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

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Iteration 0: log pseudolikelihood = -520.18215 Iteration 1: log pseudolikelihood = -346.48503 Iteration 2: log pseudolikelihood = -303.98085 Iteration 3: log pseudolikelihood = -294.47534 Iteration 4: log pseudolikelihood = -293.55088 Iteration 5: log pseudolikelihood = -293.53868 Iteration 6: log pseudolikelihood = -293.53868 Probit estimates Number of obs = 1666 Wald chi2(24) = 210.95 Prob > chi2 = 0.0000 Log pseudolikelihood = -293.53868 Pseudo R2 = 0.4357 ------------------------------------------------------------------------------ | Robust emp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- antidep | -.0183852 .0158644 -1.16 0.246 -.0494789 .0127085 mtx | .0006765 .0006373 1.06 0.288 -.0005726 .0019255 wageinc | 9.77e-06 1.26e-06 7.73 0.000 7.29e-06 .0000122 age | -.1110705 .035392 -3.14 0.002 -.1804375 -.0417036 ab02 | -.3474777 .2192384 -1.58 0.113 -.777177 .0822216 ab36 | .4810489 .3138864 1.53 0.125 -.1341571 1.096255 ab79 | .337391 .3180397 1.06 0.289 -.2859553 .9607372 ab1014 | .0203084 .255521 0.08 0.937 -.4805036 .5211203 single | -.1759805 .3307162 -0.53 0.595 -.8241723 .4722112 ieland1 | 1.28209 .4261012 3.01 0.003 .4469473 2.117233 ieland2 | 1.65214 .600906 2.75 0.006 .4743858 2.829894 short | -.0839803 .2937117 -0.29 0.775 -.6596445 .491684 higher | .4271495 .2199292 1.94 0.052 -.0039039 .8582029 agesq | .0015129 .0004548 3.33 0.001 .0006216 .0024043 useofmedicin | .0004723 .0005373 0.88 0.379 -.0005807 .0015253 u | -.0488988 .0615769 -0.79 0.427 -.1695872 .0717896 ohat | .0177281 .0158631 1.12 0.264 -.013363 .0488192 y96 | -.2968905 .3521879 -0.84 0.399 -.9871661 .393385 y97 | -.1541951 .3506618 -0.44 0.660 -.8414796 .5330895 y98 | -.2545084 .5233599 -0.49 0.627 -1.280275 .7712582 y99 | -.0634835 .3750674 -0.17 0.866 -.7986021 .6716351 y00 | -.4554536 .407458 -1.12 0.264 -1.254057 .3431495 y01 | -.4472914 .3996442 -1.12 0.263 -1.23058 .3359968 y02 | -.1767992 .2296154 -0.77 0.441 -.6268371 .2732388 _cons | 1.398057 .7599105 1.84 0.066 -.0913407 2.887454 ------------------------------------------------------------------------------ note: 0 failures and 26 successes completely determined. . . mfx Marginal effects after probit y = Pr(emp) (predict) = .99024594 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- antidep | -.0004795 .00043 -1.11 0.266 -.001325 .000366 18.8417 mtx | .0000176 .00002 1.03 0.303 -.000016 .000051 186.292 wageinc | 2.55e-07 .00000 3.59 0.000 1.2e-07 3.9e-07 182701 age | -.0028966 .00121 -2.39 0.017 -.005268 -.000525 46.2701 121
ab02*| -.0132912 .01201 -1.11 0.269 -.036837 .010254 .034814 ab36*| .0080002 .00406 1.97 0.049 .000038 .015962 .072629 ab79*| .0064272 .0047 1.37 0.171 -.002775 .015629 .083433 ab1014*| .0005212 .00646 0.08 0.936 -.012143 .013186 .159664 single*| -.005143 .01093 -0.47 0.638 -.026564 .016278 .233493 ieland1*| .1293632 .09316 1.39 0.165 -.053225 .311951 .972989 ieland2*| .0102742 .00383 2.69 0.007 .002775 .017774 .012605 short*| -.002228 .00795 -0.28 0.779 -.017806 .01335 .418968 higher*| .0086015 .00418 2.06 0.040 .000405 .016798 .210684 agesq | .0000395 .00002 2.51 0.012 8.6e-06 .00007 2240.74 useofm~n | .0000123 .00001 0.86 0.389 -.000016 .00004 620.683 u | -.0012752 .00167 -0.76 0.446 -.004557 .002006 6.08998 ohat | .0004623 .00043 1.08 0.282 -.00038 .001305 2.7e-08 y96*| -.0105591 .01629 -0.65 0.517 -.042484 .021366 .060624 y97*| -.0046915 .01229 -0.38 0.703 -.028775 .019392 .077431 y98*| -.0085307 .02198 -0.39 0.698 -.051601 .03454 .086435 y99*| -.0017555 .01095 -0.16 0.873 -.023214 .019702 .107443 y00*| -.0178656 .023 -0.78 0.437 -.062947 .027216 .132653 y01*| -.0173545 .0225 -0.77 0.441 -.061455 .026746 .136255 y02*| -.0053231 .008 -0.67 0.506 -.020999 .010353 .160864 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 Random effects probit – Table 7: . use /akf/702517/ycb2517/Initial/finaldata4.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 = -503.06464 Iteration 1: log likelihood = -328.50135 Iteration 2: log likelihood = -284.26838 Iteration 3: log likelihood = -273.9389 Iteration 4: log likelihood = -272.86409 Iteration 5: log likelihood = -272.84747 Iteration 6: log likelihood = -272.84746 Fitting full model: rho = 0.0 log likelihood = -272.84746 rho = 0.1 log likelihood = -263.18252 rho = 0.2 log likelihood = -256.80355 rho = 0.3 log likelihood = -253.12946 rho = 0.4 log likelihood = -252.12012 rho = 0.5 log likelihood = -254.14923 Iteration 0: log likelihood = -252.12012 Iteration 1: log likelihood = -236.43227 Iteration 2: log likelihood = -228.71939 Iteration 3: log likelihood = -225.43945 122
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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
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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
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 and 118: ab79 | -.2830833 .3452789 -0.82 0.4
Page 119 and 120: -----------------------------------
Page 121 and 122: . predict ohat, resid . . probit em
Page 123: y99 | -.1676129 .4540083 -0.37 0.71
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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