# UNIVERSIDAD NACIONAL DEL ALTIPLANO
# INGENIERIA ESTADISTICA E INFORMATICA
# CURSO: SERIES DE TIEMPO
library(readxl)
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library(lubridate)
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library(tidyverse)
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library(tseries)
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library(astsa)
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library(foreign)
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library(timsac)
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library(lmtest)
library(mFilter)
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library(dynlm)
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library(nlme)
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library(broom)
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library(knitr)
library(MASS)
library(parallel)
library(car)
library(mlogit)
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library(forecast)
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library(fpp2)
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library(stats)
library(quantmod)
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library(FinTS)
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library(fGarch)
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library(parallel)
preciopm <- read_excel("E:/SERIES DE TIEMPO/TAREA 07/preciopm.xls")
preciopm
## # A tibble: 352 x 1
## Precio
## <dbl>
## 1 14.9
## 2 13.9
## 3 12.3
## 4 10.9
## 5 10.5
## 6 9.82
## 7 13.2
## 8 20.8
## 9 26.2
## 10 25.5
## # ... with 342 more rows
#View(preciopm)
attach(preciopm)
names(preciopm)
## [1] "Precio"
Preciopm <- preciopm$Precio
Preciopm
## [1] 14.897350 13.856284 12.298178 10.927095 10.452086 9.817348
## [7] 13.188972 20.825314 26.238379 25.461774 23.590050 19.221472
## [13] 13.795437 10.845017 11.366329 12.068577 11.991099 11.930451
## [19] 12.852894 12.724638 13.357160 14.094443 11.643019 10.081917
## [25] 10.079426 10.222971 10.928616 12.263161 13.882490 14.717433
## [31] 14.694666 14.592165 15.002168 15.047736 13.499847 11.999895
## [37] 11.803837 12.505585 12.812625 12.873810 12.220930 11.106039
## [43] 11.024373 11.150484 11.278000 11.218994 9.715306 9.101101
## [49] 10.227919 9.954569 10.307307 11.943946 12.818879 14.009499
## [55] 14.525943 12.763118 12.527938 13.827166 14.100563 13.831668
## [61] 14.282580 14.800758 15.121214 16.031652 16.052267 14.611588
## [67] 13.256370 13.449261 13.363175 12.991089 13.580205 15.116482
## [73] 14.640357 15.096147 16.682205 17.461726 16.069193 15.561163
## [79] 15.978825 17.149939 19.308046 20.562573 18.913614 19.316873
## [85] 18.060119 15.709637 14.482912 14.259241 14.834376 13.795084
## [91] 14.158890 14.821528 14.748565 15.700761 14.316305 11.438379
## [97] 9.713509 8.882054 7.631504 9.058311 9.082049 8.297230
## [103] 8.804921 8.239905 9.465377 9.128815 7.865962 6.349166
## [109] 7.392438 7.126746 9.856138 12.350982 12.237096 13.074408
## [115] 15.319650 17.430406 19.077463 18.434955 19.961135 20.140117
## [121] 21.348773 23.547154 22.935065 20.260149 23.193243 24.744170
## [127] 22.778127 24.355191 25.822586 25.011949 24.065706 17.812948
## [133] 18.309991 