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

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Iteration 0: log likelihood = -2397.2264 Iteration 1: log likelihood = -1170.3153 Iteration 2: log likelihood = -906.60879 Iteration 3: log likelihood = -849.76569 Iteration 4: log likelihood = -842.89382 Iteration 5: log likelihood = -842.75549 Iteration 6: log likelihood = -842.75543 Fitting full model: rho = 0.0 log likelihood = -842.75542 rho = 0.1 log likelihood = -776.80317 rho = 0.2 log likelihood = -741.28085 rho = 0.3 log likelihood = -721.23042 rho = 0.4 log likelihood = -711.93865 rho = 0.5 log likelihood = -712.01733 Iteration 0: log likelihood = -711.93867 Iteration 1: log likelihood = -653.64213 Iteration 2: log likelihood = -631.95208 Iteration 3: log likelihood = -625.16514 Iteration 4: log likelihood = -624.1176 Iteration 5: log likelihood = -624.0903 Iteration 6: log likelihood = -624.09027 Random-effects probit regression Number of obs = 3508 Group variable (i): udtrnr Number of groups = 1202 Random effects u_i ~ Gaussian Obs per group: min = 1 avg = 2.9 max = 9 Wald chi2(16) = 243.54 Log likelihood = -624.09027 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ emp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- antidep | -.0008029 .0007894 -1.02 0.309 -.0023502 .0007443 mtx | -.0005063 .0007161 -0.71 0.480 -.0019099 .0008972 wageinc | .0000342 2.27e-06 15.11 0.000 .0000298 .0000387 age | .0390453 .0586165 0.67 0.505 -.075841 .1539316 ab02 | -.285581 .4620704 -0.62 0.537 -1.191222 .6200603 ab36 | -.6165498 .3589297 -1.72 0.086 -1.320039 .0869395 ab79 | -.2731251 .3455655 -0.79 0.429 -.9504211 .4041709 ab1014 | -.5889686 .2514611 -2.34 0.019 -1.081823 -.0961139 single | -.7601944 .2074002 -3.67 0.000 -1.166691 -.3536976 ieland1 | .8201282 .863622 0.95 0.342 -.8725398 2.512796 ieland2 | 1.446609 .9496382 1.52 0.128 -.4146479 3.307865 short | .7106384 .2086481 3.41 0.001 .3016957 1.119581 higher | -.0656993 .2853568 -0.23 0.818 -.6249883 .4935898 agesq | -.0012652 .0006486 -1.95 0.051 -.0025365 6.11e-06 useofmedicin | -.0004506 .0001149 -3.92 0.000 -.0006758 -.0002253 u | -.0604819 .0370195 -1.63 0.102 -.1330387 .0120749 _cons | -1.48013 1.5785 -0.94 0.348 -4.573932 1.613672 -------------+---------------------------------------------------------------- /lnsig2u | 1.436999 .158339 1.12666 1.747338 -------------+---------------------------------------------------------------- sigma_u | 2.051353 .1624046 1.756512 2.395684 rho | .8079895 .0245651 .755222 .8516167 115
------------------------------------------------------------------------------ Likelihood-ratio test of rho=0: chibar2(01) = 437.33 Prob >= chibar2 = 0.000 . . lrtest A . (log-likelihoods of null models cannot be compared) likelihood-ratio test LR chi2(1) = 0.92 (Assumption: . nested in A) Prob > chi2 = 0.3367 . . mfx Marginal effects after xtprobit y = Linear prediction (predict) = .07410012 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- antidep | -.0008029 .00079 -1.02 0.309 -.00235 .000744 27.1569 mtx | -.0005063 .00072 -0.71 0.480 -.00191 .000897 188.936 wageinc | .0000342 .00000 15.11 0.000 .00003 .000039 88834.5 age | .0390453 .05862 0.67 0.505 -.075841 .153932 49.9438 ab02*| -.285581 .46207 -0.62 0.537 -1.19122 .62006 .022235 ab36*| -.6165498 .35893 -1.72 0.086 -1.32004 .08694 .054162 ab79*| -.2731251 .34557 -0.79 0.429 -.950421 .404171 .062999 ab1014*| -.5889686 .25146 -2.34 0.019 -1.08182 -.096114 .117161 single*| -.7601944 .2074 -3.67 0.000 -1.16669 -.353698 .267674 ieland1*| .8201282 .86362 0.95 0.342 -.87254 2.5128 .960946 ieland2*| 1.446609 .94964 1.52 0.128 -.414648 3.30787 .023375 short*| .7106384 .20865 3.41 0.001 .301696 1.11958 .364025 higher*| -.0656993 .28536 -0.23 0.818 -.624988 .49359 .151368 agesq | -.0012652 .00065 -1.95 0.051 -.002537 6.1e-06 2604.35 useofm~n | -.0004506 .00011 -3.92 0.000 -.000676 -.000225 846.506 u | -.0604819 .03702 -1.63 0.102 -.133039 .012075 6.34359 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 Probit yr 2003 – Table 7: . use /akf/702517/ycb2517/Initial/finaldata4.dta . /*EMP/UNEMP w dosis - year 2003 - robust std errors*/ . probit emp antidep mtx wageinc age ab02 ab36 ab79 ab1014 single ieland1 ielan > d2 short higher agesq useofmedicin u if yr==2003, robust note: ieland2 != 0 predicts success perfectly ieland2 dropped and 4 obs not used Iteration 0: log pseudolikelihood = -87.073703 Iteration 1: log pseudolikelihood = -56.307727 Iteration 2: log pseudolikelihood = -46.213664 Iteration 3: log pseudolikelihood = -43.143476 Iteration 4: log pseudolikelihood = -42.568445 Iteration 5: log pseudolikelihood = -42.543339 Iteration 6: log pseudolikelihood = -42.543284 Probit estimates Number of obs = 312 Wald chi2(15) = 59.45 116
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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
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 and 114: . . probit emp antidep mtx wageinc
Page 115 and 116: ho = 0.2 log likelihood = -681.4270
Page 117: ab79 | -.2830833 .3452789 -0.82 0.4
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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