# ============================================================
# ANALISIS VAR: INFLASI, BI RATE, KURS
# ============================================================
# 1. PANGGIL LIBRARY
library(readxl)
## Warning: package 'readxl' was built under R version 4.5.3
library(vars)
## Warning: package 'vars' was built under R version 4.5.3
## Loading required package: MASS
## Warning: package 'MASS' was built under R version 4.5.3
## Loading required package: strucchange
## Warning: package 'strucchange' was built under R version 4.5.3
## Loading required package: zoo
## Warning: package 'zoo' was built under R version 4.5.2
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
## Loading required package: sandwich
## Warning: package 'sandwich' was built under R version 4.5.3
## Loading required package: urca
## Warning: package 'urca' was built under R version 4.5.3
## Loading required package: lmtest
## Warning: package 'lmtest' was built under R version 4.5.3
library(urca)
library(forecast)
## Warning: package 'forecast' was built under R version 4.5.3
# 2. BACA DATA
file <- "C:/ARW/UAS_ARW/Data_Inflasi_Suku Bunga_Kurs Rupiah_Soal Nomor 2.xlsx"
inflasi <- read_excel(file, sheet = "Inflasi")
sukubunga <- read_excel(file, sheet = "Suku Bunga")
kurs <- read_excel(file, sheet = "Nilai Tukar Rupiah")
head(data)
##
## 1 function (..., list = character(), package = NULL, lib.loc = NULL,
## 2 verbose = getOption("verbose"), envir = .GlobalEnv, overwrite = TRUE)
## 3 {
## 4 fileExt <- function(x) {
## 5 db <- grepl("\\\\.[^.]+\\\\.(gz|bz2|xz)$", x)
## 6 ans <- sub(".*\\\\.", "", x)
# 3. BERSIHKAN DATA
# Ubah koma jadi titik, lalu jadikan angka
inflasi$Inflasi <- as.numeric(gsub(",", ".", inflasi$Inflasi))
sukubunga$`BI- Rate` <- as.numeric(gsub(",", ".", sukubunga$`BI- Rate`))
kurs$USD <- as.numeric(gsub(",", ".", kurs$USD))
# 4. GABUNGKAN JADI SATU DATA
data <- data.frame(
inflasi = inflasi$Inflasi,
birate = sukubunga$`BI- Rate`,
kurs = kurs$USD
)
head(data)
## inflasi birate kurs
## 1 0.89 6.50 9057
## 2 0.13 6.75 8823
## 3 -0.32 6.75 8709
## 4 -0.31 6.75 8574
## 5 0.12 6.75 8537
## 6 0.55 6.75 8597
summary(data)
## inflasi birate kurs
## Min. :-0.7600 Min. :3.500 Min. : 8508
## 1st Qu.: 0.0700 1st Qu.:4.750 1st Qu.:12386
## Median : 0.2100 Median :5.750 Median :14078
## Mean : 0.3068 Mean :5.592 Mean :13554
## 3rd Qu.: 0.5125 3rd Qu.:6.312 3rd Qu.:15035
## Max. : 3.2900 Max. :7.750 Max. :18078
# 5. JADIKAN TIME SERIES (BULANAN, MULAI JAN 2011)
ts_data <- ts(data, start = c(2011, 1), frequency = 12)
ts_data
