Download - Modelo Provit Econometria
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NOMBRE DEL DOCENTE
Mgr. RENE PAREDES MAMANI
NOMBREANTONI HINOJOSA FRANCO
CURSO
METODOS CUANTITAVOS APLICADOS
MODELO PROVIT
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. use "D:\MAESTRIA\DATOS\REGRESIONES\PRACTICAS\practica.dta", clear
. regress salary sales
Source | SS df MS Number of obs = 177
-------------+------------------------------------------ F( 1, 175) = 29.58
Model | 8784947.36 1 8784947.36 Prob > F = 0.0000
Residual | 51981017.4 175 297034.385 R-squared = 0.1446-------------+-------------------------------------------- Adj R-squared = 0.1397
Total | 60765964.7 176 345261.163 Root MSE = 545.01
------------------------------------------------------------------------------
salary | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
sales | .0366937 .0067472 5.44 0.000 .0233773 .0500102
_cons | 736.3552 47.3843 15.54 0.000 642.837 829.8735
------------------------------------------------------------------------------
. scatter salary sales
. scatter salary sales
. egen m_salary=mean(salary)
. egen m_sales=mean(sales)
. gen p_sales=_b[_cons]+_b[sales]*sales
. sort sales
. twoway line p_sales sales, saving(g_sales.gph, replace)
(note: file g_sales.gph not found)
(file g_sales.gph saved)
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. regress salary sales profits ceoten, noconstant
Source | SS df MS Number of obs = 177
-------------+------------------------------------------------ F( 3, 174) = 92.78
Model | 119048425 3 39682808.4 Prob > F = 0.0000
Residual | 74418186.8 174 427690.729 R-squared = 0.6153
-------------+------------------------------------------------ Adj R-squared = 0.6087
Total | 193466612 177 1093031.71 Root MSE = 653.98
----------------------------------------------------------------------------------------------------
salary | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+-------------------------------------------------------------------------------------
sales | .04367 .0131256 3.33 0.001 .0177642 .0695759
profits | .3626016 .2026449 1.79 0.075 -.0373568 .76256
ceoten | 50.9095 4.903747 10.38 0.000 41.23101 60.58798
-----------------------------------------------------------------------------------------------------
. regress salary sales profits ceoten
Source | SS df MS Number of obs = 177
-------------+------------------------------------------ F( 3, 173) = 13.80
Model | 11733215 3 3911071.67 Prob > F = 0.0000
Residual | 49032749.7 173 283426.299 R-squared = 0.1931
-------------+-------------------------------------------- Adj R-squared = 0.1791
Total | 60765964.7 176 345261.163 Root MSE = 532.38
------------------------------------------------------------------------------------------------
salary | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------------------------
sales | .0196671 .0109819 1.79 0.075 -.0020087 .0413428
profits | .3410285 .1649803 2.07 0.040 .015395 .666662
ceoten | 13.29683 5.632999 2.36 0.019 2.178577 24.41508
_cons | 619.8005 65.4907 9.46 0.000 490.5368 749.0642
----------------------------------------------------------------------------------------------------
. egen m_salary=mean(salary)
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m_salary already defined
r(110);
. egen m_sales=mean(sales)
m_sales already defined
r(110);
. egen m_profits=mean(profits)
. egen m_ceoten=mean(ceoten)
. egen m_cons=mean(_cons)
. gen p_sales=_b[_cons] + _b[sales]*sales + _b[profits]*m_profits + _b[ceoten]*m_ceoten
p_sales already defined
r(110);
. sort sales
. twoway line p_sales sales, saving(g_sales.gph, replace)
(file g_sales.gph saved)
