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

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------------------------------------------------------------------------------ Likelihood-ratio test of rho=0: chibar2(01) = 118.34 Prob >= chibar2 = 0.000 . . lrtest A . (log-likelihoods of null models cannot be compared) likelihood-ratio test LR chi2(1) = 0.00 (Assumption: . nested in A) Prob > chi2 = 0.9856 . . mfx Marginal effects after xtprobit y = Linear prediction (predict) = 6.7920514 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- antidep | -.0012876 .00122 -1.06 0.291 -.003679 .001104 18.8417 mtx | .0002749 .00174 0.16 0.874 -.003131 .003681 186.292 wageinc | .0000303 .00000 6.67 0.000 .000021 .000039 182701 age | -.3614512 .13069 -2.77 0.006 -.617601 -.105302 46.2701 ab02*| -.4999006 .78428 -0.64 0.524 -2.03705 1.03725 .034814 ab36*| .4617282 .6016 0.77 0.443 -.717385 1.64084 .072629 ab79*| -.1985345 .65403 -0.30 0.761 -1.48041 1.08335 .083433 ab1014*| -.1618629 .44603 -0.36 0.717 -1.03606 .712337 .159664 single*| -1.825653 .55859 -3.27 0.001 -2.92047 -.73084 .233493 ieland1*| 2.5429 1.57673 1.61 0.107 -.547441 5.63324 .972989 ieland2*| 3.71043 1.87243 1.98 0.048 .040541 7.38032 .012605 short*| .8885039 .50836 1.75 0.080 -.107857 1.88487 .418968 higher*| 2.062108 1.21082 1.70 0.089 -.311064 4.43528 .210684 agesq | .0047 .00165 2.85 0.004 .001472 .007928 2240.74 useofm~n | -.0000217 .00021 -0.10 0.918 -.000435 .000392 620.683 u | .0459402 .09457 0.49 0.627 -.139421 .231302 6.08998 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 Probit yr 2003 – Table 8: . use /akf/702517/ycb2517/Initial/finaldata3.dta . /*generalised residual*/ . quietly reg antidep_last5yrs mtx wageinc age ab02 ab36 ab79 ab1014 single iel > and1 ieland2 short higher agesq useofmedicin u if yr==2003 . . 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 127
. . probit emp antidep_last5yrs mtx wageinc age ab02 ab36 ab79 ab1014 single iela > nd1 ieland2 short higher agesq useofmedicin u res if yr==2003, robust Iteration 0: log pseudolikelihood = -402.02192 Iteration 1: log pseudolikelihood = -185.57763 Iteration 2: log pseudolikelihood = -129.59187 Iteration 3: log pseudolikelihood = -115.31053 Iteration 4: log pseudolikelihood = -112.33537 Iteration 5: log pseudolikelihood = -112.11667 Iteration 6: log pseudolikelihood = -112.11525 Iteration 7: log pseudolikelihood = -112.11525 Probit estimates Number of obs = 580 Wald chi2(17) = 161.84 Prob > chi2 = 0.0000 Log pseudolikelihood = -112.11525 Pseudo R2 = 0.7211 ------------------------------------------------------------------------------ | Robust emp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- antidep_la~s | .0292075 .1466685 0.20 0.842 -.2582576 .3166725 mtx | .0006103 .0008655 0.71 0.481 -.001086 .0023066 wageinc | .0000187 1.83e-06 10.21 0.000 .0000151 .0000223 age | -.0472756 .0506643 -0.93 0.351 -.1465759 .0520246 ab02 | .5921382 .4195766 1.41 0.158 -.2302169 1.414493 ab36 | -.0936244 .3521821 -0.27 0.790 -.7838887 .5966399 ab79 | .2564399 .3004253 0.85 0.393 -.3323828 .8452626 ab1014 | .1035813 .2598664 0.40 0.690 -.4057476 .6129101 single | -.0658467 .2145613 -0.31 0.759 -.4863791 .3546857 ieland1 | .3738134 .4481719 0.83 0.404 -.5045874 1.252214 ieland2 | .9010256 .7084818 1.27 0.203 -.4875731 2.289624 short | .180488 .2095012 0.86 0.389 -.2301268 .5911028 higher | .0064302 .219379 0.03 0.977 -.4235448 .4364052 agesq | .0003098 .0005688 0.54 0.586 -.000805 .0014247 useofmedicin | -.0001462 .0001056 -1.39 0.166 -.0003531 .0000607 u | -.1042433 .066449 -1.57 0.117 -.234481 .0259943 res | -.0487172 .3053085 -0.16 0.873 -.6471109 .5496765 _cons | .2580592 1.290829 0.20 0.842 -2.27192 2.788038 ------------------------------------------------------------------------------ note: 0 failures and 15 successes completely determined. . . est store A . . mfx Marginal effects after probit y = Pr(emp) (predict) = .77424308 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- antide~s | .0087764 .04399 0.20 0.842 -.077446 .094998 .308621 mtx | .0001834 .00026 0.70 0.484 -.00033 .000697 220.258 wageinc | 5.61e-06 .00000 14.07 0.000 4.8e-06 6.4e-06 115135 128
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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
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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
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 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: Iteration 2: log likelihood = -306.
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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