------------------------------------------------------------------------------------------------------ log: c:\Imbook\bwebpage\Section6\mma24p2poiscluster.txt log type: text opened on: 24 May 2005, 16:35:22 . . ********** OVERVIEW OF MMA24P2POISCLUSTER.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 24.7 pages 848-53 Table 24.6 . * Cluster robust inference for Poisson cross-section application using . * Vietnam Living Standard Survey data . . * (0) Descriptive Statistics (Table 24.3 second half) . * (1) Frequencies of data (Table 24.5) . * (2) Poisson regression with individual-level data (Table 24.6) . . * The results differ in second significant digit from those in text . * despite same sample size. Not sure why. . . * For Table 24.4 for clustered household data see MMA24P1OLSCLUSTER.DO . . * The Poisson cluster effects model is . * y_it ~ Poiss0n(x_it'b + a_i) . * Default xtreg output assumes Poisson distribution - var = mean. . * This is usually too strong an assumption. . * Instead should get cluster-robust errors after xtpois . * See Section 21.2.3 pages 709-12 and section 23.26 pages 788-9 . * Stata Version 8 does not do this. . * Here we do a panel bootstrap - results not reported in the text . . * To speed up programs reduce breps - the number of bootstrap reps . * This program takes a long time if bootstrap . . * To run this program you need data set . * vietnam_ex2.dta . . ********** SETUP ********** . . set more off . version 8.0 . set scheme s1mono /* Used for graphs */ . . ********** DATA DESCRIPTION ********** . . * The data comes from World Bank 1997 Vietnam Living Standards Survey . * A subset was used in chapter 4.6.4. . * The larger sample here is described on pages 848-9 . . * The data are HOUSEHOLD data . * There are N=5006 individuals in 194 clusters (communes) . . * The separate data set vietnam_ex1.dta has individual level data . . ********** READ IN INDIVIDUAL-LEVEL DATA and SUMMARIZE (Table 24.3) ********** . . use vietnam_ex2.dta, clear . desc Contains data from vietnam_ex2.dta obs: 27,766 vars: 12 11 Apr 2005 12:33 size: 1,443,832 (85.9% of memory free) ------------------------------------------------------------------------------- storage display value variable name type format label variable label ------------------------------------------------------------------------------- COMPED98 float %9.0g SEX float %9.0g AGE float %9.0g MARRIED float %9.0g ILLDUM float %9.0g INJDUM float %9.0g ILLDAYS float %9.0g ACTDAYS float %9.0g PHARVIS float %9.0g HLTHINS float %9.0g lnhhinc float %9.0g commune float %9.0g ------------------------------------------------------------------------------- Sorted by: . sum Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- COMPED98 | 27765 3.390672 1.93115 0 11 SEX | 27765 .5111471 .4998847 0 1 AGE | 27765 2.977504 .9671446 0 4.59512 MARRIED | 27765 .3988835 .4896775 0 1 ILLDUM | 27765 .6219701 .8995068 0 9 -------------+-------------------------------------------------------- INJDUM | 27765 .0096885 .0979537 0 1 ILLDAYS | 27765 2.804034 5.45823 0 60 ACTDAYS | 27765 .0657302 1.115939 0 30 PHARVIS | 27765 .5117594 1.313427 0 30 HLTHINS | 27765 .1625788 .3689876 0 1 -------------+-------------------------------------------------------- lnhhinc | 