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

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Iteration 5: log likelihood = -770.02574 Iteration 6: log likelihood = -770.02563 Fitting full model: rho = 0.0 log likelihood = -770.02562 rho = 0.1 log likelihood = -712.58572 rho = 0.2 log likelihood = -681.06772 rho = 0.3 log likelihood = -663.21895 rho = 0.4 log likelihood = -655.03872 rho = 0.5 log likelihood = -655.37426 Iteration 0: log likelihood = -655.03874 Iteration 1: log likelihood = -600.46878 Iteration 2: log likelihood = -580.45053 Iteration 3: log likelihood = -574.68719 Iteration 4: log likelihood = -573.85806 Iteration 5: log likelihood = -573.83396 Iteration 6: log likelihood = -573.83394 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) = 225.13 Log likelihood = -573.83394 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ emp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- ad_dummy | -.3392331 .2505918 -1.35 0.176 -.830384 .1519178 mtx | -.0005456 .0007849 -0.70 0.487 -.0020841 .0009928 wageinc | .0000339 2.34e-06 14.48 0.000 .0000293 .0000385 age | .0377323 .0601334 0.63 0.530 -.0801271 .1555917 ab02 | -.0573035 .4945603 -0.12 0.908 -1.026624 .9120168 ab36 | -.5143236 .3795164 -1.36 0.175 -1.258162 .2295148 ab79 | -.2424511 .3551448 -0.68 0.495 -.9385222 .45362 ab1014 | -.4806625 .2647499 -1.82 0.069 -.9995628 .0382377 single | -.7474585 .213788 -3.50 0.000 -1.166475 -.3284419 ieland1 | .8799252 .895094 0.98 0.326 -.8744268 2.634277 ieland2 | 1.592064 .9962577 1.60 0.110 -.3605648 3.544694 short | .6381415 .2259877 2.82 0.005 .1952136 1.081069 higher | -.0753776 .3149125 -0.24 0.811 -.6925947 .5418396 agesq | -.0012078 .0006724 -1.80 0.072 -.0025257 .0001101 useofmedicin | -.0004552 .0001176 -3.87 0.000 -.0006857 -.0002247 u | -.0757359 .0483956 -1.56 0.118 -.1705895 .0191177 s_1 | -.0991747 .1625065 -0.61 0.542 -.4176815 .2193321 _cons | -1.40745 1.608911 -0.87 0.382 -4.560858 1.745958 -------------+---------------------------------------------------------------- /lnsig2u | 1.404753 .1689736 1.073571 1.735936 -------------+---------------------------------------------------------------- sigma_u | 2.018544 .1705404 1.7105 2.382065 rho | .8029371 .0267365 .7452755 .8501701 ------------------------------------------------------------------------------ Likelihood-ratio test of rho=0: chibar2(01) = 392.38 Prob >= chibar2 = 0.000 . 101
. /*with proxy, generalised residual*/ . quietly xtprobit ad_dummy mtx wageinc age ab02 ab36 ab79 ab1014 single ieland > 1 ieland2 short higher agesq useofmedicin u . . 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 . . xtprobit emp ad_dummy mtx wageinc age ab02 ab36 ab79 ab1014 single ieland1 ie > land2 short higher agesq useofmedicin u res Fitting comparison model: Iteration 0: log likelihood = -2397.2264 Iteration 1: log likelihood = -1170.2091 Iteration 2: log likelihood = -906.25634 Iteration 3: log likelihood = -849.1573 Iteration 4: log likelihood = -842.22476 Iteration 5: log likelihood = -842.08459 Iteration 6: log likelihood = -842.08453 Fitting full model: rho = 0.0 log likelihood = -842.08451 rho = 0.1 log likelihood = -775.97807 rho = 0.2 log likelihood = -740.37837 rho = 0.3 log likelihood = -720.29173 rho = 0.4 log likelihood = -710.98419 rho = 0.5 log likelihood = -711.06979 Iteration 0: log likelihood = -710.9842 Iteration 1: log likelihood = -652.81059 Iteration 2: log likelihood = -631.30507 Iteration 3: log likelihood = -624.69828 Iteration 4: log likelihood = -623.68727 Iteration 5: log likelihood = -623.66033 Iteration 6: log likelihood = -623.66031 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(17) = 243.11 Log likelihood = -623.66031 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ emp | Coef. Std. Err. z P>|z| [95% Conf. Interval] 102
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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
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 and 102: single | -.3439072 .0893125 -3.85 0
Page 103: (Assumption: . nested in A) Prob >
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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