. gen p_profits=_b[_cons] + _b[sales]*m_sales + _b[profits]*profits + _b[ceoten]*m_ceoten
. sort profits
. twoway line p_profits profits, saving(g_profits.gph, replace)
(note: file g_profits.gph not found)
(file g_profits.gph saved)
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. gen p_ceoten=_b[_cons] + _b[sales]*m_sales + _b[profits]*m_profits + _b[ceoten]*ceoten
. sort ceoten
. twoway line p_ceoten ceoten, saving(g_ceotem.gph, replace)
(note: file g_ceotem.gph not found)
(file g_ceotem.gph saved)
. gen lsalary=log(salary)
. gen lsales=log(sales)
. gen lprofits=log(profits)
(9 missing values generated)
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. gen lceoten=log(ceoten)
(5 missing values generated)
. regress lsalary sales
Source | SS df MS Number of obs = 177
-------------+---------------------------------------------- F( 1, 175) = 35.33
Model | 10.8581306 1 10.8581306 Prob > F = 0.0000
Residual | 53.7880825 175 .307360471 R-squared = 0.1680
-------------+---------------------------------------------- Adj R-squared = 0.1632
Total | 64.6462131 176 .367308029 Root MSE = .5544
------------------------------------------------------------------------------
lsalary | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
sales | .0000408 6.86e-06 5.94 0.000 .0000272 .0000543_cons | 6.438865 .0482009 133.58 0.000 6.343736 6.533995
------------------------------------------------------------------------------
. mfx
Marginal effects after regress
y = Fitted values (predict)
= 6.5828476
------------------------------------------------------------------------------
variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X
---------+--------------------------------------------------------------------
sales | .0000408 .00001 5.94 0.000 .000027 .000054 3529.46------------------------------------------------------------------------------
. regress salary lsales
Source | SS df MS Number of obs = 177
-------------+------------------------------ F( 1, 175) = 40.10
Model | 11327355 1 11327355 Prob > F = 0.0000
Residual | 49438609.8 175 282506.342 R-squared = 0.1864
-------------+------------------------------ Adj R-squared = 0.1818
Total | 60765964.7 176 345261.163 Root MSE = 531.51
------------------------------------------------------------------------------
salary | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lsales | 177.1491 27.9762 6.33 0.000 121.9349 232.3633
_cons | -415.1051 206.2038 -2.01 0.046 -822.0715 -8.138719
------------------------------------------------------------------------------
. stepwise, pr(.2): regress salary sales profits ceoten grad college
begin with full model
p = 0.7798 >= 0.2000 removing grad
p = 0.6108 >= 0.2000 removing college
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Source | SS df MS Number of obs = 177
-------------+------------------------------ F( 3, 173) = 13.80
Model | 11733215 3 3911071.67 Prob > F = 0.0000
Residual | 49032749.7 173 283426.299 R-squared = 0.1931
-------------+------------------------------ Adj R-squared = 0.1791Total | 60765964.7 176 345261.163 Root MSE = 532.38
------------------------------------------------------------------------------
salary | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
sales | .0196671 .0109819 1.79 0.075 -.0020087 .0413428
profits | .3410285 .1649803 2.07 0.040 .015395 .666662
ceoten | 13.29683 5.632999 2.36 0.019 2.178577 24.41508
_cons | 619.8005 65.4907 9.46 0.000 490.5368 749.0642
------------------------------------------------------------------------------
. regress salary sales profits ceoten grad collegeSource | SS df MS Number of obs = 177
-------------+--------------------------------------------- F( 5, 171) = 8.27
Model | 11829650.4 5 2365930.08 Prob > F = 0.0000
