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

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inclusion of time dummies is likely to reduce some of the bias present in a pooled estimation compared to panel data estimation. The coefficient on the dummy indicating antidepressant use is negative, but again it is not significant. Column 3 presents the results from the random effects probit estimation, exploiting the panel nature of the data. The variable of interest has a negative coefficient, but is not significant. Some of the other explanatory variables, which are included in the estimations as control variables, become significant when estimated with pooled probit or random effects probit methods compared to the probit estimation on a single year. The pseudo R 2 is reported as a goodness of fit measure. 69 The results in table 3 show that all three estimations have a relatively high coefficient of determination, but the pseudo R 2 is decreasing when more elaborate models and a larger dataset are applied. This counteracts what one would expect, since presumably more elaborate models exploit the data better. But it should be kept in mind that the pseudo R 2 does not say anything about the correctness of the applied method. As mentioned throughout the thesis, the explanatory variable is possibly endogenous. To find out whether this is the case, a test of endogeneity is performed for the indicator of antidepressant use on all three estimations following the Rivers-Vuong method presented in chapter 5. Since I am dealing with a binary explanatory variable the generalised residuals are calculated after the auxiliary regression in the first step and included in the main equation in the second step and the Likelihood Ratio test is performed. The results from the Likelihood Ratio test are shown in table 4. Probit yr 2003 (1) Table 4 Likelihood Ratio test Pooled probit (2) Random effects probit (3) χ 2 (1) 0.01[0.9379] 0.33[0.5636] 0.02[0.8802] As can be seen from the table, the null hypothesis of exogeneity cannot be rejected in all three estimations. Thus it cannot be rejected that the indicator of antidepressant use is exogenous. Therefore it appears that the possible endogeneity of the explanatory variable, which was first presupposed, does not cause problems in the estimations. 69 The pseudo R 2 , which also is called McFadden’s R 2 is computed as follows, 2 R = 1− likelihood of the full model and lR is the log likelihood of a model with a constant only. l UR l R , where l UR is the log 53
As established in chapter 4, the panel is unbalanced due to the way that the sample population is defined. In the pooled probit the reappearance of all observation units is coincidental by assumption and therefore ignored. The random effects probit model in contrast, exploits the reappearance of the same observation units. <strong>An</strong> unbalanced panel can bias the results when applying panel data methods, if the unbalanced panel is caused by sample selection. Therefore a test for sample selection is performed by including a lag of the selection indicator in the equation of interest, as described in chapter 5. This test is performed on the random effects probit estimation, and it is included in the appendices. With a coefficient of the lag of the selection indicator of -0.09917 and a standard error of 0.1625 the null hypothesis of no sample selection bias cannot be rejected. 70 This implies that the results are not biased due to sample selection. In chapter 5 a measure of ρ, the magnitude of the unobserved effect, was defined. When conducting the random effects probit estimation in STATA, an estimate of ρ is automatically reported. The estimate of ρ for the random effects estimation displayed in column 3 in table 3 is equal to 0.8065 with a standard error of 0.0249. A likelihood-ratio test testing whether ρ is equal to 0 is rejected at a significance level of 1 %. It can therefore not be rejected that there is an unobserved effect present in the data. As mentioned in chapter 5, the coefficients of a probit model can only be interpreted regarding their direction. In order to draw conclusions about the magnitude of the coefficients, marginal effects are calculated. The marginal effects are evaluated at the mean, as described in chapter 5. Table 5 displays the marginal effects for the estimations with a binary antidepressant indicator. Table 5 Marginal effects - a binary antidepressant indicator Probit yr 2003 (1) Pooled probit (2) Random effects probit (3) variable dy/dx s.e. dy/dx s.e. dy/dx s.e. ad_dummy† -0.0063 0.0899 -0.0690 0.0491 -0.3334 0.2447 mtx 0.0002 0.0003 0.0001 0.0001 -0.0005 0.0007 wageinc 0.0000*** 0.0000 0.0000*** 0.0000 0.0000*** 0.0000 age -0.0142 0.0151 -0.0008 0.0073 0.0400 0.0584 ab02† 0.1382* 0.0748 -0.0667 0.0854 -0.2722 0.4634 ab36† -0.0309 0.1122 -0.1267** 0.0596 -0.6195* 0.3603 70 The p-value is 0.542. 54
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: antidepressant use are displayed in
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:
-----------------------------------
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. . probit emp antidep mtx wageinc
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ho = 0.2 log likelihood = -681.4270
Page 117 and 118:
ab79 | -.2830833 .3452789 -0.82 0.4
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-----------------------------------
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. 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
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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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