27765 2.60261 .6244145 .0467014 5.405502 commune | 27765 101.5266 56.28334 1 194 . . rename COMPED98 EDUC . rename ILLDUM ILLNESS . rename INJDUM INJURY . rename HLTHINS INSURANCE . rename lnhhinc LNHHEXP . rename commune COMMUNE . . * Following should give same descriptive statistics . * as in bottom half (Household) in Table 24.3 p.850 . * But there are is a difference for LNHHEXP plus here no data on MEDEXP . sum PHARVIS LNHHEXP AGE SEX MARRIED EDUC ILLNESS INJURY ILLDAYS ACTDAYS INSURANCE COMMUNE Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- PHARVIS | 27765 .5117594 1.313427 0 30 LNHHEXP | 27765 2.60261 .6244145 .0467014 5.405502 AGE | 27765 2.977504 .9671446 0 4.59512 SEX | 27765 .5111471 .4998847 0 1 MARRIED | 27765 .3988835 .4896775 0 1 -------------+-------------------------------------------------------- EDUC | 27765 3.390672 1.93115 0 11 ILLNESS | 27765 .6219701 .8995068 0 9 INJURY | 27765 .0096885 .0979537 0 1 ILLDAYS | 27765 2.804034 5.45823 0 60 ACTDAYS | 27765 .0657302 1.115939 0 30 -------------+-------------------------------------------------------- INSURANCE | 27765 .1625788 .3689876 0 1 COMMUNE | 27765 101.5266 56.28334 1 194 . sum LNHHEXP, detail LNHHEXP ------------------------------------------------------------- Percentiles Smallest 1% 1.302267 .0467014 5% 1.658267 .1111674 10% 1.875315 .3755146 Obs 27765 25% 2.188848 .4177101 Sum of Wgt. 27765 50% 2.534935 Mean 2.60261 Largest Std. Dev. .6244145 75% 2.962732 5.405502 90% 3.458658 5.405502 Variance .3898934 95% 3.737957 5.405502 Skewness .4925002 99% 4.295394 5.405502 Kurtosis 3.583693 . . * Following gives Table 24.5 (page 852) frequencies . * These differ in some places from Table 24.5 - especially for number = 0 . tabulate PHARVIS PHARVIS | Freq. Percent Cum. ------------+----------------------------------- 0 | 20,668 74.44 74.44 1 | 3,829 13.79 88.23 2 | 1,716 6.18 94.41 3 | 777 2.80 97.21 4 | 359 1.29 98.50 5 | 174 0.63 99.13 6 | 64 0.23 99.36 7 | 43 0.15 99.51 8 | 16 0.06 99.57 9 | 4 0.01 99.59 10 | 78 0.28 99.87 11 | 1 0.00 99.87 12 | 5 0.02 99.89 13 | 1 0.00 99.89 14 | 3 0.01 99.90 15 | 9 0.03 99.94 16 | 1 0.00 99.94 20 | 8 0.03 99.97 22 | 2 0.01 99.97 27 | 1 0.00 99.98 28 | 3 0.01 99.99 30 | 3 0.01 100.00 ------------+----------------------------------- Total | 27,765 100.00 . . * Histogram with kernel density estimate . hist PHARVIS, discrete kdensity (start=0, width=1) . . * Write data to a text (ascii) file so can use with programs other than Stata . outfile PHARVIS LNHHEXP AGE SEX MARRIED EDUC ILLNESS INJURY ILLDAYS /* > */ ACTDAYS INSURANCE COMMUNE using vietnam_ex2.asc, replace . . ********** ANALYSIS: CLUSTER ANALYSIS FOR POISSON MODEL [Table 24.6 p.851] ********* . . * Regressor list for the Poisson regressions . global XLISTPOISSON LNHHEXP INSURANCE SEX AGE MARRIED ILLDAYS ACTDAYS INJURY ILLNESS EDUC . . * Poisson with usual standard errors (Table 24.6 columns 1-2) . poisson PHARVIS $XLISTPOISSON Iteration 0: log likelihood = -26309.924 Iteration 1: log likelihood = -25300.337 Iteration 2: log likelihood = -25281.839 Iteration 3: log likelihood = -25281.786 Iteration 4: log likelihood = -25281.786 Poisson regression Number of obs = 27765 LR chi2(10) = 13226.50 Prob > chi2 = 0.0000 Log likelihood = -25281.786 