Residual | 48936314.3 171 286177.277 R-squared = 0.1947
-------------+---------------------------------------------- Adj R-squared = 0.1711
Total | 60765964.7 176 345261.163 Root MSE = 534.96
------------------------------------------------------------------------------
salary | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
sales | .0197139 .0110377 1.79 0.076 -.0020739 .0415017
profits | .340988 .1664067 2.05 0.042 .0125121 .6694639
ceoten | 12.85655 5.712335 2.25 0.026 1.580781 24.13232
grad | -23.16249 82.71258 -0.28 0.780 -186.4317 140.1067
college | -111.8679 248.3771 -0.45 0.653 -602.1479 378.412
_cons | 744.1547 251.862 2.95 0.004 246.9958 1241.314
------------------------------------------------------------------------------
. mfx
Marginal effects after regress
y = Fitted values (predict)
= 865.86441
----------------------------------------------------------------------------------------------------
variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X
---------+------------------------------------------------------------------------------------------
sales | .0197139 .01104 1.79 0.074 -.00192 .041347 3529.46
profits | .340988 .16641 2.05 0.040 .014837 .667139 207.831
ceoten | 12.85655 5.71233 2.25 0.024 1.66058 24.0525 7.9548
grad*| -23.16249 82.713 -0.28 0.779 -185.276 138.951 .531073
college*| -111.8679 248.38 -0.45 0.652 -598.678 374.942 .971751
-----------------------------------------------------------------------------------------------------
(*) dy/dx is for discrete change of dummy variable from 0 to 1
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use "D:\MAESTRIA\DATOS\probit\mroz.dta", clear
. reg inlf nwifeinc educ exper expersq age kidslt6 kidsge6
. mfx
Marginal effects after regress
y = Fitted values (predict)
= .56839309
eststo m1
. esttab m1
Source SS df MS Number of obs = 753
F( 7, 745) = 38.22
Model 48.8080578 7 6.97257969 Prob > F = 0
Residual 135.919698 745 0.18244255 R-squared = 0.2642
Adj R-squared = 0.2573
Total 184.727756 752 0.24564861 Root MSE = 0.42713
inl f Coef. Std. Err. t P>t [95% Conf. Interval]
nwifeinc -0.0034052 0.0014485 -2.35 0.019 -0.0062488 -0.0005616
educ 0.0379953 0.007376 5.15 0 0.023515 0.0524756
exper 0.0394924 0.0056727 6.96 0 0.0283561 0.0506287
expersq -0.0005963 0.0001848 -3.23 0.001 -0.0009591 -0.0002335
age -0.0160908 0.0024847 -6.48 0 -0.0209686 -0.011213
kidslt6 -0.2618105 0.0335058 -7.81 0 -0.3275875 -0.1960335kidsge6 0.0130122 0.013196 0.99 0.324 -0.0128935 0.0389179
_cons 0.5855192 0.154178 3.8 0 0.2828442 0.8881943
variable dy/dx Std. Err. z P>z [ 95% C.I. ] X
nwifeinc -0.0034052 0.00145 -2.35 0.019 -0.006244 -0.000566 20.129
educ 0.0379953 0.00738 5.15 0 0.023539 0.052452 12.2869
exper 0.0394924 0.00567 6.96 0 0.028374 0.050611 10.6308
expersq -0.0005963 0.00018 -3.23 0.001 -0.000958 -0.000234 178.039
age -0.0160908 0.00248 -6.48 0 -0.020961 -0.011221 42.5378
kidslt6 -0.2618105 0.03351 -7.81 0 -0.327481 -0.19614 0.237716
kidsge6 0.0130122 0.0132 0.99 0.324 -0.012851 0.038876 1.35325
-1
inlf
nwifeinc -0.00341*
(-2.35)
educ 0.0380***
-5.15
exper 0.0395***
-6.96
expersq -0.000596**
(-3.23)
age -0.0161***
(-6.48)
kidslt6 -0.262***
(-7.81)
kidsge6 0.013
-0.99
_cons 0.586***-3.8
N 753
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t statistics in parentheses
* p
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eststo m2
. esttab m2
t statistics in parentheses
* p
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(-6.71)
kidslt6 -0.262***
(-8.24)
kidsge6 0.013
-0.96
N 753
Marginal effects; t statistics in parentheses
(d) for discrete change of dummy variable from 0 to 1
* p
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mfx
Marginal effects after logit
