1 Matriz de normalización Sn.

load(file = "C:/Users/Rivera/Downloads/data_clave_C.RData")
library(stargazer)
## 
## Please cite as:
##  Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.
##  R package version 5.2.2. https://CRAN.R-project.org/package=stargazer
XX_matriX<-t(X) %*% X
stargazer(XX_matriX, type = "text")
## 
## ================================================================================
##             (Intercept)    AGE      S      ED        EX0       EX1        LF    
## --------------------------------------------------------------------------------
## (Intercept)     47        6,513    16     4,965     3,995     3,771     26,376  
## AGE            6,513     909,801  2,379  684,593   544,916   514,269  3,651,280 
## S               16        2,379    16     1,517     1,116     1,052     8,529   
## ED             4,965     684,593  1,517  530,251   429,411   405,548  2,797,986 
## EX0            3,995     544,916  1,116  429,411   380,203   358,515  2,248,672 
## EX1            3,771     514,269  1,052  405,548   358,515   338,527  2,121,781 
## LF            26,376    3,651,280 8,529 2,797,986 2,248,672 2,121,781 14,877,110
## --------------------------------------------------------------------------------

Normalizacion XtX

library(stargazer)
options(scipen=999)
Sn<-solve(diag(sqrt(diag(XX_matriX))))
stargazer(Sn, type = "text")
## 
## ==========================================
## 0.146   0     0     0     0     0     0   
## 0     0.001   0     0     0     0     0   
## 0       0   0.250   0     0     0     0   
## 0       0     0   0.001   0     0     0   
## 0       0     0     0   0.002   0     0   
## 0       0     0     0     0   0.002   0   
## 0       0     0     0     0     0   0.0003
## ------------------------------------------

\(\mid X^t X\mid\) Normalizada:

library(stargazer)
XXnorm<-(Sn%*%XX_matriX)%*%Sn
stargazer(XXnorm, type = "text")
## 
## =========================================
## 1     0.996 0.583 0.995 0.945 0.945 0.997
## 0.996   1   0.624 0.986 0.927 0.927 0.992
## 0.583 0.624   1   0.521 0.452 0.452 0.553
## 0.995 0.986 0.521   1   0.956 0.957 0.996
## 0.945 0.927 0.452 0.956   1   0.999 0.945
## 0.945 0.927 0.452 0.957 0.999   1   0.945
## 0.997 0.992 0.553 0.996 0.945 0.945   1  
## -----------------------------------------

2 los eigenvalues de XXnorm y calculo del indice de condicion.

3. Vector de factores inflacionarios de Varianza.

Matriz de Correlacion de los regresores del modelo.

print(R)
##           AGE         S        ED       EX0       EX1        LF
## AGE  1.000000  0.584355 -0.530240 -0.505737 -0.513173 -0.160949
## S    0.584355  1.000000 -0.702741 -0.372636 -0.376168 -0.505469
## ED  -0.530240 -0.702741  1.000000  0.482952  0.499410  0.561178
## EX0 -0.505737 -0.372636  0.482952  1.000000  0.993586  0.121493
## EX1 -0.513173 -0.376168  0.499410  0.993586  1.000000  0.106350
## LF  -0.160949 -0.505469  0.561178  0.121493  0.106350  1.000000

Inversa de la Matriz de Correlación \(R^{-1}\):

inversa_R<-solve(R)
print(inversa_R)
##            AGE          S         ED         EX0         EX1         LF
## AGE  1.8916760 -0.8482687  0.3852114   0.3547993   0.1492460 -0.3994603
## S   -0.8482687  2.4505377  1.0202661   0.4611896  -0.5377022  0.5307452
## ED   0.3852114  1.0202661  2.9734031   4.1964513  -4.9588590 -1.0733609
## EX0  0.3547993  0.4611896  4.1964513  86.4656601 -87.3018851 -3.2851515
## EX1  0.1492460 -0.5377022 -4.9588590 -87.3018851  89.7097948  3.6009631
## LF  -0.3994603  0.5307452 -1.0733609  -3.2851515   3.6009631  1.8224895

VIF´s para el modelo estimado:

VIFs<-diag(inversa_R)
print(VIFs)
##       AGE         S        ED       EX0       EX1        LF 
##  1.891676  2.450538  2.973403 86.465660 89.709795  1.822490

¿Que variables se consideran son colineales? las variables “S”, “ED”, “EX0”, y “EX1”

4 Residuos del modelo.

5. Pruebas de normalidad.