Pseudo R2 = 0.2073 ------------------------------------------------------------------------------ PHARVIS | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- LNHHEXP | .078686 .0138419 5.68 0.000 .0515564 .1058156 INSURANCE | -.2485716 .0259704 -9.57 0.000 -.2994727 -.1976706 SEX | .0851733 .0171697 4.96 0.000 .0515213 .1188253 AGE | .0252426 .0106126 2.38 0.017 .0044423 .0460429 MARRIED | .1239639 .0209267 5.92 0.000 .0829483 .1649795 ILLDAYS | .0429083 .0010728 40.00 0.000 .0408057 .0450109 ACTDAYS | .0089793 .0052409 1.71 0.087 -.0012927 .0192514 INJURY | .1717029 .0747292 2.30 0.022 .0252364 .3181694 ILLNESS | .5623976 .0064536 87.15 0.000 .5497488 .5750464 EDUC | -.0524459 .0048173 -10.89 0.000 -.0618878 -.0430041 _cons | -1.640821 .0458542 -35.78 0.000 -1.730694 -1.550949 ------------------------------------------------------------------------------ . estimates store poisiid . . * Poisson with heteroskedastic-robust standard errors (Table 24.6 column 3) . poisson PHARVIS $XLISTPOISSON, robust Iteration 0: log pseudo-likelihood = -26309.924 Iteration 1: log pseudo-likelihood = -25300.337 Iteration 2: log pseudo-likelihood = -25281.839 Iteration 3: log pseudo-likelihood = -25281.786 Iteration 4: log pseudo-likelihood = -25281.786 Poisson regression Number of obs = 27765 Wald chi2(10) = 2423.07 Prob > chi2 = 0.0000 Log pseudo-likelihood = -25281.786 Pseudo R2 = 0.2073 ------------------------------------------------------------------------------ | Robust PHARVIS | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- LNHHEXP | .078686 .0255091 3.08 0.002 .0286891 .1286829 INSURANCE | -.2485716 .0437892 -5.68 0.000 -.3343969 -.1627464 SEX | .0851733 .030907 2.76 0.006 .0245967 .1457499 AGE | .0252426 .0198448 1.27 0.203 -.0136526 .0641377 MARRIED | .1239639 .0419107 2.96 0.003 .0418205 .2061073 ILLDAYS | .0429083 .0028779 14.91 0.000 .0372678 .0485488 ACTDAYS | .0089793 .0207444 0.43 0.665 -.031679 .0496377 INJURY | .1717029 .2043534 0.84 0.401 -.2288224 .5722282 ILLNESS | .5623976 .0228635 24.60 0.000 .517586 .6072092 EDUC | -.0524459 .0081043 -6.47 0.000 -.0683301 -.0365618 _cons | -1.640821 .0872497 -18.81 0.000 -1.811828 -1.469815 ------------------------------------------------------------------------------ . estimates store poishet . . * Poisson with cluster-robust standard errors (Table 24.6 column 4) . poisson PHARVIS $XLISTPOISSON, cluster(COMMUNE) Iteration 0: log pseudo-likelihood = -26309.924 Iteration 1: log pseudo-likelihood = -25300.337 Iteration 2: log pseudo-likelihood = -25281.839 Iteration 3: log pseudo-likelihood = -25281.786 Iteration 4: log pseudo-likelihood = -25281.786 Poisson regression Number of obs = 27765 Wald chi2(10) = 1295.38 Log pseudo-likelihood = -25281.786 Prob > chi2 = 0.0000 (standard errors adjusted for clustering on COMMUNE) ------------------------------------------------------------------------------ | Robust PHARVIS | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- LNHHEXP | .078686 .0472052 1.67 0.096 -.0138344 .1712065 INSURANCE | -.2485716 .0617873 -4.02 0.000 -.3696725 -.1274708 SEX | .0851733 .0327427 2.60 0.009 .0209988 .1493478 AGE | .0252426 .0262626 0.96 0.336 -.0262311 .0767163 MARRIED | .1239639 .048607 2.55 0.011 .028696 .2192318 ILLDAYS | .0429083 .0037384 11.48 0.000 .0355811 .0502355 ACTDAYS | .0089793 .0190493 0.47 0.637 -.0283567 .0463154 INJURY | .1717029 .2214258 0.78 0.438 -.2622836 .6056894 ILLNESS | .5623976 .028512 19.72 0.000 .506515 .6182802 EDUC | -.0524459 .0153841 -3.41 0.001 -.0825982 -.0222937 _cons | -1.640821 .1541108 -10.65 0.000 -1.942873 -1.33877 ------------------------------------------------------------------------------ . estimates store poisclust . . * Random effects estimation (Table 24.6 columns 5-6) . * This uses the xtpois command which first requires identifying the cluster . iis COMMUNE . xtpois PHARVIS $XLISTPOISSON, re Fitting Poisson model: Iteration 0: log likelihood = -26309.924 Iteration 1: log likelihood = -25300.337 Iteration 2: log likelihood = -25281.839 Iteration 3: log likelihood = -25281.786 Iteration 4: log likelihood = -25281.786 Fitting full model: Iteration 0: log likelihood = -23538.342 Iteration 1: log likelihood = -23430.615 Iteration 2: log likelihood = -23419.142 Iteration 3: log likelihood = -23419.132 Iteration 4: log likelihood = -23419.132 Random-effects Poisson regression Number of obs = 27765 Group variable (i): COMMUNE Number of groups = 194 Random effects u_i ~ Gamma Obs per group: min = 51 avg = 143.1 max = 206 Wald chi2(10) = 13723.01 Log likelihood = -23419.132 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ PHARVIS | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- LNHHEXP | -.1013746 .0187549 -5.41 0.000 -.1381336 -.0646157 INSURANCE | -.1675953 .0273642 -6.12 0.000 -.2212283 -.1139624 SEX | .099303 .0172541 5.76 0.000 .0654855 .1331206 AGE | .0047406 .0107899 0.44 0.660 -.0164073 .0258884 MARRIED | .1579958 .0212825 7.42 0.000 .1162828 .1997088 ILLDAYS | .046055 .0011422 40.32 0.000 .0438164 .0482937 ACTDAYS | .0186084 .0054546 3.41 0.001 .0079176 .0292991 INJURY | .1479464 .0780863 1.89 0.058 -.0051 .3009928 ILLNESS | .5801872 .0076855 75.49 0.000 .565124 .5952505 EDUC | -.0284493 .0055827 -5.10 0.000 -.0393911 -.0175075 _cons | -1.276974 .0723199 -17.66 0.000 -1.418718 -1.135229 -------------+---------------------------------------------------------------- /lnalpha | -1.039839 .1035295 -1.242753 -.836925 -------------+---------------------------------------------------------------- alpha | .3535115 .0365989 .2885885 .4330401 ------------------------------------------------------------------------------ Likelihood-ratio test of alpha=0: chibar2(01) = 3725.31 Prob>=chibar2 = 0.000 . estimates store poisre . . * Following shows that cluster option for xtpois in Stata version does nothing . xtpois PHARVIS $XLISTPOISSON, i(COMMUNE) re Fitting Poisson model: Iteration 0: log likelihood = -26309.924 Iteration 1: log likelihood = -25300.337 Iteration 2: log likelihood = -25281.839 Iteration 3: log likelihood = -25281.786 Iteration 4: log likelihood = -25281.786 Fitting full model: Iteration 0: log likelihood = -23538.342 Iteration 1: log likelihood = -23430.615 Iteration 2: log likelihood = -23419.142 Iteration 3: log likelihood = -23419.132 Iteration 4: log likelihood = -23419.132 Random-effects Poisson regression Number of obs = 27765 Group variable (i): COMMUNE Number of groups = 194 Random effects u_i ~ Gamma Obs per group: min = 51 avg = 143.1 max = 206 Wald chi2(10) = 