An Analysis on Danish Micro Data - School of Economics and ...

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age | -.0120558 .0242528 -0.50 0.619 -.0595904 .0354789 ab02 | .0075541 .2192776 0.03 0.973 -.4222221 .4373302 ab36 | -.1988077 .1784342 -1.11 0.265 -.5485324 .150917 ab79 | -.1690557 .1539746 -1.10 0.272 -.4708404 .132729 ab1014 | -.1864877 .1332758 -1.40 0.162 -.4477034 .074728 single | -.2984545 .1011123 -2.95 0.003 -.496631 -.100278 ieland1 | .4358014 .2263498 1.93 0.054 -.007836 .8794388 ieland2 | .7428362 .3343576 2.22 0.026 .0875074 1.398165 short | .1645215 .0891585 1.85 0.065 -.010226 .339269 higher | -.2999522 .1700744 -1.76 0.078 -.6332919 .0333874 agesq | -.0001133 .0002723 -0.42 0.677 -.000647 .0004203 useofmedicin | -.0002798 .0000672 -4.16 0.000 -.0004116 -.000148 u | -.0439467 .0356813 -1.23 0.218 -.1138807 .0259874 y99 | .1285279 .1379968 0.93 0.352 -.1419408 .3989967 y00 | .0538564 .1390792 0.39 0.699 -.2187339 .3264467 y02 | -.0346478 .1361316 -0.25 0.799 -.3014608 .2321651 y03 | .0239295 .1401969 0.17 0.864 -.2508515 .2987104 _cons | -.3463452 .6287682 -0.55 0.582 -1.578708 .8860178 ------------------------------------------------------------------------------ note: 0 failures and 32 successes completely determined. . . lrtest A ., force likelihood-ratio test LR chi2(1) = 0.02 (Assumption: . nested in A) Prob > chi2 = 0.9018 . . mfx Marginal effects after probit y = Pr(emp) (predict) = .6489114 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- antide~s | -.0178148 .01911 -0.93 0.351 -.055261 .019631 .26819 mtx | .0001394 .00015 0.93 0.353 -.000155 .000434 202.322 wageinc | 6.29e-06 .00000 30.94 0.000 5.9e-06 6.7e-06 100005 age | -.0044705 .00899 -0.50 0.619 -.022092 .013151 50.2382 ab02*| .0027973 .08109 0.03 0.972 -.156131 .161725 .024112 ab36*| -.0758552 .06965 -1.09 0.276 -.21236 .060649 .052876 ab79*| -.0642299 .05965 -1.08 0.282 -.181138 .052679 .06599 ab1014*| -.0707722 .05149 -1.37 0.169 -.171687 .030142 .12225 single*| -.1130728 .03918 -2.89 0.004 -.189867 -.036279 .260152 ieland1*| .1697045 .09 1.89 0.059 -.006692 .346101 .959814 ieland2*| .2234253 .07418 3.01 0.003 .078029 .368822 .021997 short*| .0604672 .03253 1.86 0.063 -.003289 .124223 .376904 higher*| -.1146676 .06674 -1.72 0.086 -.245478 .016143 .158629 agesq | -.000042 .0001 -0.42 0.678 -.00024 .000156 2630.49 useofm~n | -.0001038 .00003 -4.04 0.000 -.000154 -.000053 870.992 u | -.0162962 .01323 -1.23 0.218 -.042227 .009635 5.4599 y99*| .0467927 .04946 0.95 0.344 -.050142 .143727 .15736 y00*| .0198382 .05092 0.39 0.697 -.079964 .11964 .18824 y02*| -.0128951 .05081 -0.25 0.800 -.112473 .086683 .217005 y03*| .0088524 .05177 0.17 0.864 -.092613 .110318 .245347 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 133
Probit yr 2003 – Table 9: . use /akf/702517/ycb2517/Initial/finaldata3.dta . /*Including the variable indicating the doses of antidep taken last 5 years, > year 2003*/ . quietly reg cons_antidep mtx wageinc age ab02 ab36 ab79 ab1014 single ieland1 > ieland2 short higher agesq useofmedicin u if yr==2003 . . predict ohat, resid . . probit emp cons_antidep mtx wageinc age ab02 ab36 ab79 ab1014 single ieland1 > ieland2 short higher agesq useofmedicin u ohat if yr==2003, robust note: ab02 dropped due to collinearity Iteration 0: log pseudolikelihood = -402.02192 Iteration 1: log pseudolikelihood = -185.75636 Iteration 2: log pseudolikelihood = -129.61051 Iteration 3: log pseudolikelihood = -115.26218 Iteration 4: log pseudolikelihood = -112.32902 Iteration 5: log pseudolikelihood = -112.12227 Iteration 6: log pseudolikelihood = -112.12104 Probit estimates Number of obs = 580 Wald chi2(16) = 163.36 Prob > chi2 = 0.0000 Log pseudolikelihood = -112.12104 Pseudo R2 = 0.7211 ------------------------------------------------------------------------------ | Robust emp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- cons_antidep | -.0076847 .0054205 -1.42 0.156 -.0183087 .0029394 mtx | .0024686 .0016833 1.47 0.143 -.0008306 .0057678 wageinc | .0000172 1.97e-06 8.72 0.000 .0000133 .000021 age | -.0867949 .0574685 -1.51 0.131 -.1994312 .0258414 ab36 | .0929413 .355177 0.26 0.794 -.6031929 .7890756 ab79 | .7896308 .5021054 1.57 0.116 -.1944776 1.773739 ab1014 | .3000002 .2994794 1.00 0.316 -.2869687 .886969 single | .3710854 .3684268 1.01 0.314 -.3510179 1.093189 ieland1 | .8610399 .5817527 1.48 0.139 -.2791745 2.001254 ieland2 | .8080762 .7046116 1.15 0.251 -.5729371 2.189089 short | .1514142 .2144752 0.71 0.480 -.2689494 .5717779 higher | .2403415 .2621278 0.92 0.359 -.2734195 .7541025 agesq | .0007675 .0006635 1.16 0.247 -.0005329 .0020679 useofmedicin | .0000883 .0002008 0.44 0.660 -.0003053 .0004819 u | -.280949 .1426478 -1.97 0.049 -.5605336 -.0013645 ohat | .0076396 .0054188 1.41 0.159 -.0029811 .0182602 _cons | 1.720691 1.528157 1.13 0.260 -1.274442 4.715824 ------------------------------------------------------------------------------ note: 0 failures and 14 successes completely determined. . . mfx 134
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Estimating the Impact of An
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Abstract This thesis aims at analys
