Errata for Microeconometrics Using Stata
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| (1) | Chapter 4, p. 117, second line from bottom |
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| . generate xfd = rchi2(10)/5 | . generate xfd = rchi2(5)/5 |
| (1) | Chapter 4, p. 130, output at top |
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. summarize y1 y2
Variable | Obs Mean Std. Dev. Min Max
-------------+------------------------------------------------------
y1 | 1000 10.08618 2.082605 3.108118 16.40892
y2 | 1000 20.20292 2.999583 10.12452 29.79675
. correlate y1 y2
| y1 y2
-------------+------------------
y1 | 1.0000
y2 | 0.5553 1.0000
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. summarize y1 y2
Variable | Obs Mean Std. Dev. Min Max
-------------+------------------------------------------------------
y1 | 1000 10.0093 1.985447 3.176768 16.38992
y2 | 1000 20.07195 2.941012 9.202241 29.94806
. correlate y1 y2
| y1 y2
-------------+------------------
y1 | 1.0000
y2 | 0.4724 1.0000
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| (1) | Chapter 4, p. 140, first line of Stata program |
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| . * Program for finite-sample properties of OLS: fixed regressors | . * Program for finite-sample properties of OLS: power |
| (1) | Chapter 4, p. 142, regress output |
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. regress y x, noconstant
Source | SS df MS Number of obs = 10000
-----------+------------------------------- F( 1, 9999) =42724.08
Model | 81730.3312 1 81730.3312 Prob > F = 0.0000
Residual | 19127.893 9999 1.9129806 R-squared = 0.8103
-----------+------------------------------- Adj R-squared = 0.8103
Total | 100858.224 10000 10.0858224 Root MSE = 1.3831
-----------+------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-----------+------------------------------------------------------------
x | .9001733 .004355 206.70 0.000 .8916366 .90871
-----------+------------------------------------------------------------
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. regress y x, noconstant
Source | SS df MS Number of obs = 10000
-----------+------------------------------- F( 1, 9999) =41969.38
Model | 80231.7664 1 80231.7664 Prob > F = 0.0000
Residual | 191114.8283 9999 1.91167399 R-squared = 0.8076
-----------+------------------------------- Adj R-squared = 0.8076
Total | 99346-5946 10000 9.93465946 Root MSE = 1.3826
-----------+------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-----------+------------------------------------------------------------
x | .8997697 .004392 204.86 0.000 .8911604 .9083789
-----------+------------------------------------------------------------
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| (1,2) | Chapter 6, p. 190, second paragraph, third line |
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| residuals from OLS of x1 on x2. | residuals from OLS of x2 on x1. |
| (1) | Chapter 6, p. 204, exercise 9, second sentence |
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| The data are in mus06ivklingdata.dta. | The data are in mus06klingdata.dta. |
| (1) | Chapter 8, p. 261, unnumbered displayed equation |
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| (1) | Chapter 8, p. 262, Stata program and output |
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. * Robust Hausman test using method of Wooldridge (2002)
. quietly xtreg lwage $xlist, re
. scalar theta = e(theta)
. global yandxforhausman lwage exp wexp2 wks
. sort id
. foreach x of varlist $yandxforhausman{
2. by id: egen mean`x' = mean(`x')
3. generate md`x' = `x' - mean`x'
4. generate red`x' = `x' - theta*mean`x'
5. }
. quietly regress redlwage redexp redexp2 redwks mdexp mdexp2 mdwks
. test mdexp mdexp2 mdwks
( 1) mdexp = 0
( 2) mdexp2 = 0
( 3) mdwks = 0
F( 3, 4158) = 848.39
Prob > F = 0.0000
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. * Robust Hausman test using method of Wooldridge (2002)
. quietly xtreg lwage $xlist, re
. scalar theta = e(theta)
. global yandxforhausman lwage exp wexp2 wks ed
. sort id
. foreach x of varlist $yandxforhausman{
2. by id: egen mean`x' = mean(`x')
3. generate md`x' = `x' - mean`x'
4. generate red`x' = `x' - theta*mean`x'
5. }
. quietly regress redlwage redexp redexp2 redwks reded mdexp mdexp2 mdwks,
> vce(cluster id)
. test mdexp mdexp2 mdwks