## inflasi birate kurs
## Jan 2011 0.89 6.50 9057
## Feb 2011 0.13 6.75 8823
## Mar 2011 -0.32 6.75 8709
## Apr 2011 -0.31 6.75 8574
## May 2011 0.12 6.75 8537
## Jun 2011 0.55 6.75 8597
## Jul 2011 0.67 6.75 8508
## Aug 2011 0.93 6.75 8578
## Sep 2011 0.27 6.75 8823
## Oct 2011 -0.12 6.50 8835
## Nov 2011 0.34 6.00 9170
## Dec 2011 0.57 6.00 9068
## Jan 2012 0.76 6.00 9000
## Feb 2012 0.05 5.75 9085
## Mar 2012 0.07 5.75 9180
## Apr 2012 0.21 5.75 9190
## May 2012 0.07 5.75 9565
## Jun 2012 0.62 5.75 9480
## Jul 2012 0.70 5.75 9485
## Aug 2012 0.95 5.75 9560
## Sep 2012 0.01 5.75 9588
## Oct 2012 0.16 5.75 9615
## Nov 2012 0.07 5.75 9605
## Dec 2012 0.54 5.75 9670
## Jan 2013 1.03 5.75 9698
## Feb 2013 0.75 5.75 9667
## Mar 2013 0.63 5.75 9719
## Apr 2013 -0.10 5.75 9722
## May 2013 -0.03 5.75 9802
## Jun 2013 1.03 6.00 9929
## Jul 2013 3.29 6.50 10278
## Aug 2013 1.12 7.00 10924
## Sep 2013 -0.35 7.25 11613
## Oct 2013 0.09 7.25 11234
## Nov 2013 0.12 7.50 11977
## Dec 2013 0.55 7.50 12189
## Jan 2014 1.07 7.50 12226
## Feb 2014 0.26 7.50 11634
## Mar 2014 0.08 7.50 11404
## Apr 2014 -0.02 7.50 11532
## May 2014 0.16 7.50 11611
## Jun 2014 0.43 7.50 11969
## Jul 2014 0.93 7.50 11591
## Aug 2014 0.47 7.50 11717
## Sep 2014 0.27 7.50 12212
## Oct 2014 0.47 7.50 12082
## Nov 2014 1.50 7.75 12196
## Dec 2014 2.46 7.75 12440
## Jan 2015 -0.24 7.75 12625
## Feb 2015 -0.36 7.50 12863
## Mar 2015 0.17 7.50 13084
## Apr 2015 0.36 7.50 12937
## May 2015 0.50 7.50 13211
## Jun 2015 0.54 7.50 13332
## Jul 2015 0.93 7.50 13481
## Aug 2015 0.39 7.50 14027
## Sep 2015 -0.05 7.50 14657
## Oct 2015 -0.08 7.50 13639
## Nov 2015 0.21 7.50 13840
## Dec 2015 0.96 7.50 13795
## Jan 2016 0.51 7.25 13846
## Feb 2016 -0.09 7.00 13395
## Mar 2016 0.19 6.75 13276
## Apr 2016 -0.45 6.75 13204
## May 2016 0.24 6.75 13615
## Jun 2016 0.66 6.50 13180
## Jul 2016 0.69 6.50 13094
## Aug 2016 -0.02 5.25 13300
## Sep 2016 0.22 5.00 12998
## Oct 2016 0.14 4.75 13051
## Nov 2016 0.47 4.75 13563
## Dec 2016 0.42 4.75 13436
## Jan 2017 0.97 4.75 13343
## Feb 2017 0.23 4.75 13347
## Mar 2017 -0.02 4.75 13321
## Apr 2017 0.09 4.75 13327
## May 2017 0.39 4.75 13321
## Jun 2017 0.69 4.75 13319
## Jul 2017 0.22 4.75 13323
## Aug 2017 -0.07 4.50 13351
## Sep 2017 0.13 4.25 13492
## Oct 2017 0.01 4.25 13572
## Nov 2017 0.20 4.25 13514
## Dec 2017 0.71 4.25 13548
## Jan 2018 0.62 4.25 13413
## Feb 2018 0.17 4.25 13707
## Mar 2018 0.20 4.25 13756
## Apr 2018 0.10 4.25 13877
## May 2018 0.21 4.75 13951
## Jun 2018 0.59 5.25 14404
## Jul 2018 0.28 5.25 14413
## Aug 2018 -0.05 5.50 14711
## Sep 2018 -0.18 5.75 14929
## Oct 2018 0.28 5.75 15227
## Nov 2018 0.27 6.00 14339
## Dec 2018 0.62 6.00 14481
## Jan 2019 0.32 6.00 14072
## Feb 2019 -0.08 6.00 14062
## Mar 2019 0.11 6.00 14244
## Apr 2019 0.44 6.00 14215
## May 2019 0.68 6.00 14385
## Jun 2019 0.55 6.00 14141
## Jul 2019 0.31 5.75 14026
## Aug 2019 0.12 5.50 14237
## Sep 2019 -0.27 