y = Pr(inlf) (predict)
= .58277201
. eststo m3
. esttab m3, margin
Marginal effects; t statistics in parentheses
(d) for discrete change of dummy variable from 0 to 1* p
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. probit inlf nwifeinc educ exper expersq age kidslt6 kidsge6
Iteration 0: log likelihood = -514.8732
Iteration 1: log likelihood = -402.06651
Iteration 2: log likelihood = -401.30273
Iteration 3: log likelihood = -401.30219Iteration 4: log likelihood = -401.30219
Probit regression Number of obs = 753
LR chi2(7) = 227.14
Prob > chi2 = 0.0000
Log likelihood = -401.30219 Pseudo R2 = 0.2206
. mfx
Marginal effects after probit
y = Pr(inlf) (predict)
= .58154201
. eststo m4
. esttab m4, margin
-1
inlf
inlf
nwifeinc -0.00470*
(-2.48)
educ 0.0511***-5.19
inlf Coef. Std. Err. z P>z [95% Conf. Interval]
nwifeinc -0.0120237 0.0048398 -2.48 0.013 -0.0215096 -0.0025378
educ 0.1309047 0.0252542 5.18 0 0.0814074 0.180402
exper 0.1233476 0.0187164 6.59 0 0.0866641 0.1600311expersq -0.0018871 0.0006 -3.15 0.002 -0.003063 -0.0007111
age -0.0528527 0.0084772 -6.23 0 -0.0694678 -0.0362376
kidslt6 -0.8683285 0.1185223 -7.33 0 -1.100628 -0.636029
kidsge6 0.036005 0.0434768 0.83 0.408 -0.049208 0.1212179
_cons 0.2700768 0.508593 0.53 0.595 -0.7267473 1.266901
variable dy/dx Std. Err. z P>z [ 95% C.I. ] X
nwifeinc -0.0046962 0.00189 -2.48 0.013 -0.008401 -0.000991 20.129
educ 0.0511287 0.00986 5.19 0 0.031805 0.070452 12.2869
exper 0.0481771 0.00733 6.57 0 0.033815 0.062539 10.6308
expersq -0.0007371 0.00023 -3.14 0.002 -0.001197 -0.000277 178.039
age -0.0206432 0.00331 -6.24 0 -0.027127 -0.01416 42.5378
kidslt6 -0.3391514 0.04636 -7.32 0 -0.430012 -0.248291 0.237716
kidsge6 0.0140628 0.01699 0.83 0.408 -0.019228 0.047353 1.35325
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exper 0.0482***
-6.57
expersq
-
0.000737**
(-3.14)
age -0.0206***
(-6.24)
kidslt6 -0.339***
(-7.32)
kidsge6 0.0141
-0.83
N 753
Marginal effects; t statistics in parentheses
(d) for discrete change of dummy variable from 0 to 1
* p
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. tab inlf inlf_probit, row col
Key
frequency
row percentage
column percentag
. dprobit inlf nwifeinc educ exper expersq age kidslt6 kidsge6
Iteration 0: log likelihood = -514.8732
Iteration 1: log likelihood = -405.78215
Iteration 2: log likelihood = -401.32924
Iteration 3: log likelihood = -401.30219
Iteration 4: log likelihood = -401.30219
Probit regression, reporting marginal effects Number of obs = 753
LR chi2(7) = 227.14
Prob > chi2 = 0.0000
Log likelihood = -401.30219 Pseudo R2 = 0.2206
inlf dF/dx Std. Err. z P>z x-bar [ 95% C.I. ]
nwifeinc -0.0046962 0.0018903 -2.48 0.013 20.129 -0.008401 -0.00099
educ 0.0511287 0.0098592 5.18 0 12.2869 0.031805 0.070452
exper 0.0481771 0.0073278 6.59 0 10.6308 0.033815 0.062539
expersq -0.0007371 0.0002347 -3.15 0.002 178.039 -0.001197 -0.00027
age -0.0206432 0.0033079 -6.23 0 42.5378 -0.027127 -0.01416
kidslt6 -0.3391514 0.0463581 -7.33 0 0.237716 -0.430012 -0.24829
kidsge6 0.0140628 0.0169852 0.83 0.408 1.35325 -0.019228 0.047353
obs. P 0.5683931
pred. P 0.581542 (at x-bar)
z and P>|z| correspond to the test of the underlying coefficient being 0
=1 if in
lab frce,
1975 0 1 Total
0 205 120 325
1 80 348 428
Total 285 468 753
1(p_probit>=0.5)
0 1 Total
205 120 325
63.08 36.92 100
71.93 25.64 43.16
80 348 428
18.69 81.31 100
28.07 74.36 56.84
285 468 753
37.85 62.15 100
100 100 100
1
Total
1(p_probit>=0.5)
1if in
lab frce
1975
0
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. gen pk2a=normal( _b[_cons] + _b[nwifeinc]*nwifeinc + _b[educ]*educ + _b[exper]*exper +
_b[expersq]*expersq + _b[age]*age
> + _b[kidslt6]*2 + _b[kidsge6]*kidsge6)
.