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1 Introduction According to the Wor
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consisting of lack of data on the d
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experience symptoms of depression o
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(SSRI’s), also called the new ant
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heumatologists. But since descripti
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H > 0 or a corner solution, H = 0 .
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employers more reluctant to hire ne
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One of the most cited models when a
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One of the factors greatly influenc
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Galarraga et al. (2006) have chosen
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channel runs through enhanced compl
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3.2 The effect of antidepressants o
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of patients receiving appropriate c
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Inspired by Galarraga et al. (2006)
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are used by other patients groups,
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Consequently I am dealing with a se
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constructed, and will be explained
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As mentioned in chapter 1, one of t
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Number of observations 2364 252 211
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is perhaps not surprising, since th
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Figure 7 The distribution of the us
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Figure 9 depicts the defined daily
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where φ ( z) is the standard norma
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5.3 The binary response model and p
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The log of equation [5.7] gives the
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antidepressant use are displayed in
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As established in chapter 4, the pa
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probit estimation, respectively. Ha
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estimation. And ag
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As can be seen from table 7 the ext
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mtx 0.0002 0.0003 0.0001 0.0002 wag
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probit estimation produces more sig
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health. As a consequence, further a
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8 Conclusion The aim of this thesis
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References Agerbo, E., T. Eriksson,
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Zimmerman, F. and W. Katon. 2005. S
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wage2 Dummy=1 if wage income up to
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data medold; set b.lmdb_pct10_2003;
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if Last.aar then output; /*output o
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KEEP AAR ALDER ANC02 ANC36 ANC79 AN
Page 85 and 86: set b.register; yr=0+aar; DROP aar;
Page 87 and 88: HIGHER='Middle-range and higher edu
Page 89 and 90: dayemp4='Between 8 months and 364 d
Page 91 and 92: data collect; merge test1 (in=a) te
Page 93 and 94: data test1; set b.finaldata1; proc
Page 95 and 96: antidep_lastyr=lag(AD_dummy); if (u
Page 97 and 98: Estimation results Probit yr 2003 -
Page 99 and 100: Probit estimates Number of obs = 58
Page 101 and 102: single | -.3439072 .0893125 -3.85 0
Page 103 and 104: (Assumption: . nested in A) Prob >
Page 105 and 106: . /*with proxy, generalised residua
Page 107 and 108: . . /*Panel probit same as Galarrag
Page 109 and 110: Iteration 7: log pseudolikelihood =
Page 111 and 112: -----------------------------------
Page 113 and 114: . . probit emp antidep mtx wageinc
Page 115 and 116: ho = 0.2 log likelihood = -681.4270
Page 117 and 118: ab79 | -.2830833 .3452789 -0.82 0.4
Page 119 and 120: -----------------------------------
Page 121 and 122: . predict ohat, resid . . probit em
Page 123 and 124: y99 | -.1676129 .4540083 -0.37 0.71
Page 125 and 126: ab02*| -.0132912 .01201 -1.11 0.269
Page 127 and 128: . gen numerator=normdenxb*[ad_dummy
Page 129 and 130: Iteration 2: log likelihood = -306.
Page 131 and 132: . . probit emp antidep_last5yrs mtx
Page 133 and 134: note: 0 failures and 14 successes c
Page 135: y = Pr(emp) (predict) = .64904538 -
Page 139 and 140: higher | .0160771 .2154333 0.07 0.9
Page 141 and 142: ieland1*| .2476048 .11071 2.24 0.02
Page 143 and 144: Random-effects tobit regression Num
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