Estimació HAC

Modelo Estimado

load("C:/Users/Walter Alemán/Desktop/UES V/ECONOMETRIA/UNIDAD 2 PARTE 3/Ejercicio modelos corregidos con estimadores HAC/DATA.RData")
options(scipen = 99999)
library(lmtest)
library(dplyr)
Mod_Est<-lm(cigs~ cigpric+lcigpric+income+lincome+age+agesq+educ+white+restaurn, data = data)
coeftest(Mod_Est)

## 
## 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

Prueba de Heterocedasticidad y Autocorrelacion

Prueba Heterocedasticidad

Prueba de White (prueba de Breusch Pagan)

library(lmtest)
Test_White<-bptest(Mod_Est,~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(Test_White)

## 
##  studentized Breusch-Pagan test
## 
## data:  Mod_Est
## BP = 26.323, df = 9, p-value = 0.001809

Hay evidencia de Heterocedasticidad ya que el pvalue (0.001) < 0.05

Prueba del Multiplicador de Lagrange (Breusch Godfrey)

Autecorrelacion de 2° orden

library(lmtest)
Pre_LM<-bgtest(Mod_Est,order = 2)
print(Pre_LM)

## 
##  Breusch-Godfrey test for serial correlation of order up to 2
## 
## data:  Mod_Est
## LM test = 0.26889, df = 2, p-value = 0.8742

No hay evidencia de Autocorrelación de 2º orden ya que pvalue>0.05

Autocorrelacion de 1° orden

library(car)
durbinWatsonTest(model = Mod_Est)

##  lag Autocorrelation D-W Statistic p-value
##    1    -0.009243664      2.017442   0.888
##  Alternative hypothesis: rho != 0

No hay evidencia de Autocorrelación de 2º orden ya que pvalue>0.05

Estimación Robusta (uso del estimador HAC)

Sin Corregir

options(scipen = 99999)
library(lmtest)
coeftest(Mod_Est)

## 
## 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

Corregido (usando un estimador HAC)

options(scipen = 99999)
library(lmtest)
library(sandwich)
vcoc_HAC<-vcovHC(Mod_Est, type= "HC0")
coeftest(Mod_Est,vcov. = vcoc_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

Estimacion Robusta

library(stargazer)
library(sandwich)
vcoc_HAC<-vcovHC(Mod_Est,type = "HC0")
robust.se<-sqrt(diag(vcoc_HAC))

stargazer(Mod_Est, Mod_Est, se=list(NULL,robust.se),
          column.labels = c("Original","Corregido"), align = TRUE, type = "html", title = "Modelo de consumo de cigarros corregido por heterocedasticidad")

**Modelo de consumo de cigarros corregido por heterocedasticidad**

	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