Aplicacion para modelos corregidos estimadores HAC
UNIVERCIDAD DE EL SALVADOR
FACULDAD DE CIENCIAS ECONOMICAS
Docente: Carlos Ademir Perez Alas
Alumna: Marta Abigail Meza Robles
DUE: MR21132
Carga de datos
load("C:/Users/abyme/Downloads/Marta Abigail Meza Robles - smoke.RData")Modelo a Estimar
options(scipen = 9999999)
library(foreign)
modelo<-lm(cigs~cigpric+lcigpric+income+lincome+age+agesq+educ+white+restaurn,data = data)
print(modelo)##
## 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(equatiomatic)
equatiomatic::extract_eq(modelo)\[ \operatorname{cigs} = \alpha + \beta_{1}(\operatorname{cigpric}) + \beta_{2}(\operatorname{lcigpric}) + \beta_{3}(\operatorname{income}) + \beta_{4}(\operatorname{lincome}) + \beta_{5}(\operatorname{age}) + \beta_{6}(\operatorname{agesq}) + \beta_{7}(\operatorname{educ}) + \beta_{8}(\operatorname{white}) + \beta_{9}(\operatorname{restaurn}) + \epsilon \]
Resumen del modelo con stargazer
options(scipen = 999999)
library(stargazer)
modelo_Hac<-lm(cigs~cigpric+lcigpric+income+lincome+age+agesq+educ+white+restaurn,data = data)
stargazer(modelo,type = "html",title = "Modelo para estimadores HAC")| 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 |
Verifique si la matriz de Varianza-Covarianza del modelo es escalar, de no ser escalar indique la fuente del problema.
Prueba de White (prueba de Breusch Pagan)
library(lmtest)
white_test<-bptest(modelo,~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)+(cigpric*lcigpric)+(cigpric*income)+(cigpric*lincome)+(cigpric*age)+(cigpric*agesq)+(cigpric*educ)+(cigpric*white),data = data)
print(white_test)##
## studentized Breusch-Pagan test
##
## data: modelo
## BP = 42.143, df = 21, p-value = 0.004037Existe evidencia de que la varianza de los residuos es heterocedastica ya que p-value < 0.05
Prueba del Multiplicador de Lagrange (Breusch Godfrey)
Autocorrelación de 2º Orden:
library(lmtest)
prueba_LM<-bgtest(modelo,order = 2)
print(prueba_LM)##
## Breusch-Godfrey test for serial correlation of order up to 2
##
## data: modelo
## LM test = 0.26889, df = 2, p-value = 0.8742No hay evidencia de Autocorrelación de 2º orden ya que pvalue > 0.05
Autocorrelación de 1º orden (prueba de Durbin Watson)
library(car)
durbinWatsonTest(model = modelo)## lag Autocorrelation D-W Statistic p-value
## 1 -0.009243664 2.017442 0.862
## Alternative hypothesis: rho != 0No hay evidencia de Autocorrelación de 1º orden ya que pvalue>0.05
Estimador HAC apropiado y denominelo vcov_HAC
Sin corregir
options(scipen = 99999)
library(lmtest)
#Sin corregir:
coeftest(modelo)##
## 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)
options(scipen = 99999)
library(lmtest)
library(sandwich)
#Corregido
#HC0 Corrige Sólo Heterocedasticidad, use HC1 para corregir también Autocorrelación de Primer
vcov_HAC<-vcovHC(modelo,type = "HC0")
coeftest(modelo,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 ' ' 1Usando la libreria stargazer, presente el modelo “original” y el modelo “corregido” en una sola tabla (consulte la documentación de stargazer)
library(stargazer)
library(sandwich)
vcov_HAC<-vcovHC(modelo,type="HC0")
robust.se<-sqrt(diag(vcov_HAC))
stargazer(modelo,modelo,
se=list(NULL, robust.se),
column.labels=c("Modelo Original","Modelo Corregido"),
align=TRUE,
type = "html",title = "Modelo de consumo")| Dependent variable: | ||
| cigs | ||
| Modelo Original | Modelo 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 | |