Errata for Microeconometrics Using Stata

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(1) Chapter 4, p. 117, second line from bottom
. generate xfd = rchi2(10)/5 . generate xfd = rchi2(5)/5
(1) Chapter 4, p. 130, output at top
. 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
. 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
(1) Chapter 4, p. 140, first line of Stata program
. * Program for finite-sample properties of OLS: fixed regressors . * Program for finite-sample properties of OLS: power
(1) Chapter 4, p. 142, regress output
. 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
-----------+------------------------------------------------------------
. 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
-----------+------------------------------------------------------------
(1,2) Chapter 6, p. 190, second paragraph, third line
residuals from OLS of x1 on x2. residuals from OLS of x2 on x1.
(1) Chapter 6, p. 204, exercise 9, second sentence
The data are in mus06ivklingdata.dta. The data are in mus06klingdata.dta.
(1) Chapter 8, p. 261, unnumbered displayed equation
(1) Chapter 8, p. 262, Stata program and output
. * 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
. * 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
(1) Chapter 11, p. 378, third line of code
: 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
. 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
generate ystar = xb + u generate ystar = xb + uhat
(1,2) Chapter 15, p. 508, text below first equation in section 15.8.1
(1,2) Chapter 15, p. 514, second paragraph of section 15.9.6
The user-written gologit command The user-written gologit2 command
(1,2) Chapter 15, p. 519, fourth sentence of exercise 4
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
(1) Chapter 17, p. 595, Stata program and output
. * 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
-----------+------------------------------------------------------------
. * 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
-----------+------------------------------------------------------------
(1) Appendix B, p. 658, first line of code
: void poisson(real scalar todo,
: void poissonmle(real scalar todo,

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