Tugas Kel MPDW 4
Mohammad Abror Gustiansyah
2022-09-13
Package
library(dLagM) #bisa otomatis timeseries datanya## Warning: package 'dLagM' was built under R version 4.1.3## Loading required package: nardl## Warning: package 'nardl' was built under R version 4.1.3## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo## Loading required package: dynlm## Warning: package 'dynlm' was built under R version 4.1.3## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericlibrary(dynlm) #data harus timeseries
library(MLmetrics) #MAPE## Warning: package 'MLmetrics' was built under R version 4.1.3##
## Attaching package: 'MLmetrics'## The following object is masked from 'package:dLagM':
##
## MAPE## The following object is masked from 'package:base':
##
## Recalllibrary(lmtest)## Warning: package 'lmtest' was built under R version 4.1.3library(car)## Warning: package 'car' was built under R version 4.1.3## Loading required package: carData## Warning: package 'carData' was built under R version 4.1.3library(readxl)Data
library(readxl)
data<-read.csv("D:/Kuliah/Semester 5/MPDW/Tukel MPDW 3/salesweekly.csv")
str(data)## 'data.frame': 149 obs. of 2 variables:
## $ N02BA: num 37.9 45.9 31.5 20.7 53.3 ...
## $ Close: int 705 700 690 730 770 775 750 760 780 825 ...data## N02BA Close
## 1 37.9000 705
## 2 45.9000 700
## 3 31.5000 690
## 4 20.7000 730
## 5 53.3000 770
## 6 47.3000 775
## 7 44.2000 750
## 8 40.3550 760
## 9 31.8000 780
## 10 36.2000 825
## 11 26.1000 900
## 12 36.2000 895
## 13 36.4000 860
## 14 21.2000 895
## 15 35.1500 900
## 16 22.0000 925
## 17 23.9000 975
## 18 29.6000 975
## 19 35.2000 1010
## 20 33.7000 1050
## 21 25.1000 1015
## 22 33.9000 995
## 23 23.6000 960
## 24 26.1000 980
## 25 37.3000 1000
## 26 30.0000 1090
## 27 34.7000 1160
## 28 20.5000 1230
## 29 33.1000 1230
## 30 23.2000 1250
## 31 28.4000 1260
## 32 34.7000 1340
## 33 20.5000 1320
## 34 29.0000 1270
## 35 23.6000 1225
## 36 22.4000 1205
## 37 28.7000 1165
## 38 31.1000 1070
## 39 30.6000 1115
## 40 33.1000 1180
## 41 28.3375 1125
## 42 25.2000 1155
## 43 34.1000 1250
## 44 29.8000 1310
## 45 31.3000 1390
## 46 37.5000 1390
## 47 39.6000 1425
## 48 18.3000 1425
## 49 30.4000 1375
## 50 24.3400 1435
## 51 27.7840 1445
## 52 24.7000 1355
## 53 32.9000 1290
## 54 38.8000 1310
## 55 32.2000 1340
## 56 29.7000 1280
## 57 31.6000 1275
## 58 44.2000 1295
## 59 20.6300 1430
## 60 31.1000 1365
## 61 23.7000 1365
## 62 33.0000 1300
## 63 24.3000 1320
## 64 28.9000 1295
## 65 42.1000 1285
## 66 30.4000 1205
## 67 32.0000 1190
## 68 24.9000 1230
## 69 23.9400 1190
## 70 30.8000 1130
## 71 39.5000 1130
## 72 23.2000 1120
## 73 32.5000 1030
## 74 27.7000 985
## 75 30.5000 1000
## 76 35.7600 995
## 77 41.5000 1045
## 78 28.3000 1015
## 79 24.2000 1090
## 80 23.0000 1065
## 81 27.8000 990
## 82 40.2000 960
## 83 31.7000 840
## 84 33.6000 700
## 85 29.0000 695
## 86 27.7000 705
## 87 25.3000 665
## 88 34.1000 700
## 89 23.9000 650
## 90 23.5000 625
## 91 22.0000 775
## 92 34.4000 855
## 93 35.9000 935
## 94 30.9000 865
## 95 35.6000 940
## 96 34.7000 935
## 97 42.4000 960
## 98 29.9000 970
## 99 28.3000 960
## 100 29.4000 845
## 101 40.2000 870
## 102 25.1000 840
## 103 26.5000 870
## 104 24.3000 900
## 105 41.3000 945
## 106 50.5000 910
## 107 39.7000 1050
## 108 37.5000 1125
## 109 34.6000 1120
## 110 37.1000 1125
## 111 24.6000 1080
## 112 33.9000 1190
## 113 33.8000 1260
## 114 25.1000 1280
## 115 32.9000 1310
## 116 45.4000 1300
## 117 30.2000 1310
## 118 28.2500 1335
## 119 31.5000 1270
## 120 36.2000 1205
## 121 36.7500 1175
## 122 21.0000 1205
## 123 33.7500 1175
## 124 33.6000 1195
## 125 34.6000 1195
## 126 22.2500 1265
## 127 25.0000 1190
## 128 28.2500 1185
## 129 32.6500 1165
## 130 34.1000 1165
## 131 27.0500 1170
## 132 25.0000 1265
## 133 38.3000 1245
## 134 30.6500 1535
## 135 32.4000 1390
## 136 35.1000 1910
## 137 27.0000 2500
## 138 29.2000 2720
## 139 25.5000 2430
## 140 30.3500 2130
## 141 20.9000 2570
## 142 25.5000 2470
## 143 34.3500 2440
## 144 35.5000 2490
## 145 38.2000 2360
## 146 32.8000 2140
## 147 39.7500 2470
## 148 26.5000 2510
## 149 37.3000 2380#SPLIT DATA
train<-data[c(1:120),]
test<-data[121:149,]#data time series
train.ts<-ts(train)
test.ts<-ts(test)
data.ts<-ts(data)Model KOYCK
Metode Koyck didasarkan asumsi bahwa semakin jauh jarak lag peubah independen dari periode sekarang maka semakin kecil pengaruh peubah lag terhadap peubah dependen Koyck mengusulkan suatu metode untuk menduga model dinamis distributed lag dengan mengasumsikan bahwa semua koefisien 𝛽 mempunyai tanda sama. Model Koyck merupakan jenis paling umum dari model infinite distributed lag dan juga dikenal sebagai geometric lag.