13723.01 Log likelihood = -23419.132 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ PHARVIS | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- LNHHEXP | -.1013746 .0187549 -5.41 0.000 -.1381336 -.0646157 INSURANCE | -.1675953 .0273642 -6.12 0.000 -.2212283 -.1139624 SEX | .099303 .0172541 5.76 0.000 .0654855 .1331206 AGE | .0047406 .0107899 0.44 0.660 -.0164073 .0258884 MARRIED | .1579958 .0212825 7.42 0.000 .1162828 .1997088 ILLDAYS | .046055 .0011422 40.32 0.000 .0438164 .0482937 ACTDAYS | .0186084 .0054546 3.41 0.001 .0079176 .0292991 INJURY | .1479464 .0780863 1.89 0.058 -.0051 .3009928 ILLNESS | .5801872 .0076855 75.49 0.000 .565124 .5952505 EDUC | -.0284493 .0055827 -5.10 0.000 -.0393911 -.0175075 _cons | -1.276974 .0723199 -17.66 0.000 -1.418718 -1.135229 -------------+---------------------------------------------------------------- /lnalpha | -1.039839 .1035295 -1.242753 -.836925 -------------+---------------------------------------------------------------- alpha | .3535115 .0365989 .2885885 .4330401 ------------------------------------------------------------------------------ Likelihood-ratio test of alpha=0: chibar2(01) = 3725.31 Prob>=chibar2 = 0.000 . . * Note that can cluster bootstrap if desired to get more robust standard errors . * This is done at end of program . . * Fixed effects estimation (FGLS) (Table 24.6 columns 7-8) . xtpois PHARVIS $XLISTPOISSON, fe note: 1 group (94 obs) dropped due to all zero outcomes Iteration 0: log likelihood = -28435.61 Iteration 1: log likelihood = -24231.502 Iteration 2: log likelihood = -22468.078 Iteration 3: log likelihood = -22446.225 Iteration 4: log likelihood = -22446.002 Iteration 5: log likelihood = -22446.002 Conditional fixed-effects Poisson regression Number of obs = 27671 Group variable (i): COMMUNE Number of groups = 193 Obs per group: min = 51 avg = 143.4 max = 206 Wald chi2(10) = 13621.76 Log likelihood = -22446.002 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ PHARVIS | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- LNHHEXP | -.1146402 .019025 -6.03 0.000 -.1519285 -.0773519 INSURANCE | -.163603 .0274193 -5.97 0.000 -.2173438 -.1098622 SEX | .0997415 .0172564 5.78 0.000 .0659195 .1335635 AGE | .0033591 .0107945 0.31 0.756 -.0177977 .024516 MARRIED | .1606792 .0212958 7.55 0.000 .1189403 .2024182 ILLDAYS | .046148 .0011453 40.29 0.000 .0439032 .0483929 ACTDAYS | .0189184 .0054666 3.46 0.001 .008204 .0296328 INJURY | .1479319 .078183 1.89 0.058 -.0053039 .3011677 ILLNESS | .5803719 .0077289 75.09 0.000 .5652235 .5955203 EDUC | -.0272099 .0056191 -4.84 0.000 -.0382232 -.0161966 ------------------------------------------------------------------------------ . estimates store poisfe . . * Note that can cluster bootstrap if desired to get more robust standard errors . * This is done at end of program . . ********** DISPLAY TABLE 24.6 RESULTS page 852 ********** . . * The results here differ in the second significant digit from those in text . * despite same sample size. Not sure why. . . estimates table poisiid poishet poisclust, /* > */ b(%10.3f) t(%10.2f) stats(r2 N) ----------------------------------------------------- Variable | poisiid poishet poisclust -------------+--------------------------------------- LNHHEXP | 