18.799571 16.851520 16.360149 18.199624 18.789678
## [139] 18.433045 19.274814 19.325549 15.571857 13.259511 13.662242
## [145] 14.032789 15.278664 19.405427 21.897267 21.987875 21.507413
## [151] 22.389223 23.253909 24.514419 22.826465 20.001942 23.282675
## [157] 27.074817 27.012354 23.663690 20.458351 21.904876 23.570076
## [163] 24.909283 25.174429 22.513946 24.135865 24.170211 24.437509
## [169] 25.316733 25.162286 26.962854 28.014928 30.823501 29.251947
## [175] 30.770350 32.403355 33.356997 37.293723 30.656835 26.902246
## [181] 30.012074 32.092964 36.346343 38.118350 38.912862 43.204432
## [187] 44.357927 47.831527 48.880013 45.437368 41.707053 42.989153
## [193] 47.779745 46.563260 49.381610 54.956204 55.260986 53.833091
## [199] 56.818337 57.866679 49.690903 46.744199 45.794018 47.642082
## [205] 42.745920 47.033095 48.943810 52.898664 54.958534 58.682133
## [211] 63.212159 62.190669 66.439359 71.018809 78.145548 77.690069
## [217] 78.879230 79.714309 87.177370 93.028973 102.884604 112.291047
## [223] 118.026634 105.209472 83.841054 58.685407 40.592316 32.916936
## [229] 37.230832 37.848297 41.678599 47.490271 56.016166 63.641365
## [235] 60.191980 66.404843 64.108802 67.933160 71.534239 68.493029
## [241] 70.866452 69.134714 70.093660 72.292077 66.101763 65.540751
## [247] 66.923774 68.245485 68.332474 72.942531 75.758392 80.208666
## [253] 84.324640 89.161930 99.432700 106.104005 101.835090 100.828631
## [259] 102.599593 96.988597 97.634395 99.366005 105.806100 105.126973
## [265] 107.455818 108.451418 109.604263 104.893764 100.233358 90.181843
## [271] 94.012683 99.512167 100.076628 97.108629 93.682388 94.939944
## [277] 97.982782 103.352938 100.770902 97.755385 97.906861 96.993153
## [283] 99.672555 99.244140 98.145716 93.873293 88.485898 89.409659
## [289] 88.310808 90.822240 91.395224 93.888630 95.086309 97.011046
## [295] 93.320166 89.507449 84.128243 72.509358 63.479800 50.181375
## [301] 40.451272 45.986060 46.017331 49.325551 52.039105 52.825725
## [307] 45.165949 38.419266 36.290362 34.860768 32.508829 26.196096
## [313] 23.102382 23.729330 28.326563 31.335818 36.726361 39.807146
## [319] 37.971526 37.740903 36.826661 40.668356 38.361623 42.325962
## [325] 44.500081 44.174971 41.932428 43.221180 43.850742 41.151050
## [331] 43.877714 45.554842 48.162370 48.891135 53.345048 54.055756
## [337] 57.453995 56.156762 57.224584 58.159732 62.899289 64.636865
## [343] 66.421619 64.265666 68.363482 71.153304 59.825830 51.868669
## [349] 54.056660 57.379810 59.464492 62.077030
# Generar Serie de Tiempo de Rendimeintos
precio <- ts(Precio,start = c(2000,02), frequency = 12)
chartSeries(precio)
# Estimar la ecuacion de preciopm
precio.mean <- dynlm(precio ~ 1, data = preciopm)
summary(precio.mean)
##
## Time series regression with "ts" data:
## Start = 2000(2), End = 2029(5)
##
## Call:
## dynlm(formula = precio ~ 1, data = preciopm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.43 -25.99 -12.61 19.14 77.25
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 40.778 1.609 25.35 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 30.18 on 351 degrees of freedom
arima22 <- arima(precio,order = c(2,0,2))
#Calcular los Residuales al Cuadrado
rescuad <- resid(arima22)^2
