UNIVERSIDAD DE EL SALVADOR
FACULTAD DE CIENCIAS ECONÓMICAS
ESCUELA DE ECONOMÍA
CICLO I - 2023
“APLICACIÓN DE LA LIBRERÍA STARGAZER PARA PRESENTAR MODELOS
CORREGIDOS CON ESTIMADORES HAC”
ASIGNATURA: ECONOMETRÍA
CATEDRÁTICO: MSF. CARLOS ADEMIR PERÉZ
GRUPO TEÓRICO: 03
PRESENTADO POR: AGUILAR ZACARIAS, CRISTALI DAYAMARI
Carnet: AZ20006
1. Importación de datos
load("C:/Users/crist/Downloads/Cristali Dayamari Aguilar Zacarias - smoke.RData")
options(scipen = 999999999)
library(lmtest)
modelo_smoke<-lm(cigs~ cigpric + lcigpric + income + lincome + age + agesq + educ + white + restaurn, data = data)
coeftest(modelo_smoke)
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 340.804374604 260.015587269 1.3107 0.190334
## cigpric 2.002267667 1.492831189 1.3413 0.180220
## lcigpric -115.273464445 85.424315195 -1.3494 0.177585
## income -0.000046194 0.000133491 -0.3460 0.729402
## lincome 1.404061178 1.708165841 0.8220 0.411340
## age 0.778359013 0.160555612 4.8479 0.0000015001 ***
## agesq -0.009150353 0.001749292 -5.2309 0.0000002158 ***
## educ -0.494780616 0.168180198 -2.9420 0.003356 **
## white -0.531051635 1.460721806 -0.3636 0.716287
## restaurn -2.644241351 1.129998690 -2.3400 0.019528 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
2. Resumen del modelo con stargazer
library(stargazer)
options(scipen = 999999999)
modelo_smoke<-lm(cigs~ cigpric + lcigpric + income + lincome + age + agesq + educ + white + restaurn, data = data)
summary(modelo_smoke)
##
## Call:
## lm(formula = cigs ~ cigpric + lcigpric + income + lincome + age +
## agesq + educ + white + restaurn, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.169 -9.357 -5.915 7.851 70.744
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 340.80437460 260.01558727 1.311 0.19033
## cigpric 2.00226767 1.49283119 1.341 0.18022
## lcigpric -115.27346445 85.42431520 -1.349 0.17758
## income -0.00004619 0.00013349 -0.346 0.72940
## lincome 1.40406118 1.70816584 0.822 0.41134
## age 0.77835901 0.16055561 4.848 0.000001500 ***
## agesq -0.00915035 0.00174929 -5.231 0.000000216 ***
## educ -0.49478062 0.16818020 -2.942 0.00336 **
## white -0.53105164 1.46072181 -0.364 0.71629
## restaurn -2.64424135 1.12999869 -2.340 0.01953 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13.41 on 797 degrees of freedom
## Multiple R-squared: 0.05515, Adjusted R-squared: 0.04448
## F-statistic: 5.169 on 9 and 797 DF, p-value: 0.0000007735
library(stargazer)
stargazer(modelo_smoke, title = "Resumen del modelo de regresión", type = "html")
Resumen del modelo de regresión
|
|
|
|
Dependent variable:
|
|
|
|
|
|
cigs
|
|
|
|
cigpric
|
2.002
|
|
|
(1.493)
|
|
|
|
|
lcigpric
|
-115.273
|
|
|
(85.424)
|
|
|
|
|
income
|
-0.00005
|
|
|
(0.0001)
|
|
|
|
|
lincome
|
1.404
|
|
|
(1.708)
|
|
|
|
|
age
|
0.778***
|
|
|
(0.161)
|
|
|
|
|
agesq
|
-0.009***
|
|
|
(0.002)
|
|
|
|
|
educ
|
-0.495***
|
|
|
(0.168)
|
|
|
|
|
white
|
-0.531
|
|
|
(1.461)
|
|
|
|
|
restaurn
|
-2.644**
|
|
|
(1.130)
|
|
|
|
|
Constant
|
340.804
|
|
|
(260.016)
|
|
|
|
|
|
|
Observations
|
807
|
|
R2
|
0.055
|
|
Adjusted R2
|
0.044
|
|
Residual Std. Error
|
13.413 (df = 797)
|
|
F Statistic
|
5.169*** (df = 9; 797)
|
|
|
|
Note:
|
p<0.1; p<0.05;
p<0.01
|
3. Verificaciónn de pruebas de Heterocedasticidad y
Autocorrelación
3.1 Prueba de heterocedasticidad
3.1.1 Prueba de White (prueba de Breusch Pagan)
library(lmtest)
p_white<-bptest(modelo_smoke,~I(cigpric^2)+I(lcigpric^2)+I(income^2)+I(lincome^2)+I(age^2)+I(agesq^2)+I(educ^2)+I(white^2)+I(restaurn^2),data = data)
print(p_white)