. gen pk1a=normal( _b[_cons] + _b[nwifeinc]*nwifeinc + _b[educ]*educ + _b[exper]*exper +
_b[expersq]*expersq + _b[age]*age> +_b[kidslt6]*1 + _b[kidsge6]*kidsge6)
. gen effecta=pk2a-pk1a
. egen meffecta=mean(effecta)
.
. sum meffecta
. egen m_nwifeinc=mean(nwifeinc)
.
. egen m_educ=mean(educ)
.
. egen m_exper=mean(exper)
.
. egen m_expersq=mean(expersq)
.
. egen m_age=mean(age)
.
. egen m_kidslt6=mean(kidslt6)
.
. egen m_kidsge6=mean(kidsge6)
.
. egen m_cons=mean(_cons)
. gen pk2b=normal( _b[_cons] + _b[nwifeinc]*m_nwifeinc + _b[educ]*m_educ +
_b[exper]*m_exper + _b[expersq]*m_expersq+_b[ag
> e]*m_age + _b[kidslt6]*2 + _b[kidsge6]*m_kidsge6)
.
. gen pk1b=normal(_b[_cons] + _b[nwifeinc]*m_nwifeinc + _b[educ]*m_educ +
_b[exper]*m_exper + _b[expersq]*m_expersq +_b[ag
> e]*m_age + _b[kidslt6]*1 + _b[kidsge6]*m_kidsge6)
.sort kidslt6
. twoway line pk2b pk1b kidslt6, saving(pk2b.gph, replace)
(note: file pk2b.gph not found)
(file pk2b.gph saved)
.
. twoway line pk1b kidslt6, saving(pk1b.gph, replace)
(note: file pk1b.gph not found)
(file pk1b.gph saved)
. graph combine pk2b.gph pk1b.gph
Variable Obs Mean Std. Dev. Min Max
meffecta 753 -0.2196508 0 -0.2196508 -0.2196508
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. twoway line pk2b pk1b kidslt6, saving(pk2b.gph, replace)
(file pk2b.gph saved)
. gen effectb=pk2b-pk1b
.
. sum effectb
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
effectb | 753 -.2314838 0 -.2314838 -.2314838
. probit inlf nwifeinc educ exper expersq age kidslt6 kidsge6
Iteration 0: log likelihood = -514.8732
Iteration 1: log likelihood = -402.06651
Iteration 2: log likelihood = -401.30273Iteration 3: log likelihood = -401.30219
Iteration 4: log likelihood = -401.30219
Probit regression Number of obs = 753
LR chi2(7) = 227.14
Prob > chi2 = 0.0000
Log likelihood = -401.30219 Pseudo R2 = 0.2206
--------------------------------------------------------------------------------------------------
inlf | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+------------------------------------------------------------------------------------
nwifeinc | -.0120237 .0048398 -2.48 0.013 -.0215096 -.0025378
educ | .1309047 .0252542 5.18 0.000 .0814074 .180402
exper | .1233476 .0187164 6.59 0.000 .0866641 .1600311
expersq | -.0018871 .0006 -3.15 0.002 -.003063 -.0007111
age | -.0528527 .0084772 -6.23 0.000 -.0694678 -.0362376
kidslt6 | -.8683285 .1185223 -7.33 0.000 -1.100628 -.636029
kidsge6 .036005 .0434768 0.83 0.408 -.049208 .1212179
_cons | .2700768 .508593 0.53 0.595 -.7267473 1.266901
---------------------------------------------------------------------------------------------------
.
