Modelo Provit Econometria

7/25/2019 Modelo Provit Econometria

1/21

NOMBRE DEL DOCENTE

Mgr. RENE PAREDES MAMANI

NOMBREANTONI HINOJOSA FRANCO

CURSO

METODOS CUANTITAVOS APLICADOS

MODELO PROVIT


2/21

. 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)


3/21

. regress salary sales profits ceoten, noconstant


-------------+------------------------------------------------ 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

----------------------------------------------------------------------------------------------------


-------------+-------------------------------------------------------------------------------------

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


-------------+------------------------------------------ F( 3, 173) = 13.80

Model | 11733215 3 3911071.67 Prob > F = 0.0000


-------------+-------------------------------------------- Adj R-squared = 0.1791

Total | 60765964.7 176 345261.163 Root MSE = 532.38

------------------------------------------------------------------------------------------------


-------------+----------------------------------------------------------------------------------

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)


4/21

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)


5/21

. 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)


6/21

. gen lceoten=log(ceoten)

(5 missing values generated)

. regress lsalary sales


-------------+---------------------------------------------- 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


-------------+------------------------------ F( 1, 175) = 40.10

Model | 11327355 1 11327355 Prob > F = 0.0000


-------------+------------------------------ Adj R-squared = 0.1818

Total | 60765964.7 176 345261.163 Root MSE = 531.51

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

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


7/21


-------------+------------------------------ F( 3, 173) = 13.80

Model | 11733215 3 3911071.67 Prob > F = 0.0000


-------------+------------------------------ Adj R-squared = 0.1791Total | 60765964.7 176 345261.163 Root MSE = 532.38

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

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


-------------+---------------------------------------------- Adj R-squared = 0.1711

Total | 60765964.7 176 345261.163 Root MSE = 534.96

------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

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



= 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


8/21

use "D:\MAESTRIA\DATOS\probit\mroz.dta", clear

. reg inlf nwifeinc educ exper expersq age kidslt6 kidsge6

. mfx



= .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


9/21

t statistics in parentheses

* p


10/21

eststo m2

. esttab m2

t statistics in parentheses

* p


11/21

(-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


12/21

mfx

Marginal effects after logit

y = Pr(inlf) (predict)

= .58277201

. eststo m3

. esttab m3, margin


(d) for discrete change of dummy variable from 0 to 1* p


13/21

. probit inlf nwifeinc educ exper expersq age kidslt6 kidsge6

Iteration 0: log likelihood = -514.8732



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


14/21

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


(d) for discrete change of dummy variable from 0 to 1

* p


15/21

. tab inlf inlf_probit, row col

Key

frequency

row percentage

column percentag

. dprobit inlf nwifeinc educ exper expersq age kidslt6 kidsge6






Probit regression, reporting marginal effects Number of obs = 753

LR chi2(7) = 227.14

Prob > chi2 = 0.0000


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


16/21

. 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)


. graph combine pk2b.gph pk1b.gph

Variable Obs Mean Std. Dev. Min Max

meffecta 753 -0.2196508 0 -0.2196508 -0.2196508


17/21

. twoway line pk2b pk1b kidslt6, saving(pk2b.gph, replace)


. 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 2: log likelihood = -401.30273Iteration 3: log likelihood = -401.30219


Probit regression Number of obs = 753

LR chi2(7) = 227.14

Prob > chi2 = 0.0000


--------------------------------------------------------------------------------------------------

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"


18/21

.

. 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)


19/21

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

> )


20/21

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)


21/21

Documents

Modelo Provit Econometria