#MODEL KOYCK
model.koyck = dLagM::koyckDlm(x = train$N02BA, y = train$Close)
summary(model.koyck)##
## Call:
## "Y ~ (Intercept) + Y.1 + X.t"
##
## Residuals:
## Min 1Q Median 3Q Max
## -159.465 -40.719 -3.756 46.781 191.239
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -147.26247 265.74060 -0.554 0.581
## Y.1 0.98544 0.04475 22.020 <2e-16 ***
## X.t 5.32613 7.20594 0.739 0.461
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 64.37 on 116 degrees of freedom
## Multiple R-Squared: 0.9133, Adjusted R-squared: 0.9118
## Wald test: 628.2 on 2 and 116 DF, p-value: < 2.2e-16
##
## Diagnostic tests:
## NULL
##
## alpha beta phi
## Geometric coefficients: -10114.31 5.326128 0.9854402AIC(model.koyck)## [1] 1333.858BIC(model.koyck)## [1] 1344.975#ramalan
(fore.koyck <- forecast(model = model.koyck, x=test$N02BA, h=29))## $forecasts
## [1] 1235.928 1182.519 1197.797 1212.052 1231.427 1184.741 1153.382 1139.790
## [9] 1149.830 1167.448 1147.259 1116.446 1156.919 1156.058 1164.530 1187.259
## [17] 1166.516 1157.792 1129.489 1127.429 1075.067 1047.968 1068.400 1094.660
## [25] 1134.917 1145.827 1193.596 1170.097 1204.463
##
## $call
## forecast.koyckDlm(model = model.koyck, x = test$N02BA, h = 29)
##
## attr(,"class")
## [1] "forecast.koyckDlm" "dLagM"#mape data testing
mape.koyck <- MAPE(fore.koyck$forecasts, test$Close)
#akurasi data training
mape_train <- dLagM::GoF(model.koyck)["MAPE"]
c("MAPE_testing" = mape.koyck, "MAPE_training" = mape_train)## $MAPE_testing
## [1] 0.2827228
##
## $MAPE_training.MAPE
## [1] 0.0495913Regression with Distributed Lag
Regression with Distributed Lag (lag=2)
#REGRESSION WITH DISTRIBUTED LAG -> estimasi parameter menggunakan least square
model.dlm = dLagM::dlm(x = train$N02BA,y = train$Close , q = 2)
summary(model.dlm)##
## Call:
## lm(formula = model.formula, data = design)
##
## Residuals:
## Min 1Q Median 3Q Max
## -489.6 -161.4 11.3 179.6 394.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1383.621 143.558 9.638 <2e-16 ***
## x.t -3.275 2.941 -1.114 0.268
## x.1 -2.997 2.918 -1.027 0.307
## x.2 -3.532 2.885 -1.224 0.223
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 213.3 on 114 degrees of freedom
## Multiple R-squared: 0.03937, Adjusted R-squared: 0.01409
## F-statistic: 1.557 on 3 and 114 DF, p-value: 0.2036
##
## AIC and BIC values for the model:
## AIC BIC
## 1 1606.418 1620.271AIC(model.dlm)## [1] 1606.418BIC(model.dlm)## [1] 1620.271#ramalan
(fore.dlm <- forecast(model = model.dlm, x=test$N02BA, h=29)) #meramalkan 29 periode ke depan## $forecasts
## [1] 1043.483 1076.822 1080.325 1098.231 1050.371 1088.355 1112.833 1137.566
## [9] 1103.700 1074.283 1077.487 1100.212 1087.694 1080.127 1050.348 1063.279
## [17] 1075.537 1083.073 1117.208 1104.641 1134.126 1130.253 1120.856 1074.315
## [25] 1030.765 1036.298 1020.182 1061.824 1041.616
##
## $call
## forecast.dlm(model = model.dlm, x = test$N02BA, h = 29)
##
## attr(,"class")
## [1] "forecast.dlm" "dLagM"#mape data testing
mape.dlm <- MAPE(fore.dlm$forecasts, test$Close)
#akurasi data training
mape_train <- GoF(model.dlm)["MAPE"]
c("MAPE_testing" = mape.dlm, "MAPE_training" = mape_train)## $MAPE_testing
## [1] 0.3242882
##
## $MAPE_training.MAPE
## [1] 0.1815142Regression with Distributed Lag Optimum
#penentuan lag optimum
finiteDLMauto(formula = Close ~ N02BA,
data = data.frame(train), q.min = 1, q.max = 4 ,
model.type = "dlm", error.type = "AIC", trace = TRUE) ##q max lag maksimum## q - k MASE AIC BIC GMRAE MBRAE R.Adj.Sq Ljung-Box
## 4 4 4.06503 1578.347 1597.622 15.60200 0.97479 0.00390 0
## 3 3 4.13227 1592.298 1608.871 15.89848 1.35944 0.00739 0
## 2 2 4.18906 1606.418 1620.271 16.20682 0.94842 0.01409 0
## 1 1 4.24384 1620.475 1631.592 15.69009 1.01954 0.01891 0#model dlm dengan lag optimum
model.dlm2 = dLagM::dlm(x = train$N02BA,y = train$Close , q = 4) #terdapat lag yang tidak signifikan sehingga dapat dikurangi jumlah lagnya
summary(model.dlm2)##
## Call:
## lm(formula = model.formula, data = design)
##
## Residuals:
## Min 1Q Median 3Q Max
## -521.81 -147.54 2.44 173.13 390.79
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1481.577 175.464 8.444 1.39e-13 ***