5.25 14174
## Oct 2019 0.02 5.00 14008
## Nov 2019 0.14 5.00 14102
## Dec 2019 0.34 5.00 13901
## Jan 2020 0.39 5.00 13662
## Feb 2020 0.28 4.75 14234
## Mar 2020 0.10 4.50 16367
## Apr 2020 0.08 4.50 15157
## May 2020 0.07 4.50 14733
## Jun 2020 0.18 4.25 14302
## Jul 2020 -0.10 4.00 14653
## Aug 2020 -0.05 4.00 14554
## Sep 2020 -0.05 4.00 14918
## Oct 2020 0.07 4.00 14690
## Nov 2020 0.28 3.75 14128
## Dec 2020 0.45 3.75 14105
## Jan 2021 0.26 3.75 14084
## Feb 2021 0.10 3.50 14229
## Mar 2021 0.08 3.50 14572
## Apr 2021 0.13 3.50 14468
## May 2021 0.32 3.50 14310
## Jun 2021 -0.16 3.50 14496
## Jul 2021 0.08 3.50 14491
## Aug 2021 0.03 3.50 14374
## Sep 2021 -0.04 3.50 14307
## Oct 2021 0.12 3.50 14199
## Nov 2021 0.37 3.50 14340
## Dec 2021 0.57 3.50 14269
## Jan 2022 0.56 3.50 14381
## Feb 2022 -0.02 3.50 14371
## Mar 2022 0.66 3.50 14349
## Apr 2022 0.95 3.50 14418
## May 2022 0.40 3.50 14544
## Jun 2022 0.61 3.50 14848
## Jul 2022 0.64 3.50 14958
## Aug 2022 -0.21 3.75 14875
## Sep 2022 1.17 4.25 15247
## Oct 2022 -0.11 4.75 15542
## Nov 2022 0.09 5.25 15737
## Dec 2022 0.66 5.50 15731
## Jan 2023 0.34 5.75 14979
## Feb 2023 0.16 5.75 15274
## Mar 2023 0.18 5.75 15062
## Apr 2023 0.33 5.75 14751
## May 2023 0.09 5.75 14969
## Jun 2023 0.14 5.75 15026
## Jul 2023 0.21 5.75 15083
## Aug 2023 -0.02 5.75 15239
## Sep 2023 0.19 5.75 15526
## Oct 2023 0.17 6.00 15916
## Nov 2023 0.38 6.00 15384
## Dec 2023 0.41 6.00 15416
## Jan 2024 0.04 6.00 15796
## Feb 2024 0.37 6.00 15673
## Mar 2024 0.52 6.00 15853
## Apr 2024 0.25 6.25 16249
## May 2024 -0.03 6.25 16253
## Jun 2024 -0.08 6.25 16421
## Jul 2024 -0.18 6.25 16320
## Aug 2024 -0.03 6.25 15409
## Sep 2024 -0.12 6.00 15138
## Oct 2024 0.08 6.00 15732
## Nov 2024 0.30 6.00 15864
## Dec 2024 0.44 6.00 16162
## Jan 2025 -0.76 5.75 16259
## Feb 2025 -0.48 5.75 16431
## Mar 2025 1.65 5.75 16588
## Apr 2025 1.17 5.75 16787
## May 2025 -0.37 5.50 16255
## Jun 2025 0.19 5.50 16233
## Jul 2025 0.30 5.25 16387
## Aug 2025 -0.08 5.00 16356
## Sep 2025 0.21 4.75 16680
## Oct 2025 0.28 4.75 16640
## Nov 2025 0.17 4.75 16644
## Dec 2025 0.64 4.75 16782
## Jan 2026 -0.15 4.75 16786
## Feb 2026 0.68 4.75 16758
## Mar 2026 0.41 4.75 16993
## Apr 2026 0.13 4.75 17324
## May 2026 0.28 5.25 17789
## Jun 2026 0.44 5.75 17856
## Jul 2026 -0.14 5.75 18078
## Aug 2026 0.21 5.75 17703
# 6. PLOT DATA
par(mfrow = c(1, 1))
plot(ts_data[, "inflasi"], main = "Inflasi", ylab = "Inflasi")
plot(ts_data[, "birate"], main = "BI Rate", ylab = "BI Rate")
plot(ts_data[, "kurs"], main = "Kurs", ylab = "Kurs")
par(mfrow = c(1, 1))
# 7. UJI STASIONER (ADF) PADA LEVEL
summary(ur.df(ts_data[, "inflasi"], type = "trend", lags = 12, selectlags = "AIC"))