rescuad
## Jan Feb Mar Apr May
## 2000 7.992110e+00 1.122990e+00 1.811396e+00 8.033846e-01
## 2001 1.105986e+01 1.183476e+01 1.634808e-01 2.218577e+00 7.653346e-02
## 2002 2.320950e-01 2.622240e-01 1.885075e-01 3.912699e-02 3.921104e-01
## 2003 8.560824e-01 7.421088e-02 1.101830e-01 2.504947e-01 1.710734e-01
## 2004 1.984278e-02 1.129677e+00 1.914410e+00 1.981171e-02 1.358904e+00
## 2005 4.942817e-01 1.491806e-01 4.720345e-03 9.259098e-02 2.227558e-01
## 2006 7.475798e-01 2.774870e+00 2.330309e-01 1.236997e+00 2.110999e-01
## 2007 1.461034e+00 2.965274e+00 4.225616e+00 2.704014e-02 8.719470e-04
## 2008 5.895186e+00 1.730321e-01 1.524632e-01 1.874374e+00 2.770658e+00
## 2009 1.247886e+00 2.016204e+00 1.853223e+00 5.835247e+00 4.835006e-01
## 2010 7.561569e-01 6.499317e-01 1.984851e+00 4.179838e+00 5.962702e+00
## 2011 3.422026e+01 1.306651e+01 3.986648e-02 8.325589e+00 2.296676e-01
## 2012 1.701778e+00 2.321138e-01 4.095340e-01 1.034662e+01 2.263102e-03
## 2013 2.168263e+01 2.430280e+00 6.103010e+00 9.785872e+00 1.766941e+00
## 2014 1.351812e-02 4.921095e-01 7.183426e-01 2.940555e+00 3.944818e-03
## 2015 5.403151e-03 2.710030e+01 1.473254e-01 8.319925e+00 9.348408e-02
## 2016 1.122598e+01 1.422986e+01 1.588203e+01 1.459536e+01 1.925700e+01
## 2017 3.206919e+00 3.480277e+01 5.059871e+01 1.316767e-01 7.100218e+00
## 2018 1.316221e+01 5.045957e+00 1.001053e+00 5.334950e+01 5.353979e+00
## 2019 7.678949e-01 4.898384e+01 7.198702e+00 1.132244e+01 1.706276e+01
## 2020 2.382410e+01 2.241109e+01 6.450855e+00 4.000901e+00 4.883196e+00
## 2021 1.111005e+01 6.032401e+00 1.040150e+01 7.112345e+01 3.568579e+00
## 2022 1.115233e+01 1.381716e+01 7.563039e-01 1.597865e+00 1.899179e+01
## 2023 1.335467e+01 7.383005e+00 1.771186e+01 2.147404e+01 3.356121e-01
## 2024 1.914109e+01 1.893292e+00 1.172933e+01 4.604830e-03 7.451909e+00
## 2025 7.071266e+01 8.938121e+00 1.082560e+02 1.496917e+01 9.116088e+00
## 2026 2.597313e+01 3.225305e-02 3.648761e+00 1.301358e+01 4.276616e-02
## 2027 2.749357e+01 4.496806e-02 2.802108e+00 2.833626e+00 7.035899e+00
## 2028 2.247733e+00 1.042282e+01 7.298237e+00 3.986616e+00 5.193074e-01
## 2029 1.016899e+00 4.548928e+01 1.617295e+00 1.618963e-02 4.329960e+00
## Jun Jul Aug Sep Oct
## 2000 2.033965e-02 7.028946e-01 1.080338e+01 2.918286e+01 8.206292e-01
## 2001 8.018110e-01 6.224724e-02 4.130387e-01 1.019605e+00 1.450395e-01
## 2002 2.984801e-01 1.120120e-01 4.895176e-01 9.354452e-02 3.110778e-02
## 2003 9.295923e-01 1.180266e+00 3.042036e-02 6.047180e-02 1.322294e-01
## 2004 1.828670e-01 1.703104e-01 1.201166e-01 5.400344e+00 2.355802e-01
## 2005 5.675464e-01 2.980542e+00 6.456507e-01 3.436110e-01 4.161857e-01
## 2006 4.196401e+00 9.444140e-03 1.588013e-01 2.653482e-01 1.486636e+00
## 2007 4.736306e-02 2.916861e+00 4.175486e-01 2.730962e-02 6.988018e-01
## 2008 1.422276e+00 1.514755e+00 4.647295e-01 1.606433e+00 1.199431e+00
## 2009 3.428878e+00 6.114005e-01 2.456479e+00 2.395893e-01 7.624837e-02
## 2010 1.859834e+01 1.887949e-01 1.000932e+01 7.583882e+00 1.717868e-01
## 2011 3.385063e+00 7.886283e-01 8.410565e-01 8.684712e-01 4.703588e-01
## 2012 1.964543e+00 2.071218e-01 1.020389e+00 7.606174e-03 3.001588e-01
## 2013 8.121718e+00 1.209148e-01 5.956719e-03 2.281743e-01 