## x.t -3.910 2.947 -1.327 0.187
## x.1 -2.078 2.936 -0.708 0.481
## x.2 -1.880 2.939 -0.640 0.524
## x.3 -2.368 2.886 -0.821 0.414
## x.4 -2.497 2.858 -0.874 0.384
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 210.7 on 110 degrees of freedom
## Multiple R-squared: 0.04721, Adjusted R-squared: 0.003898
## F-statistic: 1.09 on 5 and 110 DF, p-value: 0.37
##
## AIC and BIC values for the model:
## AIC BIC
## 1 1578.347 1597.622AIC(model.dlm2)## [1] 1578.347BIC(model.dlm2)## [1] 1597.622#ramalan
(fore.dlm2 <- forecast(model = model.dlm2, x=test$N02BA, h=29))## $forecasts
## [1] 1061.093 1109.876 1072.465 1063.126 1071.487 1127.120 1108.664 1111.465
## [9] 1109.089 1112.495 1114.213 1115.615 1066.698 1085.905 1092.417 1066.223
## [17] 1073.901 1092.014 1106.374 1103.403 1152.251 1148.052 1119.406 1098.140
## [25] 1081.259 1062.153 1016.296 1054.551 1032.834
##
## $call
## forecast.dlm(model = model.dlm2, x = test$N02BA, h = 29)
##
## attr(,"class")
## [1] "forecast.dlm" "dLagM"#akurasi testing
mape.dlm2 <- MAPE(fore.dlm2$forecasts, test$Close)
#akurasi data training
mape_train <- GoF(model.dlm2)["MAPE"]
c("MAPE_testing" = mape.dlm2, "MAPE_training" = mape_train)## $MAPE_testing
## [1] 0.3182544
##
## $MAPE_training.MAPE
## [1] 0.1750361Model Autoregressive / Dynamic Regression
Apabila peubah dependen dipengaruhi oleh peubah independen pada waktu sekarang, serta dipengaruhii juga oleh peubah dependen itu sendiri pada satu waktu yang lalu maka model tersebut disebut autoregressive (Gujarati, 2004)
#MODEL AUTOREGRESSIVE
#library(dLagM)
model.ardl = ardlDlm(x = train$N02BA, y = train$Close, p = 1 , q = 1) #p:lag x, q:lag y
#model untuk p=1, q=1: yt=b0+b1yt-1+b2xt+b3xt-1
#model untuk p=2, q=3: yt=b0+b1yt-1+b2yt-2+b3xt+b4xt-1+b5xt-2
summary(model.ardl)##
## Time series regression with "ts" data:
## Start = 2, End = 120
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -153.155 -38.507 -0.333 30.191 135.223
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20.89775 43.61669 0.479 0.633
## X.t 0.10169 0.73761 0.138 0.891
## X.1 0.63284 0.73693 0.859 0.392
## Y.1 0.96283 0.02321 41.488 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 53.95 on 115 degrees of freedom
## Multiple R-squared: 0.9396, Adjusted R-squared: 0.938
## F-statistic: 596.3 on 3 and 115 DF, p-value: < 2.2e-16AIC(model.ardl)## [1] 1292.777BIC(model.ardl)## [1] 1306.673#ramalan
(fore.ardl <- forecast(model = model.ardl, x=test$N02BA, h=29))## $forecasts
## [1] 1207.757 1209.159 1201.837 1202.841 1203.815 1204.129 1196.896 1192.003
## [9] 1189.795 1190.602 1191.579 1187.850 1184.314 1188.549 1187.963 1188.781
## [17] 1190.454 1187.162 1185.009 1181.087 1179.419 1172.301 1169.258 1172.046
## [25] 1175.733 1180.442 1182.265 1187.072 1184.413
##
## $call
## forecast.ardlDlm(model = model.ardl, x = test$N02BA, h = 29)
##
## attr(,"class")
## [1] "forecast.ardlDlm" "dLagM"#akurasi testing
mape.ardl <- MAPE(fore.ardl$forecasts, test$Close) #data testing
#akurasi data training
mape_train <- GoF(model.ardl)["MAPE"]
c("MAPE_testing" = mape.ardl, "MAPE_training" = mape_train)## $MAPE_testing
## [1] 0.2658877
##
## $MAPE_training.MAPE
## [1] 0.04125257#penentuan lag optimum
ardlBoundOrders(data = data.frame(data) , formula = Close ~ N02BA ) #yang digunakan harusnya data train, tetapi karena keterbatasan data jika menggunakan data train menyebabkan error sehingga dicontohkan menggunakan keseluruhan data## $p
## N02BA
## 1 15
##
## $q
## [1] 15
##
## $Stat.table
## q = 1 q = 2 q = 3 q = 4 q = 5 q = 6 q = 7 q = 8
## p = 1 1801.998 1789.899 1780.582 1768.581 1759.000 1747.873 1738.580 1722.279
## p = 2 1791.943 1790.810 1781.509 1769.665 1760.079 1749.199 1739.685 1723.867
## p = 3 1781.596 1781.596 1783.507 1771.658 1762.034 1751.187 1741.685 1725.853
## p = 4 1774.217 1774.297 1774.297 1773.542 1763.925 1753.109 1743.614 1727.797
## p = 5 1764.563 1762.128 1763.004 1763.004 1764.965 1754.133 1744.829 1729.505
## p = 6 1754.944 1752.865 1754.599 1755.705 1755.705 1756.130 1746.827 1731.488
## p = 7 1745.201 1742.422 1744.140 1745.447 1746.827 1746.827 1748.827 1733.474
## p = 8 1735.684 1733.393 1735.155 1735.904 1737.809 1739.084 1739.084 1735.458
## p = 9 1725.032 1724.395 1726.363 1725.579 1727.359 1725.675 1724.357 1724.357
## p = 10 1714.877 1714.772 1716.595 1714.802 1716.130 1714.314 1714.227 1715.591
## p = 11 1705.908 1705.303 1707.240 1706.398 1708.151 