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.94814 -0.20190 -0.04501 0.14203 2.43355
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4215019 0.1142385 3.690 0.000304 ***
## z.lag.1 -0.8733167 0.1859033 -4.698 5.47e-06 ***
## tt -0.0015592 0.0006977 -2.235 0.026769 *
## z.diff.lag1 0.1670645 0.1684801 0.992 0.322828
## z.diff.lag2 -0.1842894 0.1460359 -1.262 0.208728
## z.diff.lag3 -0.1634268 0.1224852 -1.334 0.183936
## z.diff.lag4 -0.2262029 0.0920072 -2.459 0.014971 *
## z.diff.lag5 -0.1450356 0.0764607 -1.897 0.059572 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4069 on 167 degrees of freedom
## Multiple R-squared: 0.4863, Adjusted R-squared: 0.4648
## F-statistic: 22.58 on 7 and 167 DF, p-value: < 2.2e-16
##
##
## Value of test-statistic is: -4.6977 7.3659 11.0364
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -3.99 -3.43 -3.13
## phi2 6.22 4.75 4.07
## phi3 8.43 6.49 5.47
summary(ur.df(ts_data[, "birate"], type = "trend", lags = 12, selectlags = "AIC"))
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.18920 -0.04167 -0.00169 0.03939 0.49891
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1522068 0.0810660 1.878 0.0622 .
## z.lag.1 -0.0240329 0.0115026 -2.089 0.0382 *
## tt -0.0002009 0.0002775 -0.724 0.4701
## z.diff.lag1 0.3977601 0.0748963 5.311 3.38e-07 ***
## z.diff.lag2 0.1733207 0.0757561 2.288 0.0234 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1649 on 170 degrees of freedom
## Multiple R-squared: 0.2529, Adjusted R-squared: 0.2353
## F-statistic: 14.39 on 4 and 170 DF, p-value: 3.873e-10
##
##
## Value of test-statistic is: -2.0893 1.478 2.2157
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -3.99 -3.43 -3.13
## phi2 6.22 4.75 4.07
## phi3 8.43 6.49 5.47
summary(ur.df(ts_data[, "kurs"], type = "trend", lags = 12, selectlags = "AIC"))
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -959.09 -158.99 -11.13 176.46 2105.95
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1122.25450 339.03926 3.310 0.00114 **
## z.lag.1 -0.10358 0.03311 -3.128 0.00207 **
## tt 3.64960 1.33076 2.743 0.00675 **
## z.diff.lag -0.03886 0.07597 -0.512 0.60962
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 329.4 on 171 degrees of freedom
## Multiple R-squared: 0.06219, Adjusted R-squared: 0.04574
## F-statistic: 3.78 on 3 and 171 DF, p-value: 0.01166
##
##
## Value of test-statistic is: -3.1282 4.8692 4.9898
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -3.99 -3.43 -3.13
## phi2 6.22 4.75 4.07
## phi3 8.43 6.49 5.47
# UJI KOINTEGRASI (JOHANSEN TEST)
library(urca)
# Uji kointegrasi Johansen
# H0: Tidak ada kointegrasi
# H1: Ada kointegrasi
# Gunakan data pada LEVEL (bukan difference)
jo_test <- ca.jo(
ts_data, # data level (inflasi, birate, kurs)
type = "trace", # uji trace
