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

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. . probit emp antidep_last5yrs mtx wageinc age ab02 ab36 ab79 ab1014 single iela > nd1 ieland2 short higher agesq useofmedicin u res y96 y97 y98 y99 y00 y01 y02 > y03, robust note: y96 dropped due to collinearity note: y97 dropped due to collinearity note: y98 dropped due to collinearity note: y01 dropped due to collinearity Iteration 0: log pseudolikelihood = -1632.6254 Iteration 1: log pseudolikelihood = -780.17223 Iteration 2: log pseudolikelihood = -591.79871 Iteration 3: log pseudolikelihood = -549.88817 Iteration 4: log pseudolikelihood = -544.17003 Iteration 5: log pseudolikelihood = -544.01651 Iteration 6: log pseudolikelihood = -544.01638 Probit estimates Number of obs = 2364 Wald chi2(21) = 548.96 Prob > chi2 = 0.0000 Log pseudolikelihood = -544.01638 Pseudo R2 = 0.6668 ------------------------------------------------------------------------------ | Robust emp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- antidep_la~s | -.038805 .0831002 -0.47 0.641 -.2016784 .1240685 mtx | .0003782 .0004065 0.93 0.352 -.0004185 .0011748 wageinc | .000017 8.73e-07 19.45 0.000 .0000153 .0000187 age | -.0121945 .0243586 -0.50 0.617 -.0599364 .0355474 ab02 | .0101185 .2210211 0.05 0.963 -.4230749 .443312 ab36 | -.1985662 .1783138 -1.11 0.265 -.5480549 .1509224 ab79 | -.1682852 .1543473 -1.09 0.276 -.4708004 .1342299 ab1014 | -.185865 .1334226 -1.39 0.164 -.4473685 .0756385 single | -.2989174 .1012035 -2.95 0.003 -.4972725 -.1005623 ieland1 | .4371032 .2264545 1.93 0.054 -.0067395 .8809459 ieland2 | .7450443 .3350058 2.22 0.026 .0884449 1.401644 short | .1646846 .0891777 1.85 0.065 -.0101005 .3394696 higher | -.2998816 .1700148 -1.76 0.078 -.6331045 .0333413 agesq | -.0001118 .0002734 -0.41 0.683 -.0006477 .0004241 useofmedicin | -.0002817 .0000692 -4.07 0.000 -.0004173 -.000146 u | -.0439666 .0356801 -1.23 0.218 -.1138983 .0259652 res | -.016858 .1438248 -0.12 0.907 -.2987495 .2650335 y99 | .1295391 .1379416 0.94 0.348 -.1408216 .3998998 y00 | .0546632 .1392639 0.39 0.695 -.2182891 .3276154 y02 | -.0352147 .1363748 -0.26 0.796 -.3025043 .2320749 y03 | .0233268 .140449 0.17 0.868 -.2519481 .2986018 _cons | -.3601546 .6301611 -0.57 0.568 -1.595248 .8749384 ------------------------------------------------------------------------------ note: 0 failures and 32 successes completely determined. . . est store A . . mfx Marginal effects after probit 131
y = Pr(emp) (predict) = .64904538 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- antide~s | -.0143875 .03086 -0.47 0.641 -.074863 .046087 .26819 mtx | .0001402 .00015 0.93 0.351 -.000154 .000435 202.322 wageinc | 6.30e-06 .00000 30.47 0.000 5.9e-06 6.7e-06 100005 age | -.0045213 .00903 -0.50 0.617 -.022216 .013173 50.2382 ab02*| .0037446 .08164 0.05 0.963 -.156271 .16376 .024112 ab36*| -.075753 .06959 -1.09 0.276 -.212147 .060641 .052876 ab79*| -.0639241 .05978 -1.07 0.285 -.181097 .053249 .06599 ab1014*| -.0705234 .05154 -1.37 0.171 -.171537 .03049 .12225 single*| -.1132383 .03921 -2.89 0.004 -.190098 -.036379 .260152 ieland1*| .170211 .09004 1.89 0.059 -.006255 .346677 .959814 ieland2*| .2238517 .0741 3.02 0.003 .078609 .369094 .021997 short*| .0605177 .03254 1.86 0.063 -.003252 .124287 .376904 higher*| -.1146285 .06671 -1.72 0.086 -.245375 .016118 .158629 agesq | -.0000415 .0001 -0.41 0.683 -.00024 .000157 2630.49 useofm~n | -.0001044 .00003 -3.97 0.000 -.000156 -.000053 870.992 u | -.0163013 .01323 -1.23 0.218 -.042225 .009622 5.4599 res | -.0062504 .05332 -0.12 0.907 -.110752 .098251 -.803911 y99*| .0471462 .04941 0.95 0.340 -.04969 .143982 .15736 y00*| .0201304 .05097 0.39 0.693 -.079762 .120023 .18824 y02*| -.013105 .05089 -0.26 0.797 -.112856 .086646 .217005 y03*| .0086288 .05186 0.17 0.868 -.093016 .110273 .245347 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 . . /*Panel probit same as Galarraga - with proxy*/ . probit emp antidep_last5yrs mtx wageinc age ab02 ab36 ab79 ab1014 single iela > nd1 ieland2 short higher agesq useofmedicin u y96 y97 y98 y99 y00 y01 y02 y03 > , robust note: y96 dropped due to collinearity note: y97 dropped due to collinearity note: y98 dropped due to collinearity note: y01 dropped due to collinearity Iteration 0: log pseudolikelihood = -1632.6254 Iteration 1: log pseudolikelihood = -780.26576 Iteration 2: log pseudolikelihood = -591.79572 Iteration 3: log pseudolikelihood = -549.87515 Iteration 4: log pseudolikelihood = -544.17593 Iteration 5: log pseudolikelihood = -544.02411 Iteration 6: log pseudolikelihood = -544.02399 Probit estimates Number of obs = 2364 Wald chi2(20) = 545.99 Prob > chi2 = 0.0000 Log pseudolikelihood = -544.02399 Pseudo R2 = 0.6668 ------------------------------------------------------------------------------ | Robust emp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- antidep_la~s | -.0480419 .0513525 -0.94 0.350 -.1486911 .0526072 mtx | .0003759 .0004058 0.93 0.354 -.0004194 .0011713 wageinc | .000017 8.64e-07 19.64 0.000 .0000153 .0000187 132
Page 1 and 2:
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
Page 83 and 84: 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: note: 0 failures and 14 successes c
Page 137 and 138: Probit yr 2003 - Table 9: . use /ak
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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