options(scipen = 9999)
load("C:/Users/JOSE CASTRO/Downloads/smoke.RData")
modelo_cigs <- lm(cigs ~ cigpric + lcigpric + income + lincome +
age + agesq + educ + white + restaurn,
data = data)
print(modelo_cigs)##
## Call:
## lm(formula = cigs ~ cigpric + lcigpric + income + lincome + age +
## agesq + educ + white + restaurn, data = data)
##
## Coefficients:
## (Intercept) cigpric lcigpric income lincome
## 340.80437460 2.00226767 -115.27346445 -0.00004619 1.40406118
## age agesq educ white restaurn
## 0.77835901 -0.00915035 -0.49478062 -0.53105164 -2.64424135library(stargazer)
suppressWarnings(
stargazer(modelo_cigs,
type = "text",
title = "Modelo de Cigarrillos",
header = FALSE)
)##
## Modelo de Cigarrillos
## ===============================================
## 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<0.01library(lmtest)
white_test <- bptest(modelo_cigs,
~ 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(white_test)##
## studentized Breusch-Pagan test
##
## data: modelo_cigs
## BP = 26.323, df = 9, p-value = 0.001809p-value = 0.0018 < 0.05 → HAY heterocedasticidad La matriz Var-Cov NO es escalar por heterocedasticidad
Autocorrelación de 2º Orden:
prueba_LM <- bgtest(modelo_cigs, order = 2)
print(prueba_LM)##
## Breusch-Godfrey test for serial correlation of order up to 2
##
## data: modelo_cigs
## LM test = 0.26889, df = 2, p-value = 0.8742p-value = 0.8742 > 0.05 → NO hay autocorrelación de orden 2
Autocorrelación de 1º orden (prueba de Durbin Watson)
library(car)## Cargando paquete requerido: carDataprueba_DW <- durbinWatsonTest(modelo_cigs)
print(prueba_DW)## lag Autocorrelation D-W Statistic p-value
## 1 -0.009243664 2.017442 0.888
## Alternative hypothesis: rho != 0p-value = 0.92 > 0.05 → NO hay autocorrelación de orden 1
La matriz de Varianza-Covarianza NO es escalar. La fuente del problema es la heterocedasticidad, detectada mediante la prueba de Breusch-Pagan con p-value = 0.0018 < 0.05.
Sin corregir
options(scipen = 9999)
library(lmtest)
#Sin corregir:
coeftest(modelo_cigs)##
## 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 ' ' 1Corregido (usando un estimador HAC)
library(lmtest)
library(sandwich)
# Corregido
vcov_HAC <- vcovHC(modelo_cigs, type = "HC0")
coeftest(modelo_cigs, vcov. = vcov_HAC)##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 340.804374604 278.565072885 1.2234 0.221530
## cigpric 2.002267667 1.602727983 1.2493 0.211927
## lcigpric -115.273464445 91.344424879 -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 ' ' 1library(stargazer)
# Extraer errores estándar corregidos
se_HAC <- sqrt(diag(vcov_HAC))
suppressWarnings(
stargazer(modelo_cigs, modelo_cigs,
se = list(NULL, se_HAC),
column.labels = c("Original", "Corregido"),
type = "text",
title = "Modelo Cigarrillos: Original vs HAC Corregido",
align = TRUE)
)##
## Modelo Cigarrillos: Original vs HAC Corregido
## ===========================================================
## 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