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

More documents

Recommendations

Info

Prob > chi2 = 0.0000 Log pseudolikelihood = -42.543284 Pseudo R2 = 0.5114 ------------------------------------------------------------------------------ | Robust emp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- antidep | -.0000539 .0006492 -0.08 0.934 -.0013263 .0012186 mtx | .0011023 .0012318 0.89 0.371 -.0013119 .0035165 wageinc | .0000122 1.97e-06 6.18 0.000 8.32e-06 .000016 age | -.2625851 .0911092 -2.88 0.004 -.4411558 -.0840143 ab02 | 1.44743 .56637 2.56 0.011 .3373649 2.557495 ab36 | .3675009 .4242902 0.87 0.386 -.4640926 1.199094 ab79 | .3461866 .4063094 0.85 0.394 -.4501652 1.142538 ab1014 | .4932883 .3751264 1.31 0.189 -.2419459 1.228522 single | -.0175502 .3668996 -0.05 0.962 -.7366602 .7015598 ieland1 | .8474613 .5253228 1.61 0.107 -.1821524 1.877075 short | .1993185 .3254651 0.61 0.540 -.4385813 .8372183 higher | 1.245872 .5823958 2.14 0.032 .1043969 2.387346 agesq | .0036464 .0012108 3.01 0.003 .0012733 .0060195 useofmedicin | -.0001642 .0002774 -0.59 0.554 -.0007078 .0003795 u | -.1294659 .1335347 -0.97 0.332 -.3911891 .1322573 _cons | 3.474816 1.971356 1.76 0.078 -.3889709 7.338603 ------------------------------------------------------------------------------ note: 0 failures and 14 successes completely determined. . . mfx Marginal effects after probit y = Pr(emp) (predict) = .99800754 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- antidep | -3.40e-07 .00000 -0.08 0.934 -8.4e-06 7.8e-06 35.946 mtx | 6.97e-06 .00001 0.66 0.509 -.000014 .000028 219.566 wageinc | 7.70e-08 .00000 1.09 0.275 -6.1e-08 2.2e-07 209269 age | -.0016591 .00154 -1.08 0.282 -.004682 .001364 46.7596 ab02*| .0024685 .00268 0.92 0.357 -.002785 .007722 .048077 ab36*| .0015562 .00185 0.84 0.400 -.002068 .005181 .089744 ab79*| .0015073 .00194 0.78 0.437 -.00229 .005304 .096154 ab1014*| .0021172 .00236 0.90 0.369 -.002506 .006741 .179487 single*| -.0001125 .00237 -0.05 0.962 -.004759 .004534 .211538 ieland1*| .0184958 .02546 0.73 0.467 -.031395 .068387 .983974 short*| .0012468 .00258 0.48 0.629 -.003818 .006312 .461538 higher*| .004446 .00444 1.00 0.317 -.004255 .013147 .214744 agesq | .000023 .00002 1.10 0.271 -.000018 .000064 2293.48 useofm~n | -1.04e-06 .00000 -0.49 0.623 -5.2e-06 3.1e-06 650.074 u | -.000818 .00135 -0.61 0.544 -.003463 .001827 5.99904 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 . . quietly reg antidep mtx wageinc age ab02 ab36 ab79 ab1014 single ieland1 iela > nd2 short higher agesq useofmedicin u if yr==2003 . 117
. predict ohat, resid . . probit emp antidep mtx wageinc age ab02 ab36 ab79 ab1014 single ieland1 ielan > d2 short higher agesq useofmedicin u ohat if yr==2003, robust note: ieland2 != 0 predicts success perfectly ieland2 dropped and 4 obs not used note: ab79 dropped due to collinearity Iteration 0: log pseudolikelihood = -87.073703 Iteration 1: log pseudolikelihood = -56.307727 Iteration 2: log pseudolikelihood = -46.213664 Iteration 3: log pseudolikelihood = -43.143476 Iteration 4: log pseudolikelihood = -42.568445 Iteration 5: log pseudolikelihood = -42.54334 Iteration 6: log pseudolikelihood = -42.543284 Probit estimates Number of obs = 312 Wald chi2(15) = 59.45 Prob > chi2 = 0.0000 Log pseudolikelihood = -42.543284 Pseudo R2 = 0.5114 ------------------------------------------------------------------------------ | Robust emp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- antidep | .0057641 .0067392 0.86 0.392 -.0074444 .0189727 mtx | .0004599 .0015412 0.30 0.765 -.0025607 .0034805 wageinc | .0000126 2.12e-06 5.94 0.000 8.44e-06 .0000168 age | -.2817436 .0961798 -2.93 0.003 -.4702527 -.0932346 ab02 | 1.754321 .6310997 2.78 0.005 .517388 2.991253 ab36 | .3052985 .4467393 0.68 0.494 -.5702945 1.180891 ab1014 | .5281453 .368421 1.43 0.152 -.1939466 1.250237 single | -.164611 .3733759 -0.44 0.659 -.8964144 .5671924 ieland1 | 1.085277 .5388642 2.01 0.044 .0291226 2.141431 short | .4512945 .4076703 1.11 0.268 -.3477246 1.250314 higher | 1.392705 .6029752 2.31 0.021 .210895 2.574514 agesq | .0039731 .0013103 3.03 0.002 .0014049 .0065413 useofmedicin | -.0005588 .0004696 -1.19 0.234 -.0014792 .0003615 u | -.1288846 .1335004 -0.97 0.334 -.3905405 .1327714 ohat | -.005818 .0068284 -0.85 0.394 -.0192014 .0075655 _cons | 3.386611 1.961147 1.73 0.084 -.4571662 7.230388 ------------------------------------------------------------------------------ note: 0 failures and 14 successes completely determined. . . mfx Marginal effects after probit y = Pr(emp) (predict) = .99800754 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- antidep | .0000364 .00005 0.66 0.506 -.000071 .000144 35.946 mtx | 2.91e-06 .00001 0.28 0.777 -.000017 .000023 219.566 wageinc | 7.96e-08 .00000 1.09 0.276 -6.4e-08 2.2e-07 209269 118
Page 1 and 2:
Estimating the Impact of An
Page 3 and 4:
Abstract This thesis aims at analys
Page 5 and 6:
1 Introduction According to the Wor
Page 7 and 8:
consisting of lack of data on the d
Page 9 and 10:
experience symptoms of depression o
Page 11 and 12:
(SSRI’s), also called the new ant
Page 13 and 14:
heumatologists. But since descripti
Page 15 and 16:
H > 0 or a corner solution, H = 0 .
Page 17 and 18:
employers more reluctant to hire ne
Page 19 and 20:
One of the most cited models when a
Page 21 and 22:
One of the factors greatly influenc
Page 23 and 24:
Galarraga et al. (2006) have chosen
Page 25 and 26:
channel runs through enhanced compl
Page 27 and 28:
3.2 The effect of antidepressants o
Page 29 and 30:
of patients receiving appropriate c
Page 31 and 32:
Inspired by Galarraga et al. (2006)
Page 33 and 34:
are used by other patients groups,
Page 35 and 36:
Consequently I am dealing with a se
Page 37 and 38:
constructed, and will be explained
Page 39 and 40:
As mentioned in chapter 1, one of t
Page 41 and 42:
Number of observations 2364 252 211
Page 43 and 44:
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 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: -----------------------------------
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
show all

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

Create successful ePaper yourself

Delete template?

Save as template?