options(scipen = 999999)
load("C:/Users/Usuario/Downloads/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
## 54 125.113605calculo de la varianza del error
#Sigma cuadrado
k<-4
var_error=t(residuos)%*%residuos/(n-k)
print(var_error)## [,1]
## [1,] 1428.746Matriz de Var_Cov
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 5.3203789879calculo de estimaciones
C_estimada<-P%*%C
cuadro<- as.data.frame(cbind(C,C_estimada,residuos))
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 6132.6864cbind(C,C_estimada,residuos)## C
## 1 976.4 982.2591 -5.859103
## 2 998.1 995.4949 2.605057
## 3 1025.3 979.5343 45.765735
## 4 1090.9 1059.7976 31.102448
## 5 1107.1 1128.1379 -21.037889
## 6 1142.4 1135.3919 7.008120
## 7 1197.2 1179.3403 17.859663
## 8 1221.9 1211.1944 10.705631
## 9 1310.4 1288.3977 22.002328
## 10 1348.8 1351.4897 -2.689665
## 11 1381.8 1374.0159 7.784083
## 12 1393.0 1406.1277 -13.127696
## 13 1470.7 1453.1784 17.521565
## 14 1510.8 1493.4953 17.304695
## 15 1541.2 1557.5083 -16.308260
## 16 1617.3 1622.5555 -5.255508
## 17 1684.0 1681.2118 2.788211
## 18 1784.8 1801.1793 -16.379339
## 19 1897.6 1911.9276 -14.327554
## 20 2006.1 1994.3509 11.749135
## 21 2066.2 2097.6247 -31.424669
## 22 2184.2 2207.5296 -23.329596
## 23 2264.8 2242.6282 22.171806
## 24 2317.5 2322.5400 -5.040038
## 25 2405.2 2441.3914 -36.191398
## 26 2550.5 2575.7118 -25.211753
## 27 2675.9 2697.3113 -21.411271
## 28 2653.7 2652.2895 1.410519
## 29 2710.9 2735.1296 -24.229564
## 30 2868.9 2847.9282 20.971808
## 31 2992.1 2948.7573 43.342653
## 32 3124.7 3087.8915 36.808458
## 33 3203.2 3185.3177 17.882297
## 34 3193.0 3226.1003 -33.100273
## 35 3236.0 3273.8200 -37.819995
## 36 3275.5 3324.8708 -49.370820
## 37 3454.3 3430.8439 23.456143
## 38 3640.6 3666.1103 -25.510341
## 39 3820.9 3832.8606 -11.960629
## 40 3981.2 3990.4342 -9.234201
## 41 4113.4 4091.4504 21.949616
## 42 4279.5 4276.2889 3.211123
## 43 4393.7 4408.2114 -14.511436
## 44 4474.5 4471.3024 3.197576
## 45 4466.6 4528.9968 -62.396763
## 46 4594.5 4661.3545 -66.854500
## 47 4748.9 4740.5693 8.330745
## 48 4928.1 4836.1366 91.963380
## 49 5075.6 5013.9793 61.620735
## 50 5237.5 5189.3511 48.148861
## 51 5423.9 5434.6177 -10.717721
## 52 5683.7 5767.7697 -84.069717
## 53 5968.4 6024.8266 -56.426627
## 54 6257.8 6132.6864 125.113605names(cuadro)<-c("C","C_estimada","residuos")
print(head(cuadro))## C C_estimada residuos
## 1 976.4 982.2591 -5.859103
## 2 998.1 995.4949 2.605057
## 3 1025.3 979.5343 45.765735
## 4 1090.9 1059.7976 31.102448
## 5 1107.1 1128.1379 -21.037889
## 6 1142.4 1135.3919 7.008120