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

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y02 | -.1639108 .129876 -1.26 0.207 -.4184631 .0906414 y03 | -.1048076 .1270246 -0.83 0.409 -.3537713 .1441561 _cons | -.2114005 .6340713 -0.33 0.739 -1.454157 1.031356 ------------------------------------------------------------------------------ note: 0 failures and 32 successes completely determined. . . mfx Marginal effects after probit y = Pr(emp) (predict) = .64873296 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- cons_a~p | -.0000464 .00004 -1.09 0.274 -.00013 .000037 73.8388 mtx | .0001417 .00015 0.94 0.345 -.000152 .000436 202.322 wageinc | 6.30e-06 .00000 31.06 0.000 5.9e-06 6.7e-06 100005 age | -.0047692 .009 -0.53 0.596 -.022417 .012879 50.2382 ab02*| .0043622 .08107 0.05 0.957 -.154539 .163264 .024112 ab36*| -.0746384 .07035 -1.06 0.289 -.212527 .06325 .052876 ab79*| -.0626892 .05959 -1.05 0.293 -.179477 .054098 .06599 ab1014*| -.0700128 .05149 -1.36 0.174 -.170924 .030898 .12225 single*| -.1129074 .03923 -2.88 0.004 -.189791 -.036024 .260152 ieland1*| .1707017 .08975 1.90 0.057 -.005203 .346607 .959814 ieland2*| .2244687 .07385 3.04 0.002 .07972 .369217 .021997 short*| .0604144 .03259 1.85 0.064 -.003469 .124298 .376904 higher*| -.1148051 .06688 -1.72 0.086 -.245881 .016271 .158629 agesq | -.0000385 .0001 -0.38 0.704 -.000237 .00016 2630.49 useofm~n | -.0001036 .00003 -4.03 0.000 -.000154 -.000053 870.992 u | -.016388 .01328 -1.23 0.217 -.042408 .009632 5.4599 y00*| -.0280648 .04955 -0.57 0.571 -.125175 .069045 .18824 y01*| -.0483076 .05268 -0.92 0.359 -.151552 .054937 .192047 y02*| -.0617507 .04965 -1.24 0.214 -.159067 .035566 .217005 y03*| -.0392401 .048 -0.82 0.414 -.133326 .054846 .245347 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 Random effects tobit – Table 10: . use /akf/702517/ycb2517/Initial/finaldata4.dta . /*Panel tobit */ . xttobit daysemp ad_dummy mtx wageinc age ab02 ab36 ab79 ab1014 single ieland1 > ieland2 short higher agesq useofmedicin u, i(udtrnr) ll(0) Obtaining starting values for full model: Iteration 0: log likelihood = -8055.4181 Iteration 1: log likelihood = -8052.1085 Iteration 2: log likelihood = -8052.0875 Iteration 3: log likelihood = -8052.0875 Fitting full model: Iteration 0: log likelihood = -7944.7443 Iteration 1: log likelihood = -7943.8681 Iteration 2: log likelihood = -7943.865 139
Random-effects tobit regression Number of obs = 1388 Group variable (i): udtrnr Number of groups = 573 Random effects u_i ~ Gaussian Obs per group: min = 1 avg = 2.4 max = 9 Wald chi2(16) = 136.36 Log likelihood = -7943.865 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ daysemp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- ad_dummy | 7.643365 10.05946 0.76 0.447 -12.07281 27.35954 mtx | -.0201829 .0232258 -0.87 0.385 -.0657046 .0253388 wageinc | .0002162 .000027 8.02 0.000 .0001634 .0002691 age | 2.644079 1.932902 1.37 0.171 -1.144339 6.432497 ab02 | 8.147091 14.40895 0.57 0.572 -20.09393 36.38811 ab36 | -8.610262 10.15808 -0.85 0.397 -28.51974 11.29921 ab79 | -6.935865 9.442306 -0.73 0.463 -25.44244 11.57071 ab1014 | 9.895997 7.554595 1.31 0.190 -4.910737 24.70273 single | -9.387915 6.668741 -1.41 0.159 -22.45841 3.682576 ieland1 | 68.63038 26.46559 2.59 0.010 16.75877 120.502 ieland2 | 59.00272 36.73321 1.61 0.108 -12.99306 130.9985 short | 3.471673 6.538162 0.53 0.595 -9.34289 16.28624 higher | 5.563477 7.842209 0.71 0.478 -9.80697 20.93392 agesq | -.0155418 .022527 -0.69 0.490 -.0596939 .0286103 useofmedicin | .0002654 .0047047 0.06 0.955 -.0089556 .0094864 u | .7096135 1.303319 0.54 0.586 -1.844844 3.264071 _cons | 123.3309 49.31488 2.50 0.012 26.67547 219.9862 -------------+---------------------------------------------------------------- /sigma_u | 41.95415 3.763202 11.15 0.000 34.57841 49.32989 /sigma_e | 73.53906 1.883758 39.04 0.000 69.84696 77.23115 -------------+---------------------------------------------------------------- rho | .2455518 .0386239 .176426 .327063 ------------------------------------------------------------------------------ Observation summary: 1352 uncensored observations 36 left-censored observations 0 right-censored observations . . mfx Marginal effects after xttobit y = Fitted values (predict) = 328.23003 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- ad_dummy*| 7.643365 10.059 0.76 0.447 -12.0728 27.3595 .061239 mtx | -.0201829 .02323 -0.87 0.385 -.065705 .025339 184.138 wageinc | .0002162 .00003 8.02 0.000 .000163 .000269 215870 age | 2.644079 1.9329 1.37 0.171 -1.14434 6.4325 46.3372 ab02*| 8.147091 14.409 0.57 0.572 -20.0939 36.3881 .0317 ab36*| -8.610262 10.158 -0.85 0.397 -28.5197 11.2992 .071326 ab79*| -6.935865 9.44231 -0.73 0.463 -25.4424 11.5707 .082133 ab1014*| 9.895997 7.55459 1.31 0.190 -4.91074 24.7027 .157061 140
Page 1 and 2:
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
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data medold; set b.lmdb_pct10_2003;
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if Last.aar then output; /*output o
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KEEP AAR ALDER ANC02 ANC36 ANC79 AN
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set b.register; yr=0+aar; DROP aar;
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HIGHER='Middle-range and higher edu
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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 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: ieland1*| .2476048 .11071 2.24 0.02
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