# ============================================================
# VAR/VECM INFLASI, BI RATE, DAN KURS
# ============================================================
# 1. Package
library(readxl)
## Warning: package 'readxl' was built under R version 4.5.3
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.5.3
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.5.3
library(tseries)
## Warning: package 'tseries' was built under R version 4.5.3
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
library(urca)
## Warning: package 'urca' 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 4.5.2
##
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
##
## select
## 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 4.5.3
##
## 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: lmtest
## Warning: package 'lmtest' was built under R version 4.5.3
# 2. Import data
data <- read_excel("D:/Folder Farras/SEMESTER 4/ANALISIS RUNTUN WAKTU/uas/dataUASno2.xlsx")
# 3. Persiapan data
data$bulan <- as.Date(paste0(data$bulan, "-01"))
data <- data %>% arrange(bulan)
# 4. Time series
ts_data <- ts(
data[, c("inflasi", "birate", "kursrp")],
start = c(2011, 1),
frequency = 12
)
# ============================================================
# 5. PLOT DATA
# ============================================================
par(mfrow = c(3,1))
plot(ts_data[, "inflasi"],
main = "Inflasi",
ylab = "%")
plot(ts_data[, "birate"],
main = "BI Rate",
ylab = "%")
plot(ts_data[, "kursrp"],
main = "Kurs Rupiah/USD",
ylab = "Rp/USD")
par(mfrow = c(1,1))
# ============================================================
# 6. UJI ADF
# ============================================================
# Inflasi
adf_inflasi <- ur.df(
ts_data[, "inflasi"],
type = "drift",
selectlags = "AIC"
)
summary(adf_inflasi)
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression drift
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.12169 -0.22325 -0.06314 0.17417 2.59120
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.29647 0.03991 7.429 4.18e-12 ***
## z.lag.1 -0.96025 0.08345 -11.507 < 2e-16 ***
## z.diff.lag 0.34093 0.06942 4.911 2.02e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4146 on 181 degrees of freedom
## Multiple R-squared: 0.4333, Adjusted R-squared: 0.4271
## F-statistic: 69.2 on 2 and 181 DF, p-value: < 2.2e-16
##
##
## Value of test-statistic is: -11.5071 66.2076
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau2 -3.46 -2.88 -2.57
## phi1 6.52 4.63 3.81
# BI Rate
adf_birate <- ur.df(
ts_data[, "birate"],
type = "drift",
selectlags = "AIC"
)
summary(adf_birate)
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression drift
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.23048 -0.03737 0.00530 0.03848 0.48633
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.10373 0.05703 1.819 0.0706 .
## z.lag.1 -0.01896 0.00997 -1.902 0.0588 .
## z.diff.lag 0.48321 0.06617 7.302 8.66e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1658 on 181 degrees of freedom
## Multiple R-squared: 0.2341, Adjusted R-squared: 0.2257
## F-statistic: 27.67 on 2 and 181 DF, p-value: 3.28e-11
##
##
## Value of test-statistic is: -1.9018 1.8247
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau2 -3.46 -2.88 -2.57
## phi1 6.52 4.63 3.81
# Kurs
ln_kurs <- log(ts_data[, "kursrp"])
adf_kurs <- ur.df(
ln_kurs,
type = "drift",
selectlags = "AIC"
)
summary(adf_kurs)
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression drift
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.073824 -0.007673 -0.000246 0.009766 0.098683
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.100956 0.067723 1.491 0.13777
## z.lag.1 -0.010325 0.007133 -1.448 0.14948
## z.diff.lag 0.235053 0.071832 3.272 0.00128 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01797 on 181 degrees of freedom
## Multiple R-squared: 0.06619, Adjusted R-squared: 0.05587
## F-statistic: 6.415 on 2 and 181 DF, p-value: 0.002034
##
##
## Value of test-statistic is: -1.4475 3.4278
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau2 -3.46 -2.88 -2.57
## phi1 6.52 4.63 3.81
# First difference BI Rate
d_birate <- diff(ts_data[, "birate"])
adf_d_birate <- ur.df(
d_birate,
type = "drift",
selectlags = "AIC"
)
summary(adf_d_birate)
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression drift
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.21116 0.00062 0.00062 0.03884 0.50062
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0006207 0.0122743 -0.051 0.9597
## z.lag.1 -0.4345393 0.0786943 -5.522 1.16e-07 ***
## z.diff.lag -0.1528745 0.0753032 -2.030 0.0438 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1658 on 180 degrees of freedom
## Multiple R-squared: 0.2671, Adjusted R-squared: 0.2589
## F-statistic: 32.79 on 2 and 180 DF, p-value: 7.177e-13
##
##
## Value of test-statistic is: -5.5219 15.2806
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau2 -3.46 -2.88 -2.57