( 1) mdexp = 0
( 2) mdexp2 = 0
( 3) mdwks = 0
F( 3, 594) = 597.47
Prob > F = 0.0000
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| (1) | Chapter 11, p. 378, third line of code |
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: void poisson(todo, b, y, X, lndensity, g, H) |
: void poissonmle(todo, b, y, X, lndensity, g, H) |
| (1) | Chapter 12, p. 397, Stata commands |
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. use mus10data.dta, clear . quietly poisson ... |
. use mus10data.dta, clear . keep if year02==1 . quietly poisson ... |
| (1,2) | Chapter 13, p. 439, third line from bottom in the code |
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| generate ystar = xb + u | generate ystar = xb + uhat |
| (1,2) | Chapter 15, p. 508, text below first equation in section 15.8.1 |
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| (1,2) | Chapter 15, p. 514, second paragraph of section 15.9.6 |
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| The user-written gologit command | The user-written gologit2 command |
| (1,2) | Chapter 15, p. 519, fourth sentence of exercise 4 |
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| Obtain the MEs for the predicted probability of excellent health for the MNL model ... | Obtain the MEs for the predicted probability of excellent health for the ordered probit ... |
| (1,2) | Chapter 16, p. 548, first and second equations in table 16.2 |
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| (1) | Chapter 17, p. 595, Stata program and output |
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. * Program and bootstrap for Poisson two-step estimator
. program endogtwostep, eclass
1. version 10.1
2. tempname b
3. tempvar lpuhat
4. regress private $xlist2 income ssiratio
5. predict `lpuhat', residual
6. poisson docvis private $xlist2 lpuhat
7. matrix `b' = e(b)
8. ereturn post `b'
9. end
. bootstrap _b, reps(400) seed(10101) nodots nowarn: endogtwostep
Bootstrap results Number of obs = 3677
Replications = 400
-----------+------------------------------------------------------------
| Observed Bootstrap Normal-based
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-----------+------------------------------------------------------------
private | .5505541 .2406273 2.29 0.022 .0789334 1.022175
medicaid | .2628822 .1151473 2.28 0.022 .0371976 .4885669
age | .3350604 .0673445 4.98 0.000 .2030677 .4670532
age2 | -.0021923 .0004444 -4.93 0.000 -.0030634 -.0013213
educyr | .018606 .0078638 2.37 0.018 .0031934 .0340187
actlim | .2053417 .0407465 5.04 0.000 .1254802 .2852033
totchr | .24147 .0131985 18.30 0.000 .2156014 .2673387
lpuhat | -.4166838 .2469318 -1.69 0.092 -.9006614 .0672937
_cons | -11.90647 2.566368 -4.64 0.000 -16.93646 -6.876476
-----------+------------------------------------------------------------
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. * Program and bootstrap for Poisson two-step estimator
. program endogtwostep, eclass
1. version 10.1
2. tempname b
3. capture drop lpuhat2
4. regress private $xlist2 income ssiratio
5. predict lpuhat2, residual
6. poisson docvis private $xlist2 lpuhat2
7. matrix `b' = e(b)
8. ereturn post `b'
9. end
. bootstrap _b, reps(400) seed(10101) nodots nowarn: endogtwostep
Bootstrap results Number of obs = 3677
Replications = 400
-----------+------------------------------------------------------------
| Observed Bootstrap Normal-based
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-----------+------------------------------------------------------------
private | .5505541 .2567815 2.14 0.032 .0472716 1.053837
medicaid | .2628822 .1205813 2.18 0.029 .0265473 .4992172
age | .3350604 .0707275 4.74 0.000 .1964371 .4736838
age2 | -.0021923 .0004667 -4.70 0.000 -.0031071 -.0012776
educyr | .018606 .0083042 2.24 0.025 .0023301 .034882
actlim | .2053417 .0412756 4.97 0.000 .124443 .2862405
totchr | .24147 .0134522 17.95 0.000 .2151042 .2678359
lpuhat | -.4166838 .2617964 -1.59 0.111 -.9297953 .0964276
_cons | -11.90647 2.698704 -4.41 0.000 -17.19583 -6.617104
-----------+------------------------------------------------------------
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| (1) | Appendix B, p. 658, first line of code |
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: void poisson(real scalar todo, |
: void poissonmle(real scalar todo, |