1707.411 1709.020 1707.204
## p = 12 1695.222 1694.568 1696.527 1696.140 1698.096 1698.420 1700.345 1697.037
## p = 13 1686.319 1685.846 1687.707 1686.970 1688.872 1689.052 1690.922 1688.028
## p = 14 1676.175 1675.867 1677.764 1677.188 1679.166 1679.732 1681.709 1677.790
## p = 15 1666.976 1666.773 1668.643 1668.053 1670.023 1670.532 1672.500 1669.070
## q = 9 q = 10 q = 11 q = 12 q = 13 q = 14 q = 15
## p = 1 1711.679 1700.574 1690.907 1680.114 1668.821 1658.394 1649.226
## p = 2 1713.316 1702.371 1692.797 1681.976 1670.603 1660.153 1650.971
## p = 3 1715.310 1704.335 1694.737 1683.972 1672.600 1662.120 1652.940
## p = 4 1717.193 1706.193 1696.545 1685.874 1674.370 1663.844 1654.666
## p = 5 1718.957 1707.788 1698.117 1687.629 1676.046 1665.653 1656.497
## p = 6 1720.889 1709.675 1700.038 1689.503 1677.978 1667.560 1658.418
## p = 7 1722.867 1711.675 1702.037 1691.492 1679.959 1669.521 1660.377
## p = 8 1724.863 1713.623 1703.940 1693.375 1681.798 1671.352 1662.231
## p = 9 1725.363 1713.997 1704.180 1693.928 1682.006 1671.757 1662.666
## p = 10 1715.591 1715.975 1706.170 1695.882 1684.003 1673.720 1664.643
## p = 11 1706.660 1706.660 1707.947 1697.703 1685.818 1675.613 1666.538
## p = 12 1696.051 1698.032 1698.032 1698.978 1687.231 1677.030 1667.969
## p = 13 1688.566 1689.707 1689.898 1689.898 1689.119 1678.951 1669.885
## p = 14 1677.720 1679.300 1681.153 1679.715 1679.715 1680.755 1671.686
## p = 15 1669.611 1670.805 1671.991 1673.000 1671.674 1671.674 1673.670
##
## $min.Stat
## [1] 1649.226#PEMODELAN DLM dan ARDL dengan library dynlm
#library(dynlm)
#sama dengan model dlm p=1
cons_lm1 <- dynlm(Close ~ N02BA+L(N02BA),data = train.ts)
#sama dengan model ardl p=0 q=1
cons_lm2 <- dynlm(Close ~ N02BA+L(Close),data = train.ts)
#sama dengan ardl p=1 q=1
cons_lm3 <- dynlm(Close ~ N02BA+L(N02BA)+L(Close),data = train.ts)
#sama dengan dlm p=2
cons_lm4 <- dynlm(Close ~ N02BA+L(N02BA)+L(N02BA,2),data = train.ts)#Ringkasan Model
summary(cons_lm1)##
## Time series regression with "ts" data:
## Start = 2, End = 120
##
## Call:
## dynlm(formula = Close ~ N02BA + L(N02BA), data = train.ts)
##
## Residuals:
## Min 1Q Median 3Q Max
## -508.7 -168.5 26.4 185.2 408.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1319.760 120.832 10.922 <2e-16 ***
## N02BA -4.107 2.907 -1.413 0.160
## L(N02BA) -3.745 2.902 -1.291 0.199
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 214.6 on 116 degrees of freedom
## Multiple R-squared: 0.03554, Adjusted R-squared: 0.01891
## F-statistic: 2.137 on 2 and 116 DF, p-value: 0.1226summary(cons_lm2)##
## Time series regression with "ts" data:
## Start = 2, End = 120
##
## Call:
## dynlm(formula = Close ~ N02BA + L(Close), data = train.ts)
##
## Residuals:
## Min 1Q Median 3Q Max
## -153.776 -39.366 1.687 30.059 141.749
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 41.39622 36.46390 1.135 0.259
## N02BA 0.17848 0.73134 0.244 0.808
## L(Close) 0.95998 0.02294 41.844 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 53.88 on 116 degrees of freedom
## Multiple R-squared: 0.9392, Adjusted R-squared: 0.9382
## F-statistic: 896.1 on 2 and 116 DF, p-value: < 2.2e-16summary(cons_lm3)##
## Time series regression with "ts" data:
## Start = 2, End = 120
##
## Call:
## dynlm(formula = Close ~ N02BA + L(N02BA) + L(Close), data = train.ts)
##
## Residuals:
## Min 1Q Median 3Q Max
## -153.155 -38.507 -0.333 30.191 135.223
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20.89775 43.61669 0.479 0.633
## N02BA 0.10169 0.73761 0.138 0.891
## L(N02BA) 0.63284 0.73693 0.859 0.392
## L(Close) 0.96283 0.02321 41.488 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 53.95 on 115 degrees of freedom
## Multiple R-squared: 0.9396, Adjusted R-squared: 0.938
## F-statistic: 596.3 on 3 and 115 DF, p-value: < 2.2e-16summary(cons_lm4)##
## Time series regression with "ts" data:
## Start = 3, End = 120
##
## Call:
## dynlm(formula = Close ~ N02BA + L(N02BA) + L(N02BA, 2), data = train.ts)
##
## Residuals:
## Min 1Q Median 3Q Max
## -489.6 -161.4 11.3 179.6 394.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1383.621 143.558 9.638 <2e-16 ***
## N02BA -3.275 2.941 -1.114 0.268
## L(N02BA) -2.997 2.918 -1.027 0.307
## L(N02BA, 2) -3.532 2.885 -1.224 0.223
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 213.3 on 114 degrees of freedom
