Numeral 1

load("C:/Users/gusta_000/Desktop/Econometria/Guia/consumption_equation.RData")

n <- nrow(P)
M <- diag(n)-P
residuos <- M%*%C
print(residuos)
##          [,1]
## 1   -5.859103
## 2    2.605057
## 3   45.765735
## 4   31.102448
## 5  -21.037889
## 6    7.008120
## 7   17.859663
## 8   10.705631
## 9   22.002328
## 10  -2.689665
## 11   7.784083
## 12 -13.127696
## 13  17.521565
## 14  17.304695
## 15 -16.308260
## 16  -5.255508
## 17   2.788211
## 18 -16.379339
## 19 -14.327554
## 20  11.749135
## 21 -31.424669
## 22 -23.329596
## 23  22.171806
## 24  -5.040038
## 25 -36.191398
## 26 -25.211753
## 27 -21.411271
## 28   1.410519
## 29 -24.229564
## 30  20.971808
## 31  43.342653
## 32  36.808458
## 33  17.882297
## 34 -33.100273
## 35 -37.819995
## 36 -49.370820
## 37  23.456143
## 38 -25.510341
## 39 -11.960629
## 40  -9.234201
## 41  21.949616
## 42   3.211123
## 43 -14.511436
## 44   3.197576
## 45 -62.396763
## 46 -66.854500
## 47   8.330745
## 48  91.963380
## 49  61.620735
## 50  48.148861
## 51 -10.717721
## 52 -84.069717
## 53 -56.426627
## 54 125.113605

Numeral 2

#Sigma cuadrado
k <- 4
var_error <- t(residuos)%*%residuos/(n-k)
print(var_error)
##          [,1]
## [1,] 1428.746

Matriz de Var-Cov

options(scipen = 999999)
var_error <- as.vector(var_error)
Var_cov <- var_error*solve(XX)
print(Var_cov)
##               (Intercept)             Yd               W             I
## (Intercept) 164.522304918 -0.09333539523  0.009670913575 10.5186890800
## Yd           -0.093335395  0.00018911268 -0.000032769561 -0.0072901023
## W             0.009670914 -0.00003276956  0.000006165749  0.0004193421
## I            10.518689080 -0.00729010228  0.000419342092  5.3203789879

Calculo de las estimaciones

C_estimada <- P%*%C
print(C_estimada)
##         [,1]
## 1   982.2591
## 2   995.4949
## 3   979.5343
## 4  1059.7976
## 5  1128.1379
## 6  1135.3919
## 7  1179.3403
## 8  1211.1944
## 9  1288.3977
## 10 1351.4897
## 11 1374.0159
## 12 1406.1277
## 13 1453.1784
## 14 1493.4953
## 15 1557.5083
## 16 1622.5555
## 17 1681.2118
## 18 1801.1793
## 19 1911.9276
## 20 1994.3509
## 21 2097.6247
## 22 2207.5296
## 23 2242.6282
## 24 2322.5400
## 25 2441.3914
## 26 2575.7118
## 27 2697.3113
## 28 2652.2895
## 29 2735.1296
## 30 2847.9282
## 31 2948.7573
## 32 3087.8915
## 33 3185.3177
## 34 3226.1003
## 35 3273.8200
## 36 3324.8708
## 37 3430.8439
## 38 3666.1103
## 39 3832.8606
## 40 3990.4342
## 41 4091.4504
## 42 4276.2889
## 43 4408.2114
## 44 4471.3024
## 45 4528.9968
## 46 4661.3545
## 47 4740.5693
## 48 4836.1366
## 49 5013.9793
## 50 5189.3511
## 51 5434.6177
## 52 5767.7697
## 53 6024.8266
## 54 6132.6864