ejemplo econometria - Gustavo Perales Vivar

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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
twoway (scatter residual obs)
. ovtest
powers of fitted values collinear with explanatory variables
(typically because all explanatory variables are indicator variables)
test not possible
predict lev, h
. twoway (scatter gpa obs, sort) ( lfit gpa obs, yaxis(2)), ytitle(GPA) xtitle(obs)
CÓMO SABER CUAL PODRÍA SER UN MEJOR MODELO??
. findit rsquare
. gen eval2=evali_ini^2
. rsquare gpa evali_ini sexo minoría eval2
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.
INCORPORANDO FUNCIONES CUADRÁTICAS DE LAS VARIABLES.
AVPLOTS
CON TODAS LAS VARIABLES QUÉ DICE C-MALLOWS???
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 Note: N=Obs used in calculating BIC; see [R] BIC note
 
 . 32 -21.79434 -15.68797 3 37.37595 41.77316
 
 Model Obs ll(null) ll(model) df AIC BIC
 
. estat ic
 
 _cons 2.341381 .4324917 5.41 0.000 1.456836 3.225926
 minoría .4630601 .1620839 2.86 0.008 .1315614 .7945588
 evali_ini .026684 .0200474 1.33 0.194 -.0143175 .0676854
 
 gpa Coef. Std. Err. t P>|t| [95% Conf. Interval]
 
 Total 7.31557181 31 .235986187 Root MSE = .415
 Adj R-squared = 0.2702
 Residual 4.99459935 29 .172227564 R-squared = 0.3173
 Model 2.32097245 2 1.16048623 Prob > F = 0.0040
 F( 2, 29) = 6.74
 Source SS df MS Number of obs = 32
. reg gpa evali_ini minoría
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 . 32 -21.79434 -16.63676 2 37.27352 40.20499
 
 Model Obs ll(null) ll(model) df AIC BIC
 
. estat ic
 
 _cons 2.904286 .0917184 31.67 0.000 2.716972 3.0916
 minoría .5284416 .1564355 3.38 0.002 .2089577 .8479255
 
 gpa Coef. Std. Err. t P>|t| [95% Conf. Interval]
 
 Total 7.31557181 31 .235986187 Root MSE = .42031
 Adj R-squared = 0.2514
 Residual 5.29973216 30 .176657739 R-squared = 0.2756
 Model 2.01583965 1 2.01583965 Prob > F = 0.0020
 F( 1, 30) = 11.41
 Source SS df MS Number of obs = 32
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 Skewness 3.71 1 0.0542
 Heteroskedasticity 1.20 1 0.2729
 
 Source chi2 df p
 
Cameron & Trivedi's decomposition of IM-test
 Prob > chi2 = 0.2729
 chi2(1) = 1.20
 against Ha: unrestricted heteroskedasticity
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. imtest, white
 Prob > chi2 = 0.2595
 chi2(1) = 1.27
 Variables: fitted values of gpa
 Ho: Constant variance
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
. hettest
 Mean VIF 1.00
 
 minoría 1.00 1.000000
 
 Variable VIF 1/VIF 
. vif
 residual 32 0.95551 1.484 0.820 0.20619
 
 Variable Obs W V z Prob>z
 Shapiro-Wilk W test for normal data
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R-squared Mallows C SEE MSE models with 4 predictors
0.3422 3.50 4.8125 0.1719 sexo minoría eval2
0.3378 3.68 4.8447 0.1730 evali_ini minoría eval2
0.1721 10.61 6.0563 0.2163 evali_ini sexo eval2
0.3357 3.77 4.8601 0.1736 evali_ini sexo minoría
R-squared Mallows C SEE MSE modelswith 3 predictors
0.3243 2.25 4.9435 0.1705 minoría eval2
0.1447 9.75 6.2573 0.2158 sexo eval2
0.2949 3.47 5.1580 0.1779 sexo minoría
0.1693 8.72 6.0772 0.2096 evali_ini eval2
0.3173 2.54 4.9946 0.1722 evali_ini minoría
0.1283 10.44 6.3772 0.2199 evali_ini sexo
R-squared Mallows C SEE MSE models with 2 predictors
0.1418 7.87 6.2781 0.2093 eval2
0.2756 2.28 5.2997 0.1767 minoría
0.0092 13.42 7.2485 0.2416 sexo
0.1251 8.57 6.4003 0.2133 evali_ini
R-squared Mallows C SEE MSE models with 1 predictor
Regression models for dependent variable : gpa
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 tabulation: Freq. Numeric Label
 unique values: 3 missing .: 0/32
 range: [1,3] units: 1
 label: Schooltype
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 _cons 3.339654 .325489 10.26 0.000 2.670602 4.008707
 
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 2 -.2035428 .1423122 -1.43 0.165 -.4960697 .0889842
 school 
 
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 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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 . 32 -21.79434 .1677422 6 11.66452 20.45893
 
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 . 32 -21.79434 .1677422 6 11.66452 20.45893
 
 Model Obs ll(null) ll(model) df AIC BIC
 
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 _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 
 
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 sexo -.1785123 .1051969 -1.70 0.102 -.3947477 .037723
 evali_ini -.0233367 .0150749 -1.55 0.134 -.0543237 .0076502
 
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 Model 5.46151715 5 1.09230343 Prob > F = 0.0000
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 Source SS df MS Number of obs = 32
. reg gpa evali_ini sexo minoría i.school ib2.school
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 . 32 -21.79434 .1677422 6 11.66452 20.45893
 
 Model Obs ll(null) ll(model) df AIC BIC
 
. estat ic
 
 _cons 3.954217 .372627810.61 0.000 3.188269 4.720164
 
 2 -.8181052 .1270901 -6.44 0.000 -1.079343 -.5568677
 1 -.6145625 .1546055 -3.98 0.000 -.9323586 -.2967663
 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 ib3.school
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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
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 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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coef = .52752155, se = .17843337, t = 2.96
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 . 32 -21.79434 -14.80128 5 39.60255 46.93123
 
 Model Obs ll(null) ll(model) df AIC BIC
 
. estat ic
 
 _cons 3.791099 1.65696 2.29 0.030 .3912981 7.1909
 eval2 .0033521 .0038202 0.88 0.388 -.0044864 .0111905
 minoría .5010602 .1816911 2.76 0.010 .1282608 .8738596
 sexo -.1360889 .1647877 -0.83 0.416 -.4742053 .2020276
 evali_ini -.1130711 .1601938 -0.71 0.486 -.4417617 .2156196
 
 gpa Coef. Std. Err. t P>|t| [95% Conf. Interval]
 
 Total 7.31557181 31 .235986187 Root MSE = .41834
 Adj R-squared = 0.2584
 Residual 4.72533578 27 .175012436 R-squared = 0.3541
 Model 2.59023603 4 .647559007 Prob > F = 0.0158
 F( 4, 27) = 3.70
 Source SS df MS Number of obs = 32
. reg gpa evali_ini sexo minoría eval2
 
 
 
 
 
 
 
 
 
 
 
 
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 . 32 -21.79434 -15.09382 4 38.18764 44.05058
 
 Model Obs ll(null) ll(model) df AIC BIC
 
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 _cons 2.634386 .2425913 10.86 0.000 2.13746 3.131312
 eval2 .0006772 .0004777 1.42 0.167 -.0003013 .0016556
 minoría .5176506 .1785427 2.90 0.007 .1519224 .8833788
 sexo -.1423329 .163069 -0.87 0.390 -.4763645 .1916987
 
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 Model Obs ll(null) ll(model) df AIC BIC
 
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