ecdet = "const", # ada konstanta
K = 2 # lag (sesuaikan)
)
summary(jo_test)
##
## ######################
## # Johansen-Procedure #
## ######################
##
## Test type: trace statistic , without linear trend and constant in cointegration
##
## Eigenvalues (lambda):
## [1] 4.664030e-01 3.821592e-02 1.932174e-02 2.919387e-17
##
## Values of teststatistic and critical values of test:
##
## test 10pct 5pct 1pct
## r <= 2 | 3.63 7.52 9.24 12.97
## r <= 1 | 10.88 17.85 19.96 24.60
## r = 0 | 127.71 32.00 34.91 41.07
##
## Eigenvectors, normalised to first column:
## (These are the cointegration relations)
##
## inflasi.l2 birate.l2 kurs.l2 constant
## inflasi.l2 1.000000e+00 1.000000000 1.000000000 1.00000000
## birate.l2 -1.867410e-02 -2.355598529 -12.043223858 -0.78006064
## kurs.l2 3.201331e-05 0.003074355 -0.002692784 -0.01875741
## constant -6.266710e-01 -42.892529339 105.483146982 233.05964322
##
## Weights W:
## (This is the loading matrix)
##
## inflasi.l2 birate.l2 kurs.l2 constant
## inflasi.d -1.06237194 -0.0005719345 0.0007814209 -2.255919e-18
## birate.d -0.03593061 0.0006967080 0.0015313831 -3.122648e-18
## kurs.d 119.38456492 -3.4772434275 1.4440560471 -9.245847e-15
# Lihat bagian "test statistics" dan "critical values"
# Bandingkan
# - Jika test statistic > critical value (5%) → TOLAK H0 → ADA kointegrasi
# - Jika test statistic < critical value (5%) → GAGAL TOLAK H0 → TIDAK ADA kointegrasi
# 8. DIFFERENCE KALAU TIDAK STASIONER
d_birate <- diff(ts_data[, "birate"])
d_kurs <- diff(ts_data[, "kurs"])
summary(ur.df(d_birate, type = "trend", lags = 12, selectlags = "AIC"))
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.21287 -0.00200 -0.00053 0.03634 0.49940
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.844e-04 2.794e-02 -0.024 0.9805
## z.lag.1 -4.553e-01 7.802e-02 -5.836 2.64e-08 ***
## tt 1.474e-05 2.507e-04 0.059 0.9532
## z.diff.lag -1.497e-01 7.535e-02 -1.987 0.0486 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1659 on 170 degrees of freedom
## Multiple R-squared: 0.2868, Adjusted R-squared: 0.2742
## F-statistic: 22.79 on 3 and 170 DF, p-value: 1.892e-12
##
##
## Value of test-statistic is: -5.8358 11.3693 17.0475
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -3.99 -3.43 -3.13
## phi2 6.22 4.75 4.07
## phi3 8.43 6.49 5.47
summary(ur.df(d_kurs, type = "trend", lags = 12, selectlags = "AIC"))
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -978.00 -130.61 -3.06 167.03 2096.54
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 85.32424 57.04665 1.496 0.1366
