ejemplo econometria 1 - Gustavo Perales Vivar

Vista previa del material en texto

EJEMPLO PREPARADO PARA REPASAR CAPÍTULO 7 – WOOLDRIDGE
Se tiene información de un curso y se quiere ver que ayuda a explicar el rendimiento general de los alumnos. Los datos que se poseen son: GPA – promedio general de notas, Evali_ini – evaluación académica general al principio de año, sexo – si es hombre (1) o mujer (0) y si pertenece a una minoría étnica (1 – 0)
Se corre regresión con todas las variables disponibles:
predict yhat, xb
predict residual, r
rvfplot
swilk residual
 Shapiro-Wilk W test for normal data
 Variable | Obs W V z Prob>z
-------------+--------------------------------------------------
 res3 | 32 0.95985 1.339 0.607 0.27206
hettest
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
 Ho: Constant variance
 Variables: fitted values of gpa
 chi2(1) = 0.61
 Prob > chi2 = 0.4364
imtest, white
White's test for Ho: homoskedasticity
 against Ha: unrestricted heteroskedasticity
 chi2(7) = 5.87
 Prob > chi2 = 0.5555
Cameron & Trivedi's decomposition of IM-test
---------------------------------------------------
 Source | chi2 df p
---------------------+-----------------------------
 Heteroskedasticity | 5.87 7 0.5555
 Skewness | 7.80 3 0.0504
 Kurtosis | 0.57 1 0.4519
---------------------+-----------------------------
 Total | 14.23 11 0.2207
---------------------------------------------------
ovtest
Ramsey RESET test using powers of the fitted values of gpa
 Ho: model has no omitted variables
 F(3, 25) = 0.43
 Prob > F = 0.7325
REGRESIÓN CON VARIABLES DUMMIES Y CATEGÓRICAS.
OTRA FORMA DE HACERLO EN STATA: CREANDO LAS DUMMIES PARA CADA VARIABLE
CÓMO CREAR TODAS LAS VARIABLES DUMMIES??
Xi, noomit i.school
Nota: si ya había creado alguna de las variables, borrarlas (drop) y aplicar el comando anterior para que se generen todas.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 _cons 3.339654 .325489 10.26 0.000 2.670602 4.008707
 _Ischool_3 .6145625 .1546055 3.98 0.000 .2967663 .9323586
 _Ischool_2 -.2035428 .1423122 -1.43 0.165 -.4960697 .0889842
 minoría .3624276 .1172685 3.09 0.005 .1213786 .6034765
 sexo -.1785123 .1051969 -1.70 0.102 -.3947477 .037723
 evali_ini -.0233367 .0150749 -1.55 0.134 -.0543237 .0076502
 
 gpa Coef. Std. Err. t P>|t| [95% Conf. Interval]
 
 Total 7.31557181 31 .235986187 Root MSE = .26704
 Adj R-squared = 0.6978
 Residual 1.85405466 26 .071309795 R-squared = 0.7466
 Model 5.46151715 5 1.09230343 Prob > F = 0.0000
 F( 5, 26) = 15.32
 Source SS df MS Number of obs = 32
i.school _Ischool_1-3 (naturally coded; _Ischool_1 omitted)
. xi: reg gpa evali_ini sexo minoría i.school
 
 
 
 
 
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 minoría 32 .34375 .4825587 0 1
 sexo 32 .4375 .5040161 0 1
 evali_ini 32 21.9375 3.901509 12 29
 gpa 32 3.085938 .4857841 2.06 4
 
 Variable Obs Mean Std. Dev. Min Max
. summ gpa evali_ini sexo minoría
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 _cons 2.389232 .4375687 5.46 0.000 1.492913 3.285551
 minoría .5275216 .1784334 2.96 0.006 .1620174 .8930257
 sexo -.1442398 .1638484 -0.88 0.386 -.4798679 .1913883
 evali_ini .0263692 .0201288 1.31 0.201 -.0148627 .0676012
 
 gpa Coef. Std. Err. t P>|t| [95% Conf. Interval]
 
 Total 7.31557181 31 .235986187 Root MSE = .41662
 Adj R-squared = 0.2645
 Residual 4.86008403 28 .173574429 R-squared = 0.3357
 Model 2.45548778 3 .818495928 Prob > F = 0.0087
 F( 3, 28) = 4.72
 Source SS df MS Number of obs = 32
. reg gpa evali_ini sexo minoría
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 . 32 -21.79434 -15.25115 4 38.5023 44.36524
 
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 15 3 Semiprivate
 12 2 Public
 5 1 Private
 tabulation: Freq. Numeric Label
 unique values: 3 missing .: 0/32
 range: [1,3] units: 1
 label: Schooltype
 type: numeric (float)
 
school (unlabeled)
 
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 Note: N=Obs used in calculating BIC; see [R] BIC note. 32 -21.79434 .1677422 6 11.66452 20.45893
 
 Model Obs ll(null) ll(model) df AIC BIC
 
. estat ic
 
 _cons 3.136112 .3094752 10.13 0.000 2.499976 3.772247
 
 3 .8181052 .1270901 6.44 0.000 .5568677 1.079343
 1 .2035428 .1423122 1.43 0.165 -.0889842 .4960697
 school 
 
 minoría .3624276 .1172685 3.09 0.005 .1213786 .6034765
 sexo -.1785123 .1051969 -1.70 0.102 -.3947477 .037723
 evali_ini -.0233367 .0150749 -1.55 0.134 -.0543237 .0076502
 
 gpa Coef. Std. Err. t P>|t| [95% Conf. Interval]
 
 Total 7.31557181 31 .235986187 Root MSE = .26704
 Adj R-squared = 0.6978
 Residual 1.85405466 26 .071309795 R-squared = 0.7466
 Model 5.46151715 5 1.09230343 Prob > F = 0.0000
 F( 5, 26) = 15.32
 Source SS df MS Number of obs = 32
. reg gpa evali_ini sexo minoría i.school ib2.school