. gen p_educ=normal(_b[_cons] + _b[nwifeinc]*m_nwifeinc + _b[educ]*educ +
_b[exper]*m_exper + _b[expersq]*m_expersq +_b[ag
> e]*m_age + _b[kidslt6]*m_kidslt6 + _b[kidsge6]*m_kidsge6)
.
. gen p_kidslt6=normal(_b[_cons] + _b[nwifeinc]*m_nwifeinc + _b[educ]*m_educ +
_b[exper]*m_exper + _b[expersq]*m_expersq +
> _b[age]*m_age + _b[kidslt6]*kidslt6 + _b[kidsge6]*m_kidsge6)
.
. gen p_exper=normal(_b[_cons] + _b[nwifeinc]*m_nwifeinc + _b[educ]*m_educ +
_b[exper]*exper + _b[expersq]*expersq +_b[age
> ]*m_age + _b[kidslt6]*m_kidslt6 + _b[kidsge6]*m_kidsge6)
.
. label var p_educ "Participation probability"
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.
. label var p_kidslt6 "Participation probability"
.
. label var p_exper "Participation probability"
.
.
.
. sort educ
twoway line p_educ educ, saving(g_educ.gph, replace)
(note: file g_educ.gph not found)
(file g_educ.gph saved)
.
. graph export g_educ.wmf, replace
(note: file g_educ.wmf not found)(file D:\MAESTRIA\DATOS\PROGRAMAS DE ECONOMETRIA\Stata12 FULL\Stata12\g_educ.wmf
written in Windows Metafile format)
sort kidslt6
.
. twoway line p_kidslt6 kidslt6, saving(g_kidslt6.gph, replace)
(note: file g_kidslt6.gph not found)
(file g_kidslt6.gph saved)
.
. graph export g_kidslt6.eps, replace
(note: file g_kidslt6.eps not found)
(file g_kidslt6.eps written in EPS format)
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sort exper
.
. twoway line p_exper exper, saving(g_exper.gph, replace)
(note: file g_exper.gph not found)
(file g_exper.gph saved)
.
. graph combine g_educ.gph g_kidslt6.gph g_exper.gph pk2b.gph, saving(probit_graphs.gph,
replace)title("Estimated particip
> ation probability (probit)")
(file probit_graphs.gph saved)
.
. graph export probit_graphs.wmf, replace
(file D:\MAESTRIA\DATOS\PROGRAMAS DE ECONOMETRIA\Stata12
FULL\Stata12\probit_graphs.wmf written in Windows Metafile format
> )
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gen p_exper5=normal(_b[_cons] + _b[nwifeinc]*m_nwifeinc + _b[educ]*m_educ +
_b[exper]*(exper+5) + _b[expersq]*expersq +_
> b[age]*m_age + _b[kidslt6]*m_kidslt6 + _b[kidsge6]*m_kidsge6)
.
. gen p_exper2=normal(_b[_cons] + _b[nwifeinc]*m_nwifeinc + _b[educ]*m_educ +
_b[exper]*(exper+1) + _b[expersq]*expersq +_
> b[age]*m_age + _b[kidslt6]*m_kidslt6 + _b[kidsge6]*m_kidsge6)
.
. gen p_exper1=normal(_b[_cons] + _b[nwifeinc]*m_nwifeinc + _b[educ]*m_educ +
_b[exper]*1*exper + _b[expersq]*expersq +_b[
> age]*m_age + _b[kidslt6]*m_kidslt6 + _b[kidsge6]*m_kidsge6)
.
. sort exper
.
. twoway line p_exper5 p_exper2 p_exper1 exper, saving(experiencia, replace)
(note: file experiencia.gph not found)
(file experiencia.gph saved)
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