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

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. 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 antidep mtx wageinc age ab02 ab36 ab79 ab1014 single ieland1 iel > and2 short higher agesq useofmedicin u res Fitting comparison model: Iteration 0: log likelihood = -2397.2264 Iteration 1: log likelihood = -1170.1151 Iteration 2: log likelihood = -906.2478 Iteration 3: log likelihood = -849.33117 Iteration 4: log likelihood = -842.44778 Iteration 5: log likelihood = -842.30885 Iteration 6: log likelihood = -842.30879 Fitting full model: rho = 0.0 log likelihood = -842.30878 rho = 0.1 log likelihood = -776.2471 rho = 0.2 log likelihood = -740.644 rho = 0.3 log likelihood = -720.53824 rho = 0.4 log likelihood = -711.20843 rho = 0.5 log likelihood = -711.26772 Iteration 0: log likelihood = -711.20844 Iteration 1: log likelihood = -652.85054 Iteration 2: log likelihood = -631.2676 Iteration 3: log likelihood = -624.65579 Iteration 4: log likelihood = -623.65496 Iteration 5: log likelihood = -623.62886 Iteration 6: log likelihood = -623.62884 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) = 243.06 Log likelihood = -623.62884 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ emp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- antidep | -.0002677 .0009886 -0.27 0.787 -.0022052 .0016698 mtx | -.00052 .0007176 -0.72 0.469 -.0019264 .0008864 wageinc | .0000341 2.27e-06 15.04 0.000 .0000297 .0000386 age | .0392964 .0581997 0.68 0.500 -.0747729 .1533656 ab02 | -.273554 .4634309 -0.59 0.555 -1.181862 .6347538 ab36 | -.6163484 .3597539 -1.71 0.087 -1.321453 .0887564 113
ab79 | -.2830833 .3452789 -0.82 0.412 -.9598175 .3936509 ab1014 | -.5803921 .2515195 -2.31 0.021 -1.073361 -.0874229 single | -.7553261 .2068115 -3.65 0.000 -1.160669 -.349983 ieland1 | .8304294 .8506158 0.98 0.329 -.8367468 2.497606 ieland2 | 1.481234 .939049 1.58 0.115 -.3592682 3.321736 short | .6990454 .2107429 3.32 0.001 .2859969 1.112094 higher | -.0783054 .2836814 -0.28 0.783 -.6343107 .4776999 agesq | -.0012635 .0006432 -1.96 0.049 -.0025241 -2.97e-06 useofmedicin | -.0004582 .0001147 -4.00 0.000 -.000683 -.0002334 u | -.0606001 .0371444 -1.63 0.103 -.1334017 .0122015 res | -.0881937 .0927614 -0.95 0.342 -.2700028 .0936154 _cons | -1.466157 1.566907 -0.94 0.349 -4.537238 1.604923 -------------+---------------------------------------------------------------- /lnsig2u | 1.425657 .1601893 1.111692 1.739623 -------------+---------------------------------------------------------------- sigma_u | 2.039753 .1633733 1.743415 2.386461 rho | .8062238 .0250259 .7524444 .8506391 ------------------------------------------------------------------------------ Likelihood-ratio test of rho=0: chibar2(01) = 437.36 Prob >= chibar2 = 0.000 . . est store A . . mfx Marginal effects after xtprobit y = Linear prediction (predict) = .07982241 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- antidep | -.0002677 .00099 -0.27 0.787 -.002205 .00167 27.1569 mtx | -.00052 .00072 -0.72 0.469 -.001926 .000886 188.936 wageinc | .0000341 .00000 15.04 0.000 .00003 .000039 88834.5 age | .0392964 .0582 0.68 0.500 -.074773 .153366 49.9438 ab02*| -.273554 .46343 -0.59 0.555 -1.18186 .634754 .022235 ab36*| -.6163484 .35975 -1.71 0.087 -1.32145 .088756 .054162 ab79*| -.2830833 .34528 -0.82 0.412 -.959817 .393651 .062999 ab1014*| -.5803921 .25152 -2.31 0.021 -1.07336 -.087423 .117161 single*| -.7553261 .20681 -3.65 0.000 -1.16067 -.349983 .267674 ieland1*| .8304294 .85062 0.98 0.329 -.836747 2.49761 .960946 ieland2*| 1.481234 .93905 1.58 0.115 -.359268 3.32174 .023375 short*| .6990454 .21074 3.32 0.001 .285997 1.11209 .364025 higher*| -.0783054 .28368 -0.28 0.783 -.634311 .4777 .151368 agesq | -.0012635 .00064 -1.96 0.049 -.002524 -3.0e-06 2604.35 useofm~n | -.0004582 .00011 -4.00 0.000 -.000683 -.000233 846.506 u | -.0606001 .03714 -1.63 0.103 -.133402 .012202 6.34359 res | -.0881937 .09276 -0.95 0.342 -.270003 .093615 .292075 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 . . /*Panel probit same as Galarraga - with proxy*/ . xtprobit emp antidep mtx wageinc age ab02 ab36 ab79 ab1014 single ieland1 iel > and2 short higher agesq useofmedicin u Fitting comparison model: 114
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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
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 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: ho = 0.2 log likelihood = -681.4270
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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