## z.lag.1 -1.23924 0.11227 -11.038 <2e-16 ***
## tt -0.23666 0.50789 -0.466 0.6418
## z.diff.lag 0.14029 0.07628 1.839 0.0676 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 336.4 on 170 degrees of freedom
## Multiple R-squared: 0.5504, Adjusted R-squared: 0.5425
## F-statistic: 69.38 on 3 and 170 DF, p-value: < 2.2e-16
##
##
## Value of test-statistic is: -11.0379 40.62 60.9274
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -3.99 -3.43 -3.13
## phi2 6.22 4.75 4.07
## phi3 8.43 6.49 5.47
# 9. DATA FINAL UNTUK VAR
model_data <- cbind(
inflasi = ts_data[, "inflasi"][-1],
birate = d_birate,
kurs = d_kurs
)
model_data <- na.omit(model_data)
head(model_data)
## inflasi birate kurs
## Feb 2011 0.13 0.25 -234
## Mar 2011 -0.32 0.00 -114
## Apr 2011 -0.31 0.00 -135
## May 2011 0.12 0.00 -37
## Jun 2011 0.55 0.00 60
## Jul 2011 0.67 0.00 -89
# 10. PILIH LAG OPTIMAL
lag_select <- VARselect(model_data, lag.max = 12, type = "const")
lag_select$selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 2 2 1 2
# 11. ESTIMASI VAR (GANTI p SESUAI HASIL DI ATAS)
var_model <- VAR(model_data, p = 2, type = "const")
summary(var_model)
##
## VAR Estimation Results:
## =========================
## Endogenous variables: inflasi, birate, kurs
## Deterministic variables: const
## Sample size: 185
## Log Likelihood: -1341.028
## Roots of the characteristic polynomial:
## 0.6289 0.584 0.584 0.3473 0.3473 0.2429
## Call:
## VAR(y = model_data, p = 2, type = "const")
##
##
## Estimation results for equation inflasi:
## ========================================
## inflasi = inflasi.l1 + birate.l1 + kurs.l1 + inflasi.l2 + birate.l2 + kurs.l2 + const
##
## Estimate Std. Error t value Pr(>|t|)
## inflasi.l1 3.454e-01 7.182e-02 4.809 3.22e-06 ***
## birate.l1 3.314e-01 1.904e-01 1.741 0.0834 .
## kurs.l1 1.383e-05 9.445e-05 0.146 0.8837
## inflasi.l2 -3.295e-01 7.221e-02 -4.563 9.37e-06 ***
## birate.l2 -1.526e-01 1.875e-01 -0.814 0.4166
## kurs.l2 8.020e-05 9.485e-05 0.846 0.3989
## const 3.002e-01 4.100e-02 7.323 8.10e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Residual standard error: 0.414 on 178 degrees of freedom
## Multiple R-Squared: 0.2009, Adjusted R-squared: 0.174
## F-statistic: 7.459 on 6 and 178 DF, p-value: 3.867e-07
##
##
## Estimation results for equation birate:
## =======================================
## birate = inflasi.l1 + birate.l1 + kurs.l1 + inflasi.l2 + birate.l2 + kurs.l2 + const
##
## Estimate Std. Error t value Pr(>|t|)
## inflasi.l1 1.158e-02 2.813e-02 0.412 0.6811
## birate.l1 3.602e-01 7.455e-02 4.832 2.91e-06 ***
## kurs.l1 7.998e-05 3.699e-05 2.162 0.0319 *
## inflasi.l2 -6.622e-02 2.828e-02 -2.342 0.0203 *
## birate.l2 1.609e-01 7.341e-02 2.192 0.0297 *
## kurs.l2 8.550e-05 3.715e-05 2.302 0.0225 *
## const 5.892e-03 1.606e-02 0.367 0.7141
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Residual standard error: 0.1622 on 178 degrees of freedom
