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

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ieland2 | .8603897 .7499049 1.15 0.251 -.6093968 2.330176 short | .1733007 .2341519 0.74 0.459 -.2856285 .6322299 higher | -.0047832 .2935294 -0.02 0.987 -.5800902 .5705238 agesq | .0003091 .0005693 0.54 0.587 -.0008068 .0014249 useofmedicin | -.0001283 .000173 -0.74 0.458 -.0004673 .0002108 u | -.1100067 .0724998 -1.52 0.129 -.2521037 .0320904 res | .19101 1.825832 0.10 0.917 -3.387554 3.769574 _cons | .4104052 1.516369 0.27 0.787 -2.561624 3.382435 ------------------------------------------------------------------------------ note: 0 failures and 14 successes completely determined. . . est store A . . mfx Marginal effects after probit y = Pr(emp) (predict) = .77304318 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- ad_dummy*| -.1246787 1.20163 -0.10 0.917 -2.47984 2.23048 .113793 mtx | .0002031 .0003 0.68 0.500 -.000386 .000792 220.258 wageinc | 5.59e-06 .00000 13.46 0.000 4.8e-06 6.4e-06 115135 age | -.0143382 .01511 -0.95 0.343 -.043953 .015277 49.9776 ab02*| .1357966 .07908 1.72 0.086 -.019198 .290791 .031034 ab36*| -.0380364 .13277 -0.29 0.775 -.298257 .222185 .063793 ab79*| .0765487 .09192 0.83 0.405 -.103604 .256702 .067241 ab1014*| .0261965 .09735 0.27 0.788 -.1646 .216993 .136207 single*| -.0154451 .07095 -0.22 0.828 -.154512 .123622 .255172 ieland1*| .1167761 .18775 0.62 0.534 -.251216 .484768 .960345 ieland2*| .1763222 .09415 1.87 0.061 -.008205 .360849 .018966 short*| .0515222 .0692 0.74 0.457 -.084116 .18716 .4 higher*| -.0014433 .08865 -0.02 0.987 -.17519 .172304 .174138 agesq | .0000931 .00017 0.55 0.585 -.000241 .000427 2610.34 useofm~n | -.0000387 .00005 -0.73 0.467 -.000143 .000065 866.898 u | -.0331543 .02249 -1.47 0.140 -.077238 .010929 6.07914 res | .0575674 .55051 0.10 0.917 -1.02141 1.13655 -2.5e-10 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 . . /*Same estimation as Galarraga on year 2003, with some extra variables though > - robust std errors - with proxy*/ . probit emp ad_dummy mtx wageinc age ab02 ab36 ab79 ab1014 single ieland1 iela > nd2 short higher agesq useofmedicin u if yr==2003, robust Iteration 0: log pseudolikelihood = -402.02192 Iteration 1: log pseudolikelihood = -185.72241 Iteration 2: log pseudolikelihood = -129.61534 Iteration 3: log pseudolikelihood = -115.28423 Iteration 4: log pseudolikelihood = -112.34187 Iteration 5: log pseudolikelihood = -112.13256 Iteration 6: log pseudolikelihood = -112.13129 Iteration 7: log pseudolikelihood = -112.13129 95
Probit estimates Number of obs = 580 Wald chi2(16) = 162.64 Prob > chi2 = 0.0000 Log pseudolikelihood = -112.13129 Pseudo R2 = 0.7211 ------------------------------------------------------------------------------ | Robust emp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- ad_dummy | -.0208104 .2950877 -0.07 0.944 -.5991716 .5575509 mtx | .00063 .000877 0.72 0.473 -.001089 .002349 wageinc | .0000186 1.76e-06 10.58 0.000 .0000152 .000022 age | -.0472931 .050606 -0.93 0.350 -.1464791 .0518928 ab02 | .5848914 .4139067 1.41 0.158 -.2263508 1.396134 ab36 | -.0995858 .3509049 -0.28 0.777 -.7873468 .5881752 ab79 | .2558036 .3006665 0.85 0.395 -.333492 .8450991 ab1014 | .106274 .2599749 0.41 0.683 -.4032674 .6158154 single | -.0598418 .2110184 -0.28 0.777 -.4734303 .3537467 ieland1 | .3737413 .4496898 0.83 0.406 -.5076345 1.255117 ieland2 | .8866241 .7040704 1.26 0.208 -.4933285 2.266577 short | .1816948 .2080397 0.87 0.382 -.2260555 .5894451 higher | .0132379 .2148606 0.06 0.951 -.4078811 .4343569 agesq | .0003104 .0005686 0.55 0.585 -.0008041 .0014248 useofmedicin | -.0001429 .000107 -1.34 0.182 -.0003526 .0000668 u | -.1067894 .0668882 -1.60 0.110 -.2378879 .0243091 _cons | .3191063 1.273491 0.25 0.802 -2.17689 2.815103 ------------------------------------------------------------------------------ note: 0 failures and 14 successes completely determined. . . lrtest A ., force likelihood-ratio test LR chi2(1) = 0.01 (Assumption: . nested in A) Prob > chi2 = 0.9379 . . mfx Marginal effects after probit y = Pr(emp) (predict) = .77333712 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- ad_dummy*| -.006305 .08989 -0.07 0.944 -.182492 .169882 .113793 mtx | .0001897 .00027 0.71 0.476 -.000332 .000711 220.258 wageinc | 5.60e-06 .00000 14.39 0.000 4.8e-06 6.4e-06 115135 age | -.014243 .01514 -0.94 0.347 -.043915 .015429 49.9776 ab02*| .1381839 .07478 1.85 0.065 -.008382 .28475 .031034 ab36*| -.0309491 .11223 -0.28 0.783 -.250921 .189023 .063793 ab79*| .0704188 .0759 0.93 0.354 -.078338 .219175 .067241 ab1014*| .0310626 .07377 0.42 0.674 -.113518 .175643 .136207 single*| -.0182181 .06505 -0.28 0.779 -.145709 .109273 .255172 ieland1*| .1256886 .16611 0.76 0.449 -.199873 .451251 .960345 ieland2*| .1791095 .08692 2.06 0.039 .008755 .349464 .018966 short*| .0539401 .06166 0.87 0.382 -.066905 .174785 .4 higher*| .0039739 .06439 0.06 0.951 -.122236 .130184 .174138 agesq | .0000935 .00017 0.55 0.583 -.00024 .000427 2610.34 96
Page 1 and 2:
Estimating the Impact of An
Page 3 and 4:
Abstract This thesis aims at analys
Page 5 and 6:
1 Introduction According to the Wor
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consisting of lack of data on the d
Page 9 and 10:
experience symptoms of depression o
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(SSRI’s), also called the new ant
Page 13 and 14:
heumatologists. But since descripti
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H > 0 or a corner solution, H = 0 .
Page 17 and 18:
employers more reluctant to hire ne
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One of the most cited models when a
Page 21 and 22:
One of the factors greatly influenc
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Galarraga et al. (2006) have chosen
Page 25 and 26:
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
Page 31 and 32:
Inspired by Galarraga et al. (2006)
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are used by other patients groups,
Page 35 and 36:
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
Page 45 and 46:
Figure 7 The distribution of the us
Page 47 and 48: Figure 9 depicts the defined daily
Page 49 and 50: where φ ( z) is the standard norma
Page 51 and 52: 5.3 The binary response model and p
Page 53 and 54: The log of equation [5.7] gives the
Page 55 and 56: antidepressant use are displayed in
Page 57 and 58: As established in chapter 4, the pa
Page 59 and 60: probit estimation, respectively. Ha
Page 61 and 62: estimation. And ag
Page 63 and 64: As can be seen from table 7 the ext
Page 65 and 66: mtx 0.0002 0.0003 0.0001 0.0002 wag
Page 67 and 68: probit estimation produces more sig
Page 69 and 70: health. As a consequence, further a
Page 71 and 72: 8 Conclusion The aim of this thesis
Page 73 and 74: References Agerbo, E., T. Eriksson,
Page 75 and 76: Zimmerman, F. and W. Katon. 2005. S
Page 77 and 78: wage2 Dummy=1 if wage income up to
Page 79 and 80: data medold; set b.lmdb_pct10_2003;
Page 81 and 82: 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: Estimation results Probit yr 2003 -
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 and 136: y = Pr(emp) (predict) = .64904538 -
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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