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

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*creation of a selection indicator, which should be used in the test for sample selection bias*/ data selection; set b.finaldata2; if MTX=0 then s=0; else s=1; s_1=lag(s); if (udtrnr NE lag(udtrnr) or lag(yr) ne yr-1) then s_1=0; if (udtrnr NE lag(udtrnr) and yr=1995) then s_1=.; data ud.finaldata5; set selection; /*creating a dataset only with people in the labour force and for the years 1999-2003*/ data test; set b.finaldata3; if OLM=1 then delete; data ud.finaldata6; set test; 93
Estimation results Probit yr 2003 - Table 3 & 5: . use /akf/702517/ycb2517/Initial/finaldata2.dta . /*Same estimation as Galarraga on year 2003, with some extra variables though > - robust std errors - with proxy Generalised residual*/ . quietly probit ad_dummy mtx wageinc age ab02 ab36 ab79 ab1014 single ieland1 > ieland2 short higher agesq useofmedicin u if yr==2003, robust . . 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 . . probit emp ad_dummy mtx wageinc age ab02 ab36 ab79 ab1014 single ieland1 iela > nd2 short higher agesq useofmedicin u res if yr==2003, robust Iteration 0: log pseudolikelihood = -402.02192 Iteration 1: log pseudolikelihood = -185.20441 Iteration 2: log pseudolikelihood = -129.53324 Iteration 3: log pseudolikelihood = -115.26588 Iteration 4: log pseudolikelihood = -112.33693 Iteration 5: log pseudolikelihood = -112.12951 Iteration 6: log pseudolikelihood = -112.12826 Iteration 7: log pseudolikelihood = -112.12826 Probit estimates Number of obs = 580 Wald chi2(17) = 164.35 Prob > chi2 = 0.0000 Log pseudolikelihood = -112.12826 Pseudo R2 = 0.7211 ------------------------------------------------------------------------------ | Robust emp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- ad_dummy | -.3763669 3.360247 -0.11 0.911 -6.96233 6.209597 mtx | .0006738 .0009907 0.68 0.496 -.0012679 .0026155 wageinc | .0000185 1.85e-06 10.04 0.000 .0000149 .0000222 age | -.0475746 .0504891 -0.94 0.346 -.1465314 .0513823 ab02 | .570373 .43402 1.31 0.189 -.2802906 1.421037 ab36 | -.1215033 .4094679 -0.30 0.767 -.9240457 .6810391 ab79 | .2804363 .3720277 0.75 0.451 -.4487246 1.009597 ab1014 | .0891149 .3398843 0.26 0.793 -.5770461 .7552759 single | -.0507785 .2309565 -0.22 0.826 -.5034449 .4018878 ieland1 | .3491471 .5142155 0.68 0.497 -.6586967 1.356991 94
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
Page 45 and 46: Figure 7 The distribution of the us
Page 47 and 48: Figure 9 depicts the defined daily
Page 49 and 50: 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: antidep_lastyr=lag(AD_dummy); if (u
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 and 142: ieland1*| .2476048 .11071 2.24 0.02
Page 143 and 144: Random-effects tobit regression Num
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