## Multiple R-Squared: 0.2794, Adjusted R-squared: 0.2551
## F-statistic: 11.5 on 6 and 178 DF, p-value: 7.357e-11
##
##
## Estimation results for equation kurs:
## =====================================
## kurs = inflasi.l1 + birate.l1 + kurs.l1 + inflasi.l2 + birate.l2 + kurs.l2 + const
##
## Estimate Std. Error t value Pr(>|t|)
## inflasi.l1 51.72788 56.30011 0.919 0.3594
## birate.l1 204.73188 149.22273 1.372 0.1718
## kurs.l1 -0.12516 0.07404 -1.690 0.0927 .
## inflasi.l2 85.67447 56.60289 1.514 0.1319
## birate.l2 -70.69989 146.94693 -0.481 0.6310
## kurs.l2 -0.16039 0.07436 -2.157 0.0323 *
## const 21.28227 32.14225 0.662 0.5087
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Residual standard error: 324.6 on 178 degrees of freedom
## Multiple R-Squared: 0.06263, Adjusted R-squared: 0.03103
## F-statistic: 1.982 on 6 and 178 DF, p-value: 0.07048
##
##
##
## Covariance matrix of residuals:
## inflasi birate kurs
## inflasi 0.1714 0.01400 9.869e+00
## birate 0.0140 0.02629 6.738e+00
## kurs 9.8691 6.73786 1.053e+05
##
## Correlation matrix of residuals:
## inflasi birate kurs
## inflasi 1.00000 0.2086 0.07344
## birate 0.20860 1.0000 0.12802
## kurs 0.07344 0.1280 1.00000
# 12. UJI DIAGNOSTIK
serial.test(var_model, lags.pt = 12, type = "PT.asymptotic")
##
## Portmanteau Test (asymptotic)
##
## data: Residuals of VAR object var_model
## Chi-squared = 90.658, df = 90, p-value = 0.4607
arch.test(var_model, lags.multi = 12)
##
## ARCH (multivariate)
##
## data: Residuals of VAR object var_model
## Chi-squared = 330.84, df = 432, p-value = 0.9999
normality.test(var_model)
## $JB
##
## JB-Test (multivariate)
##
## data: Residuals of VAR object var_model
## Chi-squared = 3446.9, df = 6, p-value < 2.2e-16
##
##
## $Skewness
##
## Skewness only (multivariate)
##
## data: Residuals of VAR object var_model
## Chi-squared = 242.88, df = 3, p-value < 2.2e-16
##
##
## $Kurtosis
##
## Kurtosis only (multivariate)
##
## data: Residuals of VAR object var_model
## Chi-squared = 3204.1, df = 3, p-value < 2.2e-16
# 13. CEK STABILITAS
roots(var_model)
## [1] 0.6289188 0.5839940 0.5839940 0.3473020 0.3473020 0.2429450
dev.off()
## null device
## 1
par(mfrow = c(1, 1))
plot(stability(var_model))
# 14. IRF (CONTOH 3)
plot(irf(var_model, impulse = "inflasi", response = "inflasi", n.ahead = 24))
plot(irf(var_model, impulse = "birate", response = "inflasi", n.ahead = 24))
plot(irf(var_model, impulse = "kurs", response = "inflasi", n.ahead = 24))
# 15. FEVD
fevd(var_model, n.ahead = 24)