## Multiple R-squared: 0.03937, Adjusted R-squared: 0.01409
## F-statistic: 1.557 on 3 and 114 DF, p-value: 0.2036#SSE
deviance(cons_lm1)## [1] 5343852deviance(cons_lm2)## [1] 336811.3deviance(cons_lm3)## [1] 334665.2deviance(cons_lm4)## [1] 5187539Uji Diagnostik Model
Uji Non Autokorelasi
#uji model
if(require("lmtest")) encomptest(cons_lm1, cons_lm2)## Encompassing test
##
## Model 1: Close ~ N02BA + L(N02BA)
## Model 2: Close ~ N02BA + L(Close)
## Model E: Close ~ N02BA + L(N02BA) + L(Close)
## Res.Df Df F Pr(>F)
## M1 vs. ME 115 -1 1721.2918 <2e-16 ***
## M2 vs. ME 115 -1 0.7375 0.3923
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Diagnostik
#durbin watson
dwtest(cons_lm1)##
## Durbin-Watson test
##
## data: cons_lm1
## DW = 0.096463, p-value < 2.2e-16
## alternative hypothesis: true autocorrelation is greater than 0dwtest(cons_lm2)##
## Durbin-Watson test
##
## data: cons_lm2
## DW = 1.6168, p-value = 0.01346
## alternative hypothesis: true autocorrelation is greater than 0dwtest(cons_lm3)##
## Durbin-Watson test
##
## data: cons_lm3
## DW = 1.6193, p-value = 0.01421
## alternative hypothesis: true autocorrelation is greater than 0dwtest(cons_lm4)##
## Durbin-Watson test
##
## data: cons_lm4
## DW = 0.089977, p-value < 2.2e-16
## alternative hypothesis: true autocorrelation is greater than 0Uji Heterogenitas
#heterogenitas
bptest(cons_lm1)##
## studentized Breusch-Pagan test
##
## data: cons_lm1
## BP = 2.2549, df = 2, p-value = 0.3239bptest(cons_lm2)##
## studentized Breusch-Pagan test
##
## data: cons_lm2
## BP = 1.287, df = 2, p-value = 0.5254bptest(cons_lm3)##
## studentized Breusch-Pagan test
##
## data: cons_lm3
## BP = 2.4258, df = 3, p-value = 0.4889bptest(cons_lm4)##
## studentized Breusch-Pagan test
##
## data: cons_lm4
## BP = 2.8301, df = 3, p-value = 0.4186Uji Normalitas
#shapiro wilk
shapiro.test(residuals(cons_lm1))##
## Shapiro-Wilk normality test
##
## data: residuals(cons_lm1)
## W = 0.97511, p-value = 0.0262shapiro.test(residuals(cons_lm2))##
## Shapiro-Wilk normality test
##
## data: residuals(cons_lm2)
## W = 0.99197, p-value = 0.7238shapiro.test(residuals(cons_lm3))##
## Shapiro-Wilk normality test
##
## data: residuals(cons_lm3)
## W = 0.99196, p-value = 0.7222shapiro.test(residuals(cons_lm4))##
## Shapiro-Wilk normality test
##
## data: residuals(cons_lm4)
## W = 0.97309, p-value = 0.01796Perbandingan Keakuratan Ramalan
#PERBANDINGAN
akurasi <- matrix(c(mape.koyck, mape.dlm, mape.dlm2, mape.ardl))
row.names(akurasi)<- c("Koyck","DLM 1","DLM 2","Autoregressive")
colnames(akurasi) <- c("MAPE")
akurasi## MAPE
## Koyck 0.2827228
## DLM 1 0.3242882
## DLM 2 0.3182544
## Autoregressive 0.2658877#PLOT
par(mfrow=c(1,1))
plot(test$N02BA, test$Close, type="b", col="black", ylim=c(500,3000))
points(test$N02BA, fore.koyck$forecasts,col="red")
lines(test$N02BA, fore.koyck$forecasts,col="red")
points(test$N02BA, fore.dlm$forecasts,col="blue")
lines(test$N02BA, fore.dlm$forecasts,col="blue")
points(test$N02BA, fore.dlm2$forecasts,col="orange")
lines(test$N02BA, fore.dlm2$forecasts,col="orange")
points(test$N02BA, fore.ardl$forecasts,col="green")
lines(test$N02BA, fore.ardl$forecasts,col="green")
legend("topleft",c("aktual", "koyck","DLM 1","DLM 2", "autoregressive"), lty=1, col=c("black","red","blue","orange","green"), cex=0.8)
Materi Pengayaan / Tambahan
#MATERI PENGAYAAN/TAMBAHAN
#Mencoba dengan Data Lain (Berganda)
#Data M1 Germany
data(M1Germany)
data1 = M1Germany[1:144,]
#Mencari orde lag optimum model DLM
# Run the search over finite DLMs according to AIC values
finiteDLMauto(formula = logprice ~ interest+logm1,
data = data.frame(data1), q.min = 1, q.max = 5,
model.type = "dlm", error.type = "AIC", trace = FALSE)## q - k MASE AIC BIC GMRAE MBRAE R.Adj.Sq Ljung-Box
## 5 5 1.77163 -463.1393 -422.0566 1.43662 -1.60494 0.98836 0#model dlm berganda
model.dlmberganda = dlm(formula = logprice ~ interest + logm1,
data = data.frame(data1) , q = 5)
summary(model.dlmberganda)##
## Call:
## lm(formula = as.formula(model.formula), data = design)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.095761 -0.028610 -0.000012 0.029496 0.102597
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.81759 0.11384 -68.669 < 2e-16 ***
