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

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------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 . . /*Same estimation as Galarraga, 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 y96 y97 y98 y99 y00 y01 y02 y03, robust Iteration 0: log pseudolikelihood = -2397.2264 Iteration 1: log pseudolikelihood = -1169.1124 Iteration 2: log pseudolikelihood = -904.28001 Iteration 3: log pseudolikelihood = -846.90697 Iteration 4: log pseudolikelihood = -839.84586 Iteration 5: log pseudolikelihood = -839.69658 Iteration 6: log pseudolikelihood = -839.6965 Probit estimates Number of obs = 3508 Wald chi2(24) = 748.84 Prob > chi2 = 0.0000 Log pseudolikelihood = -839.6965 Pseudo R2 = 0.6497 ------------------------------------------------------------------------------ | Robust emp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- ad_dummy | -.1742844 .1233738 -1.41 0.158 -.4160925 .0675237 mtx | .0001328 .0003618 0.37 0.714 -.0005762 .0008418 wageinc | .0000168 7.23e-07 23.21 0.000 .0000154 .0000182 age | -.001956 .0186274 -0.11 0.916 -.0384651 .0345531 ab02 | -.168396 .2142451 -0.79 0.432 -.5883086 .2515167 ab36 | -.3193186 .1504407 -2.12 0.034 -.614177 -.0244603 ab79 | -.0744698 .1292373 -0.58 0.564 -.3277703 .1788308 ab1014 | -.2505354 .1068848 -2.34 0.019 -.4600257 -.0410451 single | -.3203914 .0820817 -3.90 0.000 -.4812686 -.1595143 ieland1 | .6006271 .1920577 3.13 0.002 .2242009 .9770532 ieland2 | .8194973 .2738106 2.99 0.003 .2828382 1.356156 short | .1942848 .0728769 2.67 0.008 .0514487 .3371209 higher | -.040152 .1278492 -0.31 0.753 -.2907318 .2104278 agesq | -.0002689 .000211 -1.27 0.202 -.0006824 .0001445 useofmedicin | -.0002895 .0000533 -5.44 0.000 -.0003939 -.0001851 u | -.0510167 .024143 -2.11 0.035 -.098336 -.0036973 y96 | -.1963778 .1855967 -1.06 0.290 -.5601406 .167385 y97 | .1257082 .1692056 0.74 0.458 -.2059287 .4573452 y98 | -.0718803 .1938154 -0.37 0.711 -.4517515 .3079909 y99 | .0157552 .1945518 0.08 0.935 -.3655593 .3970696 y00 | -.0640993 .2004765 -0.32 0.749 -.457026 .3288274 y01 | -.1016961 .2079957 -0.49 0.625 -.5093602 .305968 y02 | -.1563661 .2008877 -0.78 0.436 -.5500987 .2373665 y03 | -.0730488 .1872663 -0.39 0.696 -.4400839 .2939864 _cons | -.4026781 .549119 -0.73 0.463 -1.478932 .6735754 ------------------------------------------------------------------------------ note: 0 failures and 45 successes completely determined. . . lrtest A ., force likelihood-ratio test LR chi2(1) = 0.33 99
(Assumption: . nested in A) Prob > chi2 = 0.5635 . . mfx Marginal effects after probit y = Pr(emp) (predict) = .5732165 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- ad_dummy*| -.0689865 .04912 -1.40 0.160 -.165261 .027288 .099772 mtx | .0000521 .00014 0.37 0.713 -.000226 .00033 188.936 wageinc | 6.58e-06 .00000 28.37 0.000 6.1e-06 7.0e-06 88834.5 age | -.0007671 .00731 -0.11 0.916 -.015086 .013552 49.9438 ab02*| -.0667391 .08544 -0.78 0.435 -.234197 .100718 .022235 ab36*| -.1267386 .05957 -2.13 0.033 -.243493 -.009985 .054162 ab79*| -.0293612 .05117 -0.57 0.566 -.12966 .070938 .062999 ab1014*| -.0992994 .04247 -2.34 0.019 -.182548 -.01605 .117161 single*| -.1265101 .0325 -3.89 0.000 -.190212 -.062809 .267674 ieland1*| .2350886 .07114 3.30 0.001 .095661 .374516 .960946 ieland2*| .2719753 .06843 3.97 0.000 .137847 .406103 .023375 short*| .0756885 .02814 2.69 0.007 .020538 .130839 .364025 higher*| -.015786 .0504 -0.31 0.754 -.114559 .082987 .151368 agesq | -.0001055 .00008 -1.27 0.203 -.000268 .000057 2604.35 useofm~n | -.0001135 .00002 -5.37 0.000 -.000155 -.000072 846.506 u | -.020009 .00947 -2.11 0.035 -.038569 -.001449 6.34359 y96*| -.0778202 .07392 -1.05 0.292 -.222705 .067064 .074971 y97*| .0487378 .06475 0.75 0.452 -.078174 .175649 .087514 y98*| -.0283256 .07669 -0.37 0.712 -.178639 .121988 .095496 y99*| .006172 .07613 0.08 0.935 -.143032 .155376 .106043 y00*| -.0252398 .07921 -0.32 0.750 -.180497 .130017 .126853 y01*| -.0401187 .08244 -0.49 0.627 -.201705 .121468 .129418 y02*| -.0618002 .07979 -0.77 0.439 -.218187 .094587 .146237 y03*| -.0287648 .07399 -0.39 0.697 -.173773 .116243 .165336 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 Random effects probit – Table 3 & 5: . 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 ad_dummy mtx wageinc age ab02 ab36 ab79 ab1014 single ieland1 ie > land2 short higher agesq useofmedicin u s_1 Fitting comparison model: Iteration 0: log likelihood = -2240.7456 Iteration 1: log likelihood = -1085.0351 Iteration 2: log likelihood = -833.3197 Iteration 3: log likelihood = -777.48714 Iteration 4: log likelihood = -770.19934 100
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
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 and 98: Estimation results Probit yr 2003 -
Page 99 and 100: Probit estimates Number of obs = 58
Page 101: single | -.3439072 .0893125 -3.85 0
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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