration.txt log type: text closed on: 19 May 2005, 15:25:17 ----------------------------------------------------------------------------------------------------