##
## studentized Breusch-Pagan test
##
## data: modelo_smoke
## BP = 26.323, df = 9, p-value = 0.001809
# Interpretación: Hay evidencia de heterocedasticidad debido a que p-value < 0.05 por lo tanto se rechaza la H0.
3.2 Prueba del Multiplicador de Lagrange (Breusch Godfrey) - Pruebas
de Autocorrelación
3.2.1 Autocorrelación de 1º orden (prueba de Durbin Watson)
library(car)
durbinWatsonTest(model = modelo_smoke)
## lag Autocorrelation D-W Statistic p-value
## 1 -0.009243664 2.017442 0.916
## Alternative hypothesis: rho != 0
# Interpretación: No hay evidencia de Autocorrelación de 1º orden ya que pvalue > 0.05
3.2.2 Autocorrelación de 2º Orden:
library(lmtest)
prueba_LM<-bgtest(modelo_smoke,order = 2)
print(prueba_LM)
##
## Breusch-Godfrey test for serial correlation of order up to 2
##
## data: modelo_smoke
## LM test = 0.26889, df = 2, p-value = 0.8742
# Interpretación: No hay evidencia de Autocorrelación de 2º orden ya que pvalue > 0.05
4. Estimador HAC
options(scipen = 999999999)
library(lmtest)
library(sandwich)
# Corregido
# Hc0 corrige solo Heterocedasticidad
vcov_HAC<- vcovHC(modelo_smoke, type="HC0")
coeftest(modelo_smoke,vcov. = vcov_HAC)
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 340.804374604 278.565072832 1.2234 0.221530
## cigpric 2.002267667 1.602727983 1.2493 0.211927
## lcigpric -115.273464445 91.344424868 -1.2620 0.207331
## income -0.000046194 0.000115593 -0.3996 0.689540
## lincome 1.404061178 1.228970726 1.1425 0.253602
## age 0.778359013 0.136944678 5.6837 0.0000000184838 ***
## agesq -0.009150353 0.001451548 -6.3039 0.0000000004804 ***
## educ -0.494780616 0.162968371 -3.0361 0.002475 **
## white -0.531051635 1.361907703 -0.3899 0.696691
## restaurn -2.644241351 1.038254938 -2.5468 0.011058 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
5. Modelo “original” y el modelo “corregido” en una sola tabla
library(stargazer)
library(sandwich)
vcov_HAC<-vcovHC(modelo_smoke, type = "HC0")
robust<- sqrt(diag(vcov_HAC))
stargazer(modelo_smoke,modelo_smoke, se=list(NULL, robust),column.labels = c("Original","Corregido"), aling=TRUE, type = "html", title = "Modelo de consumo de cigarrillos")
Modelo de consumo de cigarrillos
|
|
|
|
Dependent variable:
|
|
|
|
|
|
cigs
|
|
|
Original
|
Corregido
|
|
|
(1)
|
(2)
|
|
|
|
cigpric
|
2.002
|
2.002
|
|
|
(1.493)
|
(1.603)
|
|
|
|
|
|
lcigpric
|
-115.273
|
-115.273
|
|
|
(85.424)
|
(91.344)
|
|
|
|
|
|
income
|
-0.00005
|
-0.00005
|
|
|
(0.0001)
|
(0.0001)
|
|
|
|
|
|
lincome
|
1.404
|
1.404
|
|
|
(1.708)
|
(1.229)
|
|
|
|
|
|
age
|
0.778***
|
0.778***
|
|
|
(0.161)
|
(0.137)
|
|
|
|
|
|
agesq
|
-0.009***
|
-0.009***
|
|
|
(0.002)
|
(0.001)
|
|
|
|
|
|
educ
|
-0.495***
|
-0.495***
|
|
|
(0.168)
|
(0.163)
|
|
|
|
|
|
white
|
-0.531
|
-0.531
|
|
|
(1.461)
|
(1.362)
|
|
|
|
|
|
restaurn
|
-2.644**
|
-2.644**
|
|
|
(1.130)
|
(1.038)
|
|
|
|
|
|
Constant
|
340.804
|
340.804
|
|
|
(260.016)
|
(278.565)
|
|
|
|
|
|
|
|
Observations
|
807
|
807
|
|
R2
|
0.055
|
0.055
|
|
Adjusted R2
|
0.044
|
0.044
|
|
Residual Std. Error (df = 797)
|
13.413
|
13.413
|
|
F Statistic (df = 9; 797)
|
5.169***
|
5.169***
|
|
|
|
Note:
|
p<0.1; p<0.05;
p<0.01
|
Modelo de consumo de cigarrillos
|
|
|
TRUE
|
|
|