## interest.t -1.75616 0.80358 -2.185 0.030707 *
## interest.1 1.38935 1.22707 1.132 0.259679
## interest.2 0.40776 1.23726 0.330 0.742273
## interest.3 1.23130 1.20752 1.020 0.309830
## interest.4 -0.08718 1.20869 -0.072 0.942616
## interest.5 3.06850 0.89380 3.433 0.000808 ***
## logm1.t 0.43219 0.20876 2.070 0.040474 *
## logm1.1 0.42190 0.19807 2.130 0.035109 *
## logm1.2 0.20943 0.12883 1.626 0.106532
## logm1.3 0.22053 0.13011 1.695 0.092567 .
## logm1.4 0.05513 0.21457 0.257 0.797633
## logm1.5 0.03042 0.19192 0.159 0.874296
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04343 on 126 degrees of freedom
## Multiple R-squared: 0.9894, Adjusted R-squared: 0.9884
## F-statistic: 977.9 on 12 and 126 DF, p-value: < 2.2e-16
##
## AIC and BIC values for the model:
## AIC BIC
## 1 -463.1393 -422.0566model.dlmberganda2 = dlm(formula = logprice ~ interest + logm1,
data = data.frame(data1) , q = 1)
summary(model.dlmberganda2)##
## Call:
## lm(formula = as.formula(model.formula), data = design)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.134002 -0.044697 0.006407 0.036962 0.113063
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.77917 0.13299 -58.492 < 2e-16 ***
## interest.t -3.22103 0.94184 -3.420 0.000824 ***
## interest.1 6.52775 0.94501 6.908 1.66e-10 ***
## logm1.t 0.73918 0.08419 8.780 5.61e-15 ***
## logm1.1 0.63330 0.08429 7.513 6.55e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05443 on 138 degrees of freedom
## Multiple R-squared: 0.9832, Adjusted R-squared: 0.9828
## F-statistic: 2025 on 4 and 138 DF, p-value: < 2.2e-16
##
## AIC and BIC values for the model:
## AIC BIC
## 1 -419.7575 -401.9805model.dlmberganda2 = dlm(formula = logprice ~ interest + logm1,
data = data.frame(data1) , q = 1)
summary(model.dlmberganda2)##
## Call:
## lm(formula = as.formula(model.formula), data = design)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.134002 -0.044697 0.006407 0.036962 0.113063
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.77917 0.13299 -58.492 < 2e-16 ***
## interest.t -3.22103 0.94184 -3.420 0.000824 ***
## interest.1 6.52775 0.94501 6.908 1.66e-10 ***
## logm1.t 0.73918 0.08419 8.780 5.61e-15 ***
## logm1.1 0.63330 0.08429 7.513 6.55e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05443 on 138 degrees of freedom
## Multiple R-squared: 0.9832, Adjusted R-squared: 0.9828
## F-statistic: 2025 on 4 and 138 DF, p-value: < 2.2e-16
##
## AIC and BIC values for the model:
## AIC BIC
## 1 -419.7575 -401.9805#Mencari orde lag optimum model ARDL
ardlBoundOrders(data = data1 , formula = logprice ~ interest + logm1 )## $p
## interest logm1
## 65 0 4
##
## $q
## [1] 4
##
## $Stat.table
## q = 1 q = 2 q = 3 q = 4 q = 5 q = 6 q = 7
## p = 1 -760.1786 -757.9195 -846.8342 -975.2079 -965.7536 -958.9072 -956.7315
## p = 2 -760.0433 -759.3090 -843.6247 -971.2514 -961.7929 -955.2809 -953.4890
## p = 3 -753.7746 -753.7746 -841.2485 -970.4543 -961.4343 -953.7173 -950.0412
## p = 4 -829.8076 -832.6436 -832.6436 -971.0837 -962.1804 -955.0429 -953.4667
## p = 5 -749.4144 -753.2292 -962.9290 -962.9290 -961.7063 -954.3406 -951.7660
## p = 6 -742.2103 -742.9945 -891.6195 -952.3771 -952.3771 -952.2461 -950.1105
## p = 7 -728.9374 -733.0286 -851.2943 -945.7445 -944.6879 -944.6879 -949.3720
## p = 8 -747.9277 -746.2948 -812.4289 -937.9446 -938.9491 -937.3393 -937.3393
## p = 9 -722.6891 -724.5786 -863.2734 -928.9215 -927.2914 -926.8716 -936.6432
## p = 10 -714.8175 -714.5658 -816.3319 -918.5218 -918.6350 -916.9076 -921.1246
## p = 11 -703.1807 -705.3383 -794.0772 -909.6457 -908.8225 -906.9542 -912.9605
## p = 12 -716.7111 -714.7403 -774.0127 -910.0315 -910.6834 -908.7146 -909.6612
## p = 13 -697.7175 -698.1931 -793.4602 -895.5927 -894.9273 -893.5995 -897.7589
## p = 14 -686.5600 -685.7967 -766.5292 -886.0709 -885.4341 -885.2283 -890.1638
## p = 15 -676.7280 -678.3689 -753.2854 -875.6392 -874.1257 -874.3117 -879.2727
## q = 8 q = 9 q = 10 q = 11 q = 12 q = 13 q = 14
## p = 1 -954.3375 -946.6293 -936.5328 -927.7728 -920.6435 -917.5463 -918.3110
## p = 2 -951.1470 -943.9360 -933.7047 -924.7949 -917.5334 -913.6213 -914.4063
## p = 3 -948.4683 -941.1039 -930.8509 -922.0563 -914.5728 -910.5351 -913.4996
## p = 4 -948.2330 -941.8238 -931.5689 -923.2663 -916.2063 -911.6023 -913.9345
## p = 5 -947.5994 -939.3767 -929.0155 -920.4475 -913.5968 -909.0781 -911.6312
## p = 6 -945.5758 -937.4076 -927.2439 -919.3949 -911.9537 -907.7394 -910.2890
## p = 7 -945.5181 -937.1826 -926.9640 -917.9619 -910.2774 -905.9449 -907.8712
