Ejercicio de colinealidad
Norman Javier Mejía Herrera (MH18013)
27/5/2021
#cargando la base de datos
library(wooldridge)
data(hprice1)
head(force(hprice1),n=5)## price assess bdrms lotsize sqrft colonial lprice lassess llotsize lsqrft
## 1 300 349.1 4 6126 2438 1 5.703783 5.855359 8.720297 7.798934
## 2 370 351.5 3 9903 2076 1 5.913503 5.862210 9.200593 7.638198
## 3 191 217.7 3 5200 1374 0 5.252274 5.383118 8.556414 7.225482
## 4 195 231.8 3 4600 1448 1 5.273000 5.445875 8.433811 7.277938
## 5 373 319.1 4 6095 2514 1 5.921578 5.765504 8.715224 7.829630#Estimando el modelo
library(stargazer)
modelo_hprice<-lm(formula = price~lotsize+sqrft+bdrms,data = hprice1)
stargazer(modelo_hprice, type ="text",title ="modelo estimado")##
## modelo estimado
## ===============================================
## Dependent variable:
## ---------------------------
## price
## -----------------------------------------------
## lotsize 0.002***
## (0.001)
##
## sqrft 0.123***
## (0.013)
##
## bdrms 13.853
## (9.010)
##
## Constant -21.770
## (29.475)
##
## -----------------------------------------------
## Observations 88
## R2 0.672
## Adjusted R2 0.661
## Residual Std. Error 59.833 (df = 84)
## F Statistic 57.460*** (df = 3; 84)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01#Matiz X´X
library(stargazer)
mat_x<-model.matrix(modelo_hprice)
stargazer(head(mat_x, n=5),type="text")##
## =================================
## (Intercept) lotsize sqrft bdrms
## ---------------------------------
## 1 1 6,126 2,438 4
## 2 1 9,903 2,076 3
## 3 1 5,200 1,374 3
## 4 1 4,600 1,448 3
## 5 1 6,095 2,514 4
## ---------------------------------#formando matriz xx
mat_XX<-t(mat_x)%*%mat_x
stargazer(mat_XX,type = "text")##
## ==============================================================
## (Intercept) lotsize sqrft bdrms
## --------------------------------------------------------------
## (Intercept) 88 793,748 177,205 314
## lotsize 793,748 16,165,159,010 1,692,290,257 2,933,767
## sqrft 177,205 1,692,290,257 385,820,561 654,755
## bdrms 314 2,933,767 654,755 1,182
## --------------------------------------------------------------#Verificando la independencia de los regresores indice de condicion
options(scipen=999999)
library(mctest)
library(stargazer)
eigprop(modelo_hprice)##
## Call:
## eigprop(mod = modelo_hprice)
##
## Eigenvalues CI (Intercept) lotsize sqrft bdrms
## 1 3.4816 1.0000 0.0037 0.0278 0.0042 0.0029
## 2 0.4552 2.7656 0.0068 0.9671 0.0061 0.0051
## 3 0.0385 9.5082 0.4726 0.0051 0.8161 0.0169
## 4 0.0247 11.8678 0.5170 0.0000 0.1737 0.9750
##
## ===============================
## Row 2==> lotsize, proportion 0.967080 >= 0.50
## Row 3==> sqrft, proportion 0.816079 >= 0.50
## Row 4==> bdrms, proportion 0.975026 >= 0.50Prueba FG
options(scipen = 999999)
library(psych)
library(stargazer)
library(fastGraph)
prueb_FG<-cortest.bartlett(mat_x[,-1])
prueba_FG<-t(prueb_FG)
stargazer(prueba_FG,title = "Resultados de Prueba FG",
type = "text")##
## Resultados de Prueba FG
## ===========================================
## chisq p.value df
## -------------------------------------------
## 31.3812164469326 0.000000706580566587692 3
## -------------------------------------------#Mostrando resultado es grafica
gl<-3
fg_vc<-qchisq(p=0.96,df=gl)
shadeDist(xshade = prueb_FG$chisq,ddist = "dchisq",parm1 = prueb_FG$df,lower.tail = FALSE,sub = paste("VC",fg_vc,"FG",prueb_FG$chisq))
#Factores inflacionarios de la varianza
options(scipen = 999999)
library(car)
library(mctest)
vif<-vif(modelo_hprice)
print(vif)## lotsize sqrft bdrms
## 1.037211 1.418654 1.396663mc.plot(mod = modelo_hprice,vif=2)