Chapter 18 Count, Fractional, and Other Nonnegative Responses
Examples
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name: SN
log: \iiexample18.smcl
log type: smcl
opened on: 12 May 2020, 10:32:08
. **********************************************
. * Solomon Negash - Examples
. * Wooldridge (2010). Economic Analysis of Cross-Section and Panel Data. 2nd ed.
. * STATA Program, version 16.1.
. * Chapter 18 - Count, Fractional, and Other Nonnegative Responses
. **********************************************
. // Example 18.1 (Effects of Education on Fertility)
. bcuse fertil2, clear nodesc
. eststo Linear: reg children educ age agesq evermarr urban electric tv
Source | SS df MS Number of obs = 4,358
-------------+---------------------------------- F(7, 4350) = 893.91
Model | 12688.9349 7 1812.70499 Prob > F = 0.0000
Residual | 8821.09719 4,350 2.02783843 R-squared = 0.5899
-------------+---------------------------------- Adj R-squared = 0.5892
Total | 21510.0321 4,357 4.93689055 Root MSE = 1.424
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children | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | -.0644086 .0063199 -10.19 0.000 -.0767987 -.0520184
age | .2724736 .017019 16.01 0.000 .2391077 .3058395
agesq | -.0019067 .000274 -6.96 0.000 -.0024438 -.0013696
evermarr | .6822725 .052167 13.08 0.000 .5799986 .7845463
urban | -.2278933 .0458653 -4.97 0.000 -.3178126 -.137974
electric | -.2617394 .0758688 -3.45 0.001 -.410481 -.1129979
tv | -.2499509 .0901474 -2.77 0.006 -.4266858 -.0732161
_cons | -3.39384 .2445496 -13.88 0.000 -3.873281 -2.914398
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. eststo Poisson: poisson children educ age agesq evermarr urban electric tv, r nolog
Poisson regression Number of obs = 4,358
Wald chi2(7) = 6261.94
Prob > chi2 = 0.0000
Log pseudolikelihood = -6497.0599 Pseudo R2 = 0.3219
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| Robust
children | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | -.0216645 .0025918 -8.36 0.000 -.0267442 -.0165847
age | .3373308 .0094473 35.71 0.000 .3188144 .3558473
agesq | -.0041158 .000144 -28.57 0.000 -.0043981 -.0038335
evermarr | .314751 .0232117 13.56 0.000 .269257 .360245
urban | -.0860549 .0200471 -4.29 0.000 -.1253465 -.0467633
electric | -.1205347 .0372925 -3.23 0.001 -.1936266 -.0474428
tv | -.1447046 .0438055 -3.30 0.001 -.2305617 -.0588475
_cons | -5.374829 .1477633 -36.37 0.000 -5.66444 -5.085219
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. margins,
Predictive margins Number of obs = 4,358
Model VCE : Robust
Expression : Predicted number of events, predict()
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| Delta-method
| Margin Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_cons | 2.267554 .0213693 106.11 0.000 2.225671 2.309437
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. margins, dydx(educ tv)
Average marginal effects Number of obs = 4,358
Model VCE : Robust
Expression : Predicted number of events, predict()
dy/dx w.r.t. : educ tv
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| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf. Interval]
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educ | -.0491254 .005968 -8.23 0.000 -.0608223 -.0374284
tv | -.3281255 .0993952 -3.30 0.001 -.5229365 -.1333144
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. estout Linear Poisson, cells(b(nostar fmt(4)) se(par fmt(4))) stats(ll r2 r2_p N, ///
fmt(%9.0g %9.0g %9.0g) labels(Log-Likelihood R-Squared Psuedo_R-Sqaured N )) ///
varlabels(_cons constant) varwidth(10) ti("Table 18.1 OLS and Poisson Estimates of ///
a Fertility Equation: (children)")
Table 18.1 OLS and Poisson Estimates of a Fertility Equation: (children)
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Linear Poisson
b/se b/se
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main
educ -0.0644 -0.0217
(0.0063) (0.0026)
age 0.2725 0.3373
(0.0170) (0.0094)
agesq -0.0019 -0.0041
(0.0003) (0.0001)
evermarr 0.6823 0.3148
(0.0522) (0.0232)
urban -0.2279 -0.0861
(0.0459) (0.0200)
electric -0.2617 -0.1205
(0.0759) (0.0373)
tv -0.2500 -0.1447
(0.0901) (0.0438)
constant -3.3938 -5.3748
(0.2445) (0.1478)
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Log-Like~d -7720.219 -6497.06
R-Squared .5899078
Psuedo_R~d .3218618
N 4358 4358
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. est clear
. // Example 18.2 (Is Education Endogenous in the Fertility Equation?)
. reg educ frsthalf age agesq evermarr urban electric tv
Source | SS df MS Number of obs = 4,358
-------------+---------------------------------- F(7, 4350) = 208.02
Model | 16850.0414 7 2407.14877 Prob > F = 0.0000
Residual | 50336.75 4,350 11.5716667 R-squared = 0.2508
-------------+---------------------------------- Adj R-squared = 0.2496
Total | 67186.7914 4,357 15.4204249 Root MSE = 3.4017
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educ | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
frsthalf | -.6361072 .1038091 -6.13 0.000 -.8396259 -.4325885
age | -.0702853 .0406438 -1.73 0.084 -.1499678 .0093971
agesq | -.0008118 .0006544 -1.24 0.215 -.0020947 .0004711
evermarr | -.8023536 .1241223 -6.46 0.000 -1.045697 -.5590106
urban | .8637296 .108786 7.94 0.000 .6504536 1.077006
electric | 1.977712 .1787579 11.06 0.000 1.627255 2.328168
tv | 2.714666 .2113782 12.84 0.000 2.300257 3.129075
_cons | 8.20343 .5752279 14.26 0.000 7.075691 9.33117
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. predict v2, r
(3 missing values generated)
. poisson children educ age agesq evermarr urban electric tv v2, r nolog
Poisson regression Number of obs = 4,358
Wald chi2(8) = 6267.44
Prob > chi2 = 0.0000
Log pseudolikelihood = -6496.7706 Pseudo R2 = 0.3219
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| Robust
children | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | -.0459847 .0295654 -1.56 0.120 -.1039319 .0119625
age | .3357196 .0097048 34.59 0.000 .3166986 .3547405
agesq | -.0041373 .0001451 -28.52 0.000 -.0044216 -.0038529
evermarr | .2941007 .0343224 8.57 0.000 .22683 .3613714
urban | -.0647957 .0323494 -2.00 0.045 -.1281994 -.001392
electric | -.0711916 .0688407 -1.03 0.301 -.2061168 .0637337
tv | -.0780223 .0937508 -0.83 0.405 -.2617705 .1057258
v2 | .024515 .0296235 0.83 0.408 -.033546 .0825761
_cons | -5.18482 .2767832 -18.73 0.000 -5.727305 -4.642335
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. log close
name: SN
log: iiexample18.smcl
log type: smcl
opened on: 12 May 2020, 10:32:11
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