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

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------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- antidep | -.0020812 .00489 -0.43 0.670 -.011665 .007503 27.1569 mtx | .0000233 .00016 0.15 0.883 -.000287 .000334 188.936 wageinc | 6.51e-06 .00000 21.54 0.000 5.9e-06 7.1e-06 88834.5 age | -.0034828 .01 -0.35 0.728 -.023084 .016118 49.9438 ab02*| -.0675806 .08495 -0.80 0.426 -.234077 .098915 .022235 ab36*| -.1050931 .08534 -1.23 0.218 -.272359 .062173 .054162 ab79*| -.0151147 .05935 -0.25 0.799 -.131442 .101213 .062999 ab1014*| -.0776334 .06637 -1.17 0.242 -.207722 .052455 .117161 single*| -.1138678 .04639 -2.45 0.014 -.204796 -.02294 .267674 ieland1*| .2733518 .11911 2.29 0.022 .0399 .506804 .960946 ieland2*| .2884407 .07597 3.80 0.000 .139548 .437333 .023375 short*| .0615327 .0456 1.35 0.177 -.027835 .1509 .364025 higher*| -.0162608 .05031 -0.32 0.747 -.114872 .082351 .151368 agesq | -.0000742 .00011 -0.65 0.516 -.000298 .00015 2604.35 useofm~n | -.0000566 .00015 -0.38 0.700 -.000345 .000232 846.506 u | -.0257554 .02081 -1.24 0.216 -.066542 .015031 6.34359 y96*| -.0823854 .08342 -0.99 0.323 -.24589 .081119 .074971 y97*| .0570856 .05088 1.12 0.262 -.042641 .156812 .087514 y98*| -.026784 .07344 -0.36 0.715 -.170727 .117159 .095496 y99*| .0142662 .05912 0.24 0.809 -.101603 .130136 .106043 y00*| -.0275298 .08029 -0.34 0.732 -.184887 .129828 .126853 y01*| -.0354926 .0731 -0.49 0.627 -.178759 .107774 .129418 y02*| -.037737 .0495 -0.76 0.446 -.134754 .05928 .146237 ohat | .001895 .00489 0.39 0.698 -.007693 .011483 -5.5e-09 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 Random effects probit – Table 6: . use /akf/702517/ycb2517/Initial/finaldata5.dta . /*Panel probit on the full sample - same estimation as Galarraga*/ . . iis udtrnr . tis yr . . /*Test for sample selection */ . xtprobit emp antidep mtx wageinc age ab02 ab36 ab79 ab1014 single ieland1 iel > and2 short higher agesq useofmedicin u s_1 Fitting comparison model: Iteration 0: log likelihood = -2240.7456 Iteration 1: log likelihood = -1085.039 Iteration 2: log likelihood = -833.29396 Iteration 3: log likelihood = -777.44928 Iteration 4: log likelihood = -770.16532 Iteration 5: log likelihood = -769.9923 Iteration 6: log likelihood = -769.9922 Fitting full model: rho = 0.0 log likelihood = -769.99218 rho = 0.1 log likelihood = -712.79611 111
ho = 0.2 log likelihood = -681.42709 rho = 0.3 log likelihood = -663.68109 rho = 0.4 log likelihood = -655.57901 rho = 0.5 log likelihood = -655.97949 Iteration 0: log likelihood = -655.57902 Iteration 1: log likelihood = -601.13332 Iteration 2: log likelihood = -581.10382 Iteration 3: log likelihood = -575.20116 Iteration 4: log likelihood = -574.32505 Iteration 5: log likelihood = -574.29853 Iteration 6: log likelihood = -574.2985 Random-effects probit regression Number of obs = 3269 Group variable (i): udtrnr Number of groups = 1133 Random effects u_i ~ Gaussian Obs per group: min = 1 avg = 2.9 max = 8 Wald chi2(17) = 224.95 Log likelihood = -574.2985 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ emp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- antidep | -.0007548 .0007805 -0.97 0.333 -.0022845 .0007749 mtx | -.0005417 .0007807 -0.69 0.488 -.002072 .0009885 wageinc | .000034 2.34e-06 14.55 0.000 .0000295 .0000386 age | .0358341 .059697 0.60 0.548 -.0811699 .1528381 ab02 | -.0665037 .4922627 -0.14 0.893 -1.031321 .8983135 ab36 | -.5061335 .3765491 -1.34 0.179 -1.244156 .2318892 ab79 | -.2245449 .3550032 -0.63 0.527 -.9203384 .4712485 ab1014 | -.4837389 .2630451 -1.84 0.066 -.9992979 .0318201 single | -.7490884 .2137255 -3.50 0.000 -1.167983 -.3301941 ieland1 | .8667044 .911593 0.95 0.342 -.9199851 2.653394 ieland2 | 1.570777 1.004257 1.56 0.118 -.3975299 3.539083 short | .660297 .2222343 2.97 0.003 .2247257 1.095868 higher | -.0635333 .3153708 -0.20 0.840 -.6816488 .5545821 agesq | -.0011902 .0006702 -1.78 0.076 -.0025038 .0001233 useofmedicin | -.0004515 .0001186 -3.81 0.000 -.0006839 -.0002191 u | -.075748 .0478583 -1.58 0.113 -.1695486 .0180525 s_1 | -.0949006 .1626964 -0.58 0.560 -.4137797 .2239785 _cons | -1.396449 1.608461 -0.87 0.385 -4.548974 1.756077 -------------+---------------------------------------------------------------- /lnsig2u | 1.416024 .1677786 1.087184 1.744864 -------------+---------------------------------------------------------------- sigma_u | 2.029952 .1702912 1.722182 2.392723 rho | .8047144 .0263663 .7478511 .8513039 ------------------------------------------------------------------------------ Likelihood-ratio test of rho=0: chibar2(01) = 391.39 Prob >= chibar2 = 0.000 . . /*with proxy, generalised residual*/ . quietly xtprobit antidep mtx wageinc age ab02 ab36 ab79 ab1014 single ieland1 > ieland2 short higher agesq useofmedicin u . . predict xb, xb 112
Page 1 and 2:
Estimating the Impact of An
Page 3 and 4:
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
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 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: . . probit emp antidep mtx wageinc
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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