## p = 8 -941.9617 -933.5959 -923.3691 -914.6251 -907.0608 -902.2187 -903.9255
## p = 9 -936.6432 -935.7172 -925.2881 -917.0877 -911.6973 -903.9027 -904.6405
## p = 10 -926.6891 -926.6891 -924.6986 -917.0904 -911.4197 -903.4313 -903.0612
## p = 11 -917.9145 -918.2328 -918.2328 -919.2867 -913.3674 -904.8733 -903.6541
## p = 12 -916.1321 -914.4362 -914.4610 -914.4610 -912.5159 -904.2394 -901.6216
## p = 13 -905.4744 -903.7559 -902.4406 -902.2530 -902.2530 -902.9434 -901.2363
## p = 14 -896.2370 -896.2620 -894.2896 -897.5711 -899.1407 -899.1407 -902.2350
## p = 15 -884.5637 -886.8221 -884.9832 -890.5665 -893.2335 -891.6220 -891.6220
## q = 15
## p = 1 -908.0863
## p = 2 -904.1665
## p = 3 -903.3006
## p = 4 -903.9256
## p = 5 -901.6220
## p = 6 -900.1824
## p = 7 -897.9867
## p = 8 -894.1031
## p = 9 -894.7387
## p = 10 -893.6199
## p = 11 -893.6060
## p = 12 -892.4805
## p = 13 -892.5115
## p = 14 -893.6214
## p = 15 -891.3741
##
## $min.Stat
## [1] -977.2745
##
## $Stat.p
## interest logm1 Stat
## 65 0 4 -977.2745
## 1 0 0 -976.5191
## 2 1 0 -976.2558
## 17 0 1 -975.9606
## 66 1 4 -975.6027
## 18 1 1 -975.2079
## 49 0 3 -974.4859
## 3 2 0 -974.4275
## 33 0 2 -974.0166
## 50 1 3 -973.7500
## 67 2 4 -973.6028
## 34 1 2 -973.2324
## 19 2 1 -973.2188
## 68 3 4 -972.5992
## 4 3 0 -972.4875
## 51 2 3 -971.7743
## 20 3 1 -971.3872
## 35 2 2 -971.2514
## 69 4 4 -971.0837
## 5 4 0 -970.5114
## 52 3 3 -970.4543
## 81 0 5 -969.9284
## 53 4 3 -969.5311
## 21 4 1 -969.4756
## 36 3 2 -969.3907
## 82 1 5 -968.6783
## 37 4 2 -967.4756
## 83 2 5 -966.8835
## 84 3 5 -965.6393
## 85 4 5 -963.9662
## 86 5 5 -962.9290
## 70 5 4 -961.2547
## 54 5 3 -960.9580
## 97 0 6 -960.7402
## 6 5 0 -960.6858
## 22 5 1 -959.8419
## 98 1 6 -959.6604
## 38 5 2 -957.8547
## 99 2 6 -957.7528
## 100 3 6 -956.7875
## 101 4 6 -955.2416
## 71 6 4 -954.8953
## 87 6 5 -954.6855
## 102 5 6 -954.3662
## 103 6 6 -954.0973
## 7 6 0 -954.0615
## 113 0 7 -953.9160
## 55 6 3 -953.2860
## 23 6 1 -953.1080
## 114 1 7 -952.6540
## 39 6 2 -951.1356
## 115 2 7 -950.6562
## 116 3 7 -949.6038
## 88 7 5 -949.2090
## 72 7 4 -948.5194
## 117 4 7 -947.7999
## 104 7 6 -947.7424
## 56 7 3 -947.6915
## 8 7 0 -947.5092
## 120 7 7 -947.3660
## 24 7 1 -947.0094
## 118 5 7 -946.9631
## 119 6 7 -946.8080
## 40 7 2 -945.0123
## 129 0 8 -943.9035
## 130 1 8 -942.6627
## 131 2 8 -940.6818
## 145 0 9 -940.0114
## 132 3 8 -939.6913
## 89 8 5 -939.1878
## 73 8 4 -938.5330
## 146 1 9 -938.2680
## 133 4 8 -937.8368
## 105 8 6 -937.6834
## 57 8 3 -937.6370
## 9 8 0 -937.5705
## 121 8 7 -937.5351
## 136 7 8 -937.3948
## 25 8 1 -937.0088
## 134 5 8 -936.9393
## 135 6 8 -936.8904
## 147 2 9 -936.3875
## 148 3 9 -936.3159
## 137 8 8 -935.5389
## 41 8 2 -935.0088
## 149 4 9 -934.3458
## 150 5 9 -934.1858
## 152 7 9 -934.0733
## 151 6 9 -932.9538
## 153 8 9 -932.3338
## 154 9 9 -930.9065
## 161 0 10 -929.8056
## 90 9 5 -929.2731
## 74 9 4 -928.5254
## 162 1 10 -928.1257
## 10 9 0 -927.9853
## 58 9 3 -927.9744
## 122 9 7 -927.9061
## 106 9 6 -927.6344
## 26 9 1 -927.4482
## 164 3 10 -926.5271
## 163 2 10 -926.2965
## 138 9 8 -926.1307
## 42 9 2 -925.4484
## 165 4 10 -924.5287
## 168 7 10 -924.2716
## 166 5 10 -924.0521
## 167 6 10 -922.7596
## 169 8 10 -922.5928
## 155 10 9 -921.2169
## 170 9 10 -921.1777
## 177 0 11 -920.2608
## 171 10 10 -920.0124
## 91 10 5 -919.0182
## 178 1 11 -918.7342
## 75 10 4 -918.4135
## 11 10 0 -917.8597
## 59 10 3 -917.7711
## 123 10 7 -917.6569
## 107 10 6 -917.3861
## 27 10 1 -917.2925
## 179 2 11 -916.9417
## 180 3 11 -916.8682
## 193 0 12 -916.1477
## 139 10 8 -915.9643
## 92 11 5 -915.3201
## 43 10 2 -915.2941
## 156 11 9 -915.0851
## 181 4 11 -914.8854
## 194 1 12 -914.4423
## 124 11 7 -914.3141
## 184 7 11 -914.1880
## 76 11 4 -914.1395
## 182 5 11 -914.0440
## 108 11 6 -913.4052
## 140 11 8 -913.3026
## 195 2 12 -913.1680
## 172 11 10 -913.0914
## 60 11 3 -912.7714
## 183 6 11 -912.7548
## 196 3 12 -912.5820
## 185 8 11 -912.5636
## 12 11 0 -912.2009
## 28 11 1 -912.0389
## 186 9 11 -911.1737
## 157 12 9 -911.1513
## 188 11 11 -911.1189
## 93 12 5 -910.7693
## 198 5 12 -910.7434
## 197 4 12 -910.6154
## 125 12 7 -910.5873
## 141 12 8 -910.0719
## 44 11 2 -910.0439
## 187 10 11 -909.9928
## 200 7 12 -909.4197
## 173 12 10 -909.2473
## 77 12 4 -909.1913
## 109 12 6 -908.7753
## 199 6 12 -908.7635
## 201 8 12 -908.1609
## 61 12 3 -908.0357
## 29 12 1 -907.8613
## 209 0 13 -907.6473
## 13 12 0 -907.6158
## 205 12 12 -907.5931
## 204 11 12 -907.5525
## 202 9 12 -907.3633