8.346910e+00
## 2014 4.258604e+00 9.771102e+00 5.499178e+00 6.477617e-01 4.103449e-02
## 2015 9.109596e-03 1.664603e+01 1.359284e+00 8.662083e+00 2.598394e-01
## 2016 7.874987e+00 1.286491e+00 1.850949e+01 3.153492e-01 7.452322e+01
## 2017 1.628989e-01 8.247707e+00 8.342836e+00 1.000927e+01 2.868130e+01
## 2018 5.430694e+01 2.705831e+01 2.964215e+00 2.080856e+02 1.653369e+02
## 2019 2.854455e+01 1.111341e+01 4.870390e+01 8.022590e+01 2.677365e+01
## 2020 5.144271e+01 1.106054e+01 4.219544e+00 1.364240e-01 5.137816e-02
## 2021 4.664605e+01 8.036261e+00 1.055216e+01 4.008592e+01 2.039788e+01
## 2022 1.400198e+00 4.551884e+01 9.306620e+01 1.303653e+01 5.490515e+00
## 2023 7.769440e+00 4.188506e-01 1.369470e+01 1.298974e+00 3.345829e-02
## 2024 3.852887e-01 3.777793e+00 1.543241e+01 1.086880e+00 6.798707e+00
## 2025 2.448697e+00 4.353067e-01 5.986126e+01 4.992756e+00 2.258453e+00
## 2026 1.428201e+01 1.233153e-01 1.147531e+01 1.239929e+00 5.410291e-01
## 2027 3.513224e-02 9.864624e+00 1.934401e+01 2.864303e-02 2.406693e+00
## 2028 1.844786e+01 3.071591e-01 1.503991e+00 6.361535e+00 3.184120e+01
## 2029
## Nov Dec
## 2000 1.317705e+01 1.153005e+00
## 2001 1.445791e-02 1.026190e+01
## 2002 2.649931e-01 3.536683e+00
## 2003 2.147549e-01 3.277677e+00
## 2004 1.169966e+00 8.406780e-01
## 2005 5.072760e-01 2.593262e-01
## 2006 2.231489e-02 6.218556e+00
## 2007 5.400940e-01 4.729760e+00
## 2008 1.825792e+00 2.194991e+00
## 2009 2.713081e+00 3.027037e+00
## 2010 3.914609e+00 2.542508e-01
## 2011 1.657815e+01 1.909621e-01
## 2012 6.247914e+00 4.126532e+00
## 2013 8.669815e+00 1.256190e+00
## 2014 1.201185e+01 7.697499e+01
## 2015 1.509324e+01 2.325063e+00
## 2016 4.068962e+00 4.517125e-01
## 2017 7.474692e+00 2.317848e+01
## 2018 1.599886e+02 2.592350e+01
## 2019 2.580770e+01 5.039428e+00
## 2020 2.552842e+01 4.368520e-01
## 2021 4.617180e+00 3.295055e+01
## 2022 4.103477e+00 5.540748e-01
## 2023 7.733971e+00 5.975006e+00
## 2024 7.102964e+01 5.788324e+00
## 2025 8.799746e-01 3.854093e+00
## 2026 1.683200e+01 1.981484e+01
## 2027 1.396885e-01 1.812677e+01
## 2028 7.494063e-01 1.598382e+02
## 2029
chartSeries(rescuad)
#HAcer una Regresion con los residuales al2 rezagados
precio.arch <- dynlm(rescuad ~ L(rescuad), data = preciopm)
summary(precio.arch)
##
## Time series regression with "ts" data:
## Start = 2000(3), End = 2029(5)
##
## Call:
## dynlm(formula = rescuad ~ L(rescuad), data = preciopm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -59.740 -7.489 -6.086 0.836 200.134
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.95335 1.29298 5.378 1.38e-07 ***
## L(rescuad) 0.33662 0.05041 6.678 9.59e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.1 on 349 degrees of freedom
## Multiple R-squared: 0.1133, Adjusted R-squared: 0.1108
## F-statistic: 44.59 on 1 and 349 DF, p-value: 9.587e-11
#Probamos con archtest
precioarchtest <- ArchTest(precio, lags = 1, demean = T)
precioarchtest
##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: precio
## Chi-squared = 327.78, df = 1, p-value < 2.2e-16
acf.res2 <- acf(rescuad,main = "ACF Residuales al Cuadrado", lag.max = 100, ylim = c(-0.5,1))
acf.res2 <- pacf(rescuad,main = "PACF Residuales al Cuadrado", lag.max = 100, ylim = c(-0.5,1))
