#Cargar datos
load("C:/Users/Usuario/Downloads/DATA")options(scipen = 999999)
library(lmtest)## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericmodelo_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#Modelo usando Stargazer
library(stargazer)##
## Please cite as:## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.3. https://CRAN.R-project.org/package=stargazeroptions(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#Resumen de Stargazer
library(stargazer)
stargazer(modelo_smoke, title = "Resumen del modelo con stargazer", type = "text")##
## Resumen del modelo con stargazer
## ===============================================
## 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#Heterocedasticidad
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#Multiplicador de Lagrange (Breusch Godfrey) - Pruebas de Autocorrelación
#1 auto
library(car)## Loading required package: carDatadurbinWatsonTest(model = modelo_smoke)## lag Autocorrelation D-W Statistic p-value
## 1 -0.009243664 2.017442 0.89
## Alternative hypothesis: rho != 0#2 auto
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#vcov_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 278.565072885 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 ' ' 1#modelo original y corregido
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 = "text", title = "Consumo de cigarros")##
## Consumo de cigarros
## ===========================================================
## 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
##
## Consumo de cigarros
## ====
## TRUE
## ----