## 189 12 11 -907.3200
## 210 1 13 -906.1005
## 45 12 2 -905.9070
## 203 10 12 -905.7653
## 211 2 13 -904.7293
## 212 3 13 -903.9077
## 214 5 13 -902.0824
## 158 13 9 -901.9574
## 213 4 13 -901.9144
## 94 13 5 -901.6338
## 126 13 7 -901.3766
## 142 13 8 -900.9367
## 216 7 13 -900.5676
## 225 0 14 -900.5066
## 174 13 10 -900.1413
## 215 6 13 -900.1102
## 78 13 4 -900.0282
## 110 13 6 -899.6703
## 226 1 14 -899.0967
## 217 8 13 -899.0866
## 62 13 3 -898.8589
## 30 13 1 -898.7940
## 190 13 11 -898.4409
## 221 12 13 -898.4110
## 220 11 13 -898.3058
## 218 9 13 -898.2568
## 14 13 0 -898.2039
## 206 13 12 -897.9014
## 227 2 14 -897.3889
## 46 13 2 -896.8637
## 219 10 13 -896.6244
## 222 13 13 -896.4458
## 228 3 14 -896.2512
## 230 5 14 -895.1320
## 95 14 5 -894.6021
## 229 4 14 -894.3023
## 159 14 9 -894.2497
## 127 14 7 -893.9663
## 143 14 8 -893.6932
## 231 6 14 -893.4037
## 79 14 4 -893.1343
## 232 7 14 -893.1064
## 111 14 6 -892.6253
## 175 14 10 -892.5085
## 63 14 3 -891.9131
## 191 14 11 -891.1895
## 233 8 14 -891.1877
## 234 9 14 -891.1729
## 31 14 1 -890.7573
## 236 11 14 -890.5576
## 241 0 15 -890.5500
## 15 14 0 -890.3449
## 237 12 14 -890.1854
## 235 10 14 -889.8957
## 207 14 12 -889.7107
## 242 1 15 -889.0419
## 47 14 2 -888.9410
## 238 13 14 -888.1867
## 223 14 13 -887.7488
## 239 14 14 -887.6659
## 243 2 15 -887.3088
## 244 3 15 -886.0691
## 246 5 15 -884.7479
## 96 15 5 -884.2869
## 245 4 15 -884.1417
## 160 15 9 -883.9364
## 128 15 7 -883.6409
## 144 15 8 -883.4503
## 247 6 15 -883.0158
## 80 15 4 -882.8148
## 248 7 15 -882.7881
## 112 15 6 -882.3106
## 176 15 10 -882.2093
## 64 15 3 -881.6497
## 253 12 15 -881.4274
## 252 11 15 -881.3077
## 250 9 15 -881.1831
## 192 15 11 -880.9028
## 249 8 15 -880.8964
## 32 15 1 -880.5983
## 251 10 15 -880.2736
## 16 15 0 -880.2468
## 254 13 15 -879.4467
## 208 15 12 -879.4364
## 255 14 15 -879.2846
## 48 15 2 -878.8432
## 224 15 13 -877.4985
## 240 15 14 -877.4570model.ardlDlmberganda = ardlDlm(formula = logprice ~ interest + logm1,
data = data.frame(data1) , p = 4 , q = 4)
summary(model.ardlDlmberganda)##
## Time series regression with "ts" data:
## Start = 5, End = 144
##
## Call:
## dynlm(formula = as.formula(model.text), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0290527 -0.0075965 0.0005726 0.0072745 0.0304486
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0145022 0.1822785 0.080 0.93671
## interest.t 0.0067985 0.2135315 0.032 0.97465
## interest.1 0.6093502 0.3240545 1.880 0.06238 .
## interest.2 0.0798544 0.3221168 0.248 0.80461
## interest.3 -0.3638172 0.3238873 -1.123 0.26347
## interest.4 0.2084240 0.2447331 0.852 0.39604
## logm1.t 0.0828689 0.0457486 1.811 0.07248 .
## logm1.1 -0.0092841 0.0399079 -0.233 0.81642
## logm1.2 -0.1166129 0.0390732 -2.984 0.00342 **
## logm1.3 0.0007016 0.0389297 0.018 0.98565
## logm1.4 0.0447857 0.0425474 1.053 0.29455
## logprice.1 0.3274245 0.0651574 5.025 1.7e-06 ***
## logprice.2 0.1323801 0.0684485 1.934 0.05537 .
## logprice.3 -0.1448245 0.0674268 -2.148 0.03365 *
## logprice.4 0.6730871 0.0636443 10.576 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01132 on 125 degrees of freedom
## Multiple R-squared: 0.9993, Adjusted R-squared: 0.9992
## F-statistic: 1.273e+04 on 14 and 125 DF, p-value: < 2.2e-16#model p interest 0 p logm1 4
rem.p = list(interest = c(1,2,3,4))
remove = list(p = rem.p)
model.ardlDlmberganda2 = ardlDlm(formula = logprice ~ interest + logm1,
data = data.frame(data1) , p = 4 , q = 4 ,
remove = remove)
summary(model.ardlDlmberganda2)##
## Time series regression with "ts" data:
## Start = 5, End = 144
##
## Call:
## dynlm(formula = as.formula(model.text), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0290369 -0.0083445 0.0009024 0.0079199 0.0303652
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.174838 0.133708 1.308 0.19333
## interest.t 0.448826 0.098736 4.546 1.24e-05 ***
## logm1.t 0.056659 0.043836 1.293 0.19849
## logm1.1 -0.017025 0.039159 -0.435 0.66446
## logm1.2 -0.118413 0.037399 -3.166 0.00193 **
## logm1.3 -0.006454 0.038112 -0.169 0.86580
## logm1.4 0.060220 0.040337 1.493 0.13789
## logprice.1 0.319059 0.062107 5.137 1.00e-06 ***
## logprice.2 0.111794 0.066101 1.691 0.09320 .
## logprice.3 -0.122129 0.065114 -1.876 0.06297 .
## logprice.4 0.699061 0.062611 11.165 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01149 on 129 degrees of freedom
## Multiple R-squared: 0.9993, Adjusted R-squared: 0.9992
## F-statistic: 1.73e+04 on 10 and 129 DF, p-value: < 2.2e-16