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

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age | -.0142055 .0151 -0.94 0.347 -.043805 .015394 49.9776 ab02*| .1390055 .07457 1.86 0.062 -.007142 .285153 .031034 ab36*| -.0289816 .11206 -0.26 0.796 -.24862 .190657 .063793 ab79*| .070395 .07556 0.93 0.352 -.077707 .218497 .067241 ab1014*| .0302273 .07367 0.41 0.682 -.114165 .174619 .136207 single*| -.0200232 .06609 -0.30 0.762 -.14955 .109504 .255172 ieland1*| .1254984 .16534 0.76 0.448 -.198562 .449559 .960345 ieland2*| .1800917 .08532 2.11 0.035 .012864 .347319 .018966 short*| .0534635 .06206 0.86 0.389 -.068181 .175108 .4 higher*| .0019291 .06577 0.03 0.977 -.12697 .130828 .174138 agesq | .0000931 .00017 0.55 0.583 -.000239 .000426 2610.34 useofm~n | -.0000439 .00003 -1.30 0.193 -.00011 .000022 866.898 u | -.0313234 .02063 -1.52 0.129 -.071752 .009105 6.07914 res | -.0146387 .09155 -0.16 0.873 -.194078 .1648 -.820607 ------------------------------------------------------------------------------ (*) 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 if yr==2003, robust Iteration 0: log pseudolikelihood = -402.02192 Iteration 1: log pseudolikelihood = -185.75068 Iteration 2: log pseudolikelihood = -129.6056 Iteration 3: log pseudolikelihood = -115.28147 Iteration 4: log pseudolikelihood = -112.34302 Iteration 5: log pseudolikelihood = -112.1343 Iteration 6: log pseudolikelihood = -112.13304 Iteration 7: log pseudolikelihood = -112.13304 Probit estimates Number of obs = 580 Wald chi2(16) = 162.83 Prob > chi2 = 0.0000 Log pseudolikelihood = -112.13304 Pseudo R2 = 0.7211 ------------------------------------------------------------------------------ | Robust emp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- antidep_la~s | .0052467 .0812647 0.06 0.949 -.1540292 .1645226 mtx | .0006242 .0008746 0.71 0.475 -.0010899 .0023383 wageinc | .0000186 1.76e-06 10.59 0.000 .0000152 .0000221 age | -.0473518 .0506241 -0.94 0.350 -.1465732 .0518696 ab02 | .5861389 .4144896 1.41 0.157 -.2262459 1.398524 ab36 | -.0967906 .3508863 -0.28 0.783 -.7845151 .590934 ab79 | .2544524 .3000153 0.85 0.396 -.3335667 .8424715 ab1014 | .1081523 .2599763 0.42 0.677 -.401392 .6176966 single | -.0609908 .2117839 -0.29 0.773 -.4760795 .354098 ieland1 | .3737814 .4496264 0.83 0.406 -.5074703 1.255033 ieland2 | .8910629 .7056955 1.26 0.207 -.4920749 2.274201 short | .183674 .2078821 0.88 0.377 -.2237674 .5911155 higher | .0157891 .2153691 0.07 0.942 -.4063265 .4379047 agesq | .0003112 .0005688 0.55 0.584 -.0008036 .001426 useofmedicin | -.0001434 .0001068 -1.34 0.179 -.0003527 .0000658 u | -.1060347 .0664881 -1.59 0.111 -.2363489 .0242796 _cons | .3107791 1.268455 0.25 0.806 -2.175348 2.796906 ------------------------------------------------------------------------------ 129
note: 0 failures and 14 successes completely determined. . . lrtest A ., force likelihood-ratio test LR chi2(1) = 0.04 (Assumption: . nested in A) Prob > chi2 = 0.8504 . . mfx Marginal effects after probit y = Pr(emp) (predict) = .7732281 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- antide~s | .0015806 .02448 0.06 0.949 -.046405 .049566 .308621 mtx | .000188 .00027 0.71 0.479 -.000332 .000708 220.258 wageinc | 5.61e-06 .00000 14.35 0.000 4.8e-06 6.4e-06 115135 age | -.0142645 .01515 -0.94 0.346 -.043955 .015426 49.9776 ab02*| .1384481 .07481 1.85 0.064 -.008185 .285081 .031034 ab36*| -.0300625 .11208 -0.27 0.789 -.249736 .189611 .063793 ab79*| .0701041 .07587 0.92 0.356 -.078605 .218814 .067241 ab1014*| .0316034 .0737 0.43 0.668 -.112843 .17605 .136207 single*| -.0185766 .0653 -0.28 0.776 -.14657 .109417 .255172 ieland1*| .1257292 .16607 0.76 0.449 -.199762 .45122 .960345 ieland2*| .1796732 .08664 2.07 0.038 .009853 .349493 .018966 short*| .0545338 .06167 0.88 0.377 -.066347 .175414 .4 higher*| .004738 .0645 0.07 0.941 -.121684 .13116 .174138 agesq | .0000937 .00017 0.55 0.582 -.00024 .000427 2610.34 useofm~n | -.0000432 .00003 -1.27 0.204 -.00011 .000023 866.898 u | -.0319425 .02065 -1.55 0.122 -.072423 .008538 6.07914 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 Pooled probit – Table 8: . use /akf/702517/ycb2517/Initial/finaldata3.dta . /*Including the variable indicating how many years antidep are taken, pooled > probit*/ . quietly reg antidep_last5yrs mtx wageinc age ab02 ab36 ab79 ab1014 single iel > and1 ieland2 short higher agesq useofmedicin u y96 y97 y98 y99 y00 y01 y02 y0 > 3 . . predict xb, xb . gen normxb=norm(xb) . gen normdenxb=normden(xb) . gen denominator=normxb*[1-normxb] . gen numerator=normdenxb*[ad_dummy-normxb] . gen res=numerator/denominator 130
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;
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 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: . . probit emp antidep_last5yrs mtx
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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