## $inflasi
## inflasi birate kurs
## [1,] 1.0000000 0.00000000 0.000000e+00
## [2,] 0.9856854 0.01421481 9.984009e-05
## [3,] 0.9787154 0.01514494 6.139676e-03
## [4,] 0.9784636 0.01465768 6.878743e-03
## [5,] 0.9777001 0.01478296 7.516926e-03
## [6,] 0.9775602 0.01472494 7.714875e-03
## [7,] 0.9774793 0.01477439 7.746307e-03
## [8,] 0.9774172 0.01478314 7.799647e-03
## [9,] 0.9774196 0.01478134 7.799097e-03
## [10,] 0.9774153 0.01478166 7.803050e-03
## [11,] 0.9774151 0.01478149 7.803381e-03
## [12,] 0.9774145 0.01478184 7.803684e-03
## [13,] 0.9774142 0.01478187 7.803906e-03
## [14,] 0.9774142 0.01478187 7.803902e-03
## [15,] 0.9774142 0.01478187 7.803920e-03
## [16,] 0.9774142 0.01478187 7.803920e-03
## [17,] 0.9774142 0.01478187 7.803923e-03
## [18,] 0.9774142 0.01478187 7.803924e-03
## [19,] 0.9774142 0.01478187 7.803924e-03
## [20,] 0.9774142 0.01478187 7.803924e-03
## [21,] 0.9774142 0.01478187 7.803924e-03
## [22,] 0.9774142 0.01478187 7.803924e-03
## [23,] 0.9774142 0.01478187 7.803924e-03
## [24,] 0.9774142 0.01478187 7.803924e-03
##
## $birate
## inflasi birate kurs
## [1,] 0.04351197 0.9564880 0.00000000
## [2,] 0.04852656 0.9300917 0.02138175
## [3,] 0.04539243 0.9034429 0.05116472
## [4,] 0.04536680 0.9021470 0.05248619
## [5,] 0.04529448 0.9021025 0.05260303
## [6,] 0.04553367 0.9015825 0.05288387
## [7,] 0.04545611 0.9014385 0.05310536
## [8,] 0.04544018 0.9013776 0.05318218
## [9,] 0.04543030 0.9013697 0.05319996
## [10,] 0.04542909 0.9013663 0.05320463
## [11,] 0.04542919 0.9013633 0.05320751
## [12,] 0.04542842 0.9013622 0.05320935
## [13,] 0.04542818 0.9013618 0.05321002
## [14,] 0.04542807 0.9013617 0.05321019
## [15,] 0.04542805 0.9013617 0.05321025
## [16,] 0.04542804 0.9013617 0.05321028
## [17,] 0.04542804 0.9013617 0.05321030
## [18,] 0.04542803 0.9013617 0.05321030
## [19,] 0.04542803 0.9013617 0.05321030
## [20,] 0.04542803 0.9013617 0.05321030
## [21,] 0.04542803 0.9013617 0.05321031
## [22,] 0.04542803 0.9013617 0.05321031
## [23,] 0.04542803 0.9013617 0.05321031
## [24,] 0.04542803 0.9013617 0.05321031
##
## $kurs
## inflasi birate kurs
## [1,] 0.005393152 0.01327981 0.9813270
## [2,] 0.011175691 0.02003169 0.9687926
## [3,] 0.023768204 0.01974967 0.9564821
## [4,] 0.023753360 0.02029460 0.9559520
## [5,] 0.026371123 0.02031707 0.9533118
## [6,] 0.026455847 0.02031530 0.9532289
## [7,] 0.026696859 0.02031045 0.9529927
## [8,] 0.026756068 0.02031811 0.9529258
## [9,] 0.026763606 0.02032115 0.9529152
## [10,] 0.026775142 0.02032107 0.9529038
## [11,] 0.026775150 0.02032107 0.9529038
## [12,] 0.026776442 0.02032108 0.9529025
## [13,] 0.026776611 0.02032114 0.9529022
## [14,] 0.026776671 0.02032116 0.9529022
## [15,] 0.026776717 0.02032116 0.9529021
## [16,] 0.026776717 0.02032116 0.9529021
## [17,] 0.026776724 0.02032116 0.9529021
## [18,] 0.026776725 0.02032116 0.9529021
## [19,] 0.026776725 0.02032116 0.9529021
## [20,] 0.026776725 0.02032116 0.9529021
## [21,] 0.026776725 0.02032116 0.9529021
## [22,] 0.026776725 0.02032116 0.9529021
## [23,] 0.026776725 0.02032116 0.9529021
## [24,] 0.026776725 0.02032116 0.9529021
plot(fevd(var_model, n.ahead = 24))