0.079 0.079 0.079 | 5.68 3.08 1.67 INSURANCE | -0.249 -0.249 -0.249 | -9.57 -5.68 -4.02 SEX | 0.085 0.085 0.085 | 4.96 2.76 2.60 AGE | 0.025 0.025 0.025 | 2.38 1.27 0.96 MARRIED | 0.124 0.124 0.124 | 5.92 2.96 2.55 ILLDAYS | 0.043 0.043 0.043 | 40.00 14.91 11.48 ACTDAYS | 0.009 0.009 0.009 | 1.71 0.43 0.47 INJURY | 0.172 0.172 0.172 | 2.30 0.84 0.78 ILLNESS | 0.562 0.562 0.562 | 87.15 24.60 19.72 EDUC | -0.052 -0.052 -0.052 | -10.89 -6.47 -3.41 _cons | -1.641 -1.641 -1.641 | -35.78 -18.81 -10.65 -------------+--------------------------------------- r2 | N | 27765.000 27765.000 27765.000 ----------------------------------------------------- legend: b/t . estimates table poisre poisfe, /* > */ b(%10.3f) t(%10.2f) stats(r2 N) ---------------------------------------- Variable | poisre poisfe -------------+-------------------------- PHARVIS | LNHHEXP | -0.101 -0.115 | -5.41 -6.03 INSURANCE | -0.168 -0.164 | -6.12 -5.97 SEX | 0.099 0.100 | 5.76 5.78 AGE | 0.005 0.003 | 0.44 0.31 MARRIED | 0.158 0.161 | 7.42 7.55 ILLDAYS | 0.046 0.046 | 40.32 40.29 ACTDAYS | 0.019 0.019 | 3.41 3.46 INJURY | 0.148 0.148 | 1.89 1.89 ILLNESS | 0.580 0.580 | 75.49 75.09 EDUC | -0.028 -0.027 | -5.10 -4.84 _cons | -1.277 | -17.66 -------------+-------------------------- lnalpha | _cons | -1.040 | -10.04 -------------+-------------------------- Statistics | r2 | N | 27765.000 27671.000 ---------------------------------------- legend: b/t . . ********** ADDITIONALLY DO CLUSTER BOOTSTRAPS ********** . . * These results not given in the text . . * Output at website uses breps 500 . global breps 50 . . * Note that can bootstrap if desired to get more robust standard errors . * The first reproduces pois , cluster(COMMUNE) . bootstrap "poisson PHARVIS $XLISTPOISSON" _b, cluster(COMMUNE) reps($breps) level(95) command: poisson PHARVIS LNHHEXP INSURANCE SEX AGE MARRIED ILLDAYS ACTDAYS INJURY ILLNESS EDUC statistics: b_LNHHEXP = [PHARVIS]_b[LNHHEXP] b_INSURA~E = [PHARVIS]_b[INSURANCE] b_SEX = [PHARVIS]_b[SEX] b_AGE = [PHARVIS]_b[AGE] b_MARRIED = [PHARVIS]_b[MARRIED] b_ILLDAYS = [PHARVIS]_b[ILLDAYS] b_ACTDAYS = [PHARVIS]_b[ACTDAYS] b_INJURY = [PHARVIS]_b[INJURY] b_ILLNESS = [PHARVIS]_b[ILLNESS] b_EDUC = [PHARVIS]_b[EDUC] b_cons = [PHARVIS]_b[_cons] Bootstrap statistics Number of obs = 27765 N of clusters = 194 Replications = 50 ------------------------------------------------------------------------------ Variable | Reps Observed Bias Std. Err. [95% Conf. Interval] -------------+---------------------------------------------------------------- b_LNHHEXP | 50 .078686 .0072233 .0475425 -.0168542 .1742262 (N) | -.0050689 .1878158 (P) | -.0204097 .1710779 (BC) b_INSURANCE | 50 -.2485716 .0013929 .0770506 -.4034107 -.0937326 (N) | -.3640907 -.1004183 (P) | -.4677969 -.1004183 (BC) b_SEX | 50 .0851733 -.0039062 .0345537 .0157351 .1546115 (N) | .022333 .1494552 (P) | .022333 .1494552 (BC) b_AGE | 50 .0252426 .0012812 .0270715 -.0291596 .0796447 (N) | -.025843 .0726057 (P) | -.0479862 .0726057 (BC) b_MARRIED | 50 .1239639 -.0017894 .0406114 .0423522 .2055756 (N) | .0132484 .2024732 (P) | .0132484 .2101617 (BC) b_ILLDAYS | 50 .0429083 -.0005122 .0034 .0360757 .0497409 (N) | .0358535 .0481521 (P) | .0363203 .0500312 (BC) b_ACTDAYS | 50 .0089793 -.0021093 .0249974 -.0412549 .0592135 (N) | -.0343906 .0573651 (P) | -.0352626 .0573651 (BC) b_INJURY | 50 .1717029 -.0321969 .2090263 -.2483512 .591757 (N) | -.3271621 .4807015 (P) | -.1896703 .648314 (BC) b_ILLNESS | 50 .5623976 .0061368 .0294736 .5031682 .621627 (N) | .5206931 .6271017 (P) | .5192547 .6206369 (BC) b_EDUC | 50 -.0524459 .0027244 .01598 -.0845589 -.0203329 (N) | -.0825952 -.017323 (P) | -.0850821 -.0256777 (BC) b_cons | 50 -1.640821 -.0414073 .1460702 -1.93436 -1.347282 (N) | -1.984352 -1.399226 (P) | -1.867373 -1.310915 (BC) ------------------------------------------------------------------------------ Note: N = normal P = percentile BC = bias-corrected . * The t-statistic vector is e(b)./e(se) where ./ is elt. by elt. division . * But Stata Version 8 does not do ./ so instead need the following . matrix tpois = (vecdiag(diag(e(b))*syminv(diag(e(se)))))' . matrix list tpois, format(%10.2f) tpois[11,1] r1 b_LNHHEXP 1.66 b_INSURANCE -3.23 b_SEX 2.46 b_AGE 0.93 b_MARRIED 3.05 b_ILLDAYS 12.62 b_ACTDAYS 0.36 b_INJURY 0.82 b_ILLNESS 19.08 b_EDUC -3.28 b_cons -11.23 . . * The next two reproduce xtpois , cluster(COMMUNE) . * but xtpois has no cluster option so instead cluster boostrap . . * Fixed effects estimator . bootstrap "xtpois PHARVIS $XLISTPOISSON, fe" _b, cluster(COMMUNE) reps($breps) level(95) command: xtpois PHARVIS LNHHEXP INSURANCE SEX AGE MARRIED ILLDAYS ACTDAYS INJURY ILLNESS EDUC , > fe statistics: b_LNHHEXP = [PHARVIS]_b[LNHHEXP] b_INSURA~E = [PHARVIS]_b[INSURANCE] b_SEX = [PHARVIS]_b[SEX] b_AGE = [PHARVIS]_b[AGE] b_MARRIED = [PHARVIS]_b[MARRIED] b_ILLDAYS = [PHARVIS]_b[ILLDAYS] b_ACTDAYS = [PHARVIS]_b[ACTDAYS] b_INJURY = [PHARVIS]_b[INJURY] b_ILLNESS = [PHARVIS]_b[ILLNESS] b_EDUC = [PHARVIS]_b[EDUC] Bootstrap statistics Number of obs = 27671 N of clusters = 193 Replications = 50 ------------------------------------------------------------------------------ Variable | Reps Observed Bias Std. Err. [95% Conf. Interval] -------------+---------------------------------------------------------------- b_LNHHEXP | 50 -.1146402 .0046925 .042981 -.2010138 -.0282666 (N) | -.1801919 -.0258064 (P) | -.1841975 -.043704 (BC) b_INSURANCE | 50 -.163603 .0145077 .0513299 -.2667543 -.0604516 (N) | -.2391983 -.0581847 (P) | -.269962 -.0993868 (BC) b_SEX | 50 .0997415 .0030381 .0298361 .0397836 .1596994 (N) | .0581716 .1630876 (P) | .055771 .1562326 (BC) b_AGE | 50 .0033591 -.0017336 .0228288 -.042517 .0492353 (N) | -.0508069 .040935 (P) | -.0508069 .0541492 (BC) b_MARRIED | 50 .1606793 .009603 .0435503 .0731616 .2481969 (N) | .1091381 .260388 (P) | .0877519 .2407327 (BC) b_ILLDAYS | 50 .046148 -.0004107 .0027904 .0405406 .0517555 (N) | .0397139 .0504146 (P) | .0397139 .050898 (BC) b_ACTDAYS | 50 .0189184 -.0049228 .0176306 -.0165115 .0543484 (N) | -.0169987 .0490534 (P) | -.0158923 .0497731 (BC) b_INJURY | 50 .1479319 .0204617 .2194316 -.2930323 .5888962 (N) | -.2735089 .5520838 (P) | -.3044733 .5520838 (BC) b_ILLNESS | 50 .5803719 .0003675 .0199171 .540347 .6203969 (N) | .5370637 .6163648 (P) | .5370637 .6163648 (BC) b_EDUC | 50 -.0272099 -.0003993 .0112987 -.0499155 -.0045043 (N) | -.0521668 -.0068456 (P) | -.0531845 -.0068456 (BC) ------------------------------------------------------------------------------ Note: N = normal P = percentile BC = bias-corrected . matrix tpoisfe = (vecdiag(diag(e(b))*syminv(diag(e(se)))))' . matrix list tpoisfe, format(%10.2f) tpoisfe[10,1] r1 b_LNHHEXP -2.67 b_INSURANCE -3.19 b_SEX 3.34 b_AGE 0.15 b_MARRIED 3.69 b_ILLDAYS 16.54 b_ACTDAYS 1.07 b_INJURY 0.67 b_ILLNESS 29.14 b_EDUC -2.41 . . * Random effects estimator . bootstrap "xtpois PHARVIS $XLISTPOISSON, re" _b, cluster(COMMUNE) reps($breps) level(95) command: xtpois PHARVIS LNHHEXP INSURANCE SEX AGE MARRIED ILLDAYS ACTDAYS INJURY ILLNESS EDUC , > re statistics: b_LNHHEXP = [PHARVIS]_b[LNHHEXP] b_INSURA~E = [PHARVIS]_b[INSURANCE] b_SEX = [PHARVIS]_b[SEX] b_AGE = [PHARVIS]_b[AGE] b_MARRIED = [PHARVIS]_b[MARRIED] b_ILLDAYS = [PHARVIS]_b[ILLDAYS] b_ACTDAYS = [PHARVIS]_b[ACTDAYS] b_INJURY = [PHARVIS]_b[INJURY] b_ILLNESS = [PHARVIS]_b[ILLNESS] b_EDUC = [PHARVIS]_b[EDUC] b_cons = [PHARVIS]_b[_cons] b_1cons = [lnalpha]_b[_cons] Bootstrap statistics Number of obs = 27765 N of clusters = 194 Replications = 50 ------------------------------------------------------------------------------ Variable | Reps Observed Bias Std. Err. [95% Conf. Interval] -------------+---------------------------------------------------------------- b_LNHHEXP | 50 -.1013746 .0038095 .0406385 -.1830407 -.0197086 (N) | -.1794194 -.0319058 (P) | -.1977448 -.0319058 (BC) b_INSURANCE | 50 -.1675954 -.0053195 .04945 -.2669688 -.0682219 (N) | -.2912881 -.0900193 (P) | -.2677689 -.088337 (BC) b_SEX | 50 .099303 -.0008622 .032962 .0330634 .1655427 (N) | .0463968 .1569125 (P) | .0463968 .1569125 (BC) b_AGE | 50 .0047406 -.002087 .0196285 -.0347045 .0441856 (N) | -.0319554 .0398893 (P) | -.0212454 .0454795 (BC) b_MARRIED | 50 .1579958 .0045701 .0386327 .0803604 .2356311 (N) | .1002202 .2446688 (P) | .0595091 .2383231 (BC) b_ILLDAYS | 50 .046055 -.0000891 .0033445 .039334 .0527761 (N) | .0400018 .0525925 (P) | .0400018 .0528012 (BC) b_ACTDAYS | 50 .0186084 -.0013996 .0204209 -.022429 .0596457 (N) | -.0251694 .0533912 (P) | -.0251694 .0624974 (BC) b_INJURY | 50 .1479464 -.0122248 .2130704 -.2802346 .5761274 (N) | -.2971589 .4662884 (P) | -.3564237 .4662884 (BC) b_ILLNESS | 50 .5801873 .002013 .019375 .5412517 .6191228 (N) | .5488635 .621733 (P) | .5488635 .6328769 (BC) b_EDUC | 50 -.0284493 -.0017922 .0117021 -.0519655 -.0049331 (N) | -.050308 -.0116823 (P) | -.050308 -.0065941 (BC) b_cons | 50 -1.276974 -.0036143 .1309168 -1.540061 -1.013887 (N) | -1.523902 -.9686469 (P) | -1.523902 -.9686469 (BC) b_1cons | 50 -1.039839 .0148765 .0966908 -1.234147 -.8455317 (N) | -1.170977 -.8494586 (P) | -1.183111 -.8494586 (BC) ------------------------------------------------------------------------------ Note: N = normal P = percentile BC = bias-corrected . matrix tpoisre = (vecdiag(diag(e(b))*syminv(diag(e(se)))))' . matrix list tpoisre, format(%10.2f) tpoisre[12,1] r1 b_LNHHEXP -2.49 b_INSURANCE -3.39 b_SEX 3.01 b_AGE 0.24 b_MARRIED 4.09 b_ILLDAYS 13.77 b_ACTDAYS 0.91 b_INJURY 0.69 b_ILLNESS 29.95 b_EDUC -2.43 b_cons -9.75 b_1cons -10.75 . . ********** CLOSE OUTPUT ********** . log close log: c:\Imbook\bwebpage\Section6\mma24p2poiscluster.txt log type: text closed on: 24 May 2005, 16:50:38 ----------------------------------------------------------------------------------------------------