## phi1 6.52 4.63 3.81
# First difference log Kurs
d_lnkurs <- diff(ln_kurs)
adf_d_kurs <- ur.df(
d_lnkurs,
type = "drift",
selectlags = "AIC"
)
summary(adf_d_kurs)
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression drift
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.052530 -0.007926 -0.001255 0.008252 0.091833
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.003788 0.001323 2.863 0.004699 **
## z.lag.1 -0.981540 0.088767 -11.058 < 2e-16 ***
## z.diff.lag 0.275815 0.071622 3.851 0.000163 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01737 on 180 degrees of freedom
## Multiple R-squared: 0.4322, Adjusted R-squared: 0.4259
## F-statistic: 68.51 on 2 and 180 DF, p-value: < 2.2e-16
##
##
## Value of test-statistic is: -11.0575 61.1539
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau2 -3.46 -2.88 -2.57
## phi1 6.52 4.63 3.81
# ============================================================
# 7. UJI KOINTEGRASI JOHANSEN
# Hanya variabel I(1)
# ============================================================
data_i1 <- cbind(
birate = ts_data[, "birate"],
lnkurs = log(ts_data[, "kursrp"])
)
# Pemilihan lag
VARselect(
data_i1,
lag.max = 12,
type = "const"
)
## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 3 3 3 3
##
## $criteria
## 1 2 3 4 5
## AIC(n) -1.129870e+01 -1.159321e+01 -1.170587e+01 -1.166379e+01 -1.162666e+01
## HQ(n) -1.125451e+01 -1.151956e+01 -1.160276e+01 -1.153122e+01 -1.146463e+01
## SC(n) -1.118976e+01 -1.141165e+01 -1.145169e+01 -1.133699e+01 -1.122724e+01
## FPE(n) 1.238914e-05 9.228865e-06 8.245989e-06 8.601250e-06 8.927991e-06
## 6 7 8 9 10
## AIC(n) -1.160775e+01 -1.160177e+01 -1.159910e+01 -1.156837e+01 -1.154016e+01
## HQ(n) -1.141626e+01 -1.138082e+01 -1.134869e+01 -1.128850e+01 -1.123083e+01
## SC(n) -1.113571e+01 -1.105710e+01 -1.098181e+01 -1.087847e+01 -1.077762e+01
## FPE(n) 9.100394e-06 9.157775e-06 9.185835e-06 9.477165e-06 9.754412e-06
## 11 12
## AIC(n) -1.154408e+01 -1.152291e+01
## HQ(n) -1.120529e+01 -1.115466e+01
## SC(n) -1.070892e+01 -1.061514e+01
## FPE(n) 9.723517e-06 9.940360e-06
# Johansen trace test
johansen <- ca.jo(
data_i1,
type = "trace",
ecdet = "const",
K = 2,
spec = "transitory"
)
summary(johansen)
##
## ######################
## # Johansen-Procedure #
## ######################
##
## Test type: trace statistic , without linear trend and constant in cointegration
##
## Eigenvalues (lambda):
## [1] 4.813719e-02 1.953844e-02 -1.318390e-16
##
## Values of teststatistic and critical values of test:
##
## test 10pct 5pct 1pct
## r <= 1 | 3.63 7.52 9.24 12.97
## r = 0 | 12.71 17.85 19.96 24.60
##
## Eigenvectors, normalised to first column:
## (These are the cointegration relations)
##
## birate.l1 lnkurs.l1 constant
## birate.l1 1.000000 1.000000 1.000
## lnkurs.l1 -7.003972 3.605564 1466.484
## constant 63.645189 -40.147167 -13744.823
##
## Weights W:
## (This is the loading matrix)
##
## birate.l1 lnkurs.l1 constant
## birate.d -0.0051730906 -0.01650336 7.885043e-18
## lnkurs.d 0.0009525254 -0.00122101 1.772166e-18
# ============================================================
# 8. MEMBENTUK DATA STASIONER UNTUK VAR
# ============================================================
var_data <- cbind(
inflasi = ts_data[-1, "inflasi"],
d_birate = diff(ts_data[, "birate"]),
d_lnkurs = diff(log(ts_data[, "kursrp"]))
)
head(var_data)
## inflasi d_birate d_lnkurs
## Feb 2011 0.13 0.25 -0.0130377826
## Mar 2011 -0.32 0.00 -0.0177490006
## Apr 2011 -0.31 0.00 -0.0127598504
## May 2011 0.12 0.00 -0.0105000147
## Jun 2011 0.55 0.00 0.0004514997
## Jul 2011 0.67 0.00 -0.0038278868
# ============================================================
# 9. PEMILIHAN LAG VAR
# ============================================================
lag_selection <- VARselect(
var_data,
lag.max = 12,
type = "const"
)
lag_selection$selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 2 2 2 2
# ============================================================
# 10. ESTIMASI VAR
# ============================================================
model_var <- VAR(
var_data,
p = 2,
type = "const"
)
summary(model_var)
##
## VAR Estimation Results:
## =========================
## Endogenous variables: inflasi, d_birate, d_lnkurs
## Deterministic variables: const
## Sample size: 183
## Log Likelihood: 477.638
## Roots of the characteristic polynomial:
## 0.6806 0.6462 0.6462 0.4845 0.4845 0.3228
## Call:
## VAR(y = var_data, p = 2, type = "const")
##
##
## Estimation results for equation inflasi:
## ========================================
## inflasi = inflasi.l1 + d_birate.l1 + d_lnkurs.l1 + inflasi.l2 + d_birate.l2 + d_lnkurs.l2 + const
##
## Estimate Std. Error t value Pr(>|t|)
## inflasi.l1 0.34355 0.07172 4.790 3.52e-06 ***
## d_birate.l1 0.34646 0.19361 1.790 0.0753 .
## d_lnkurs.l1 1.25289 1.73257 0.723 0.4706
## inflasi.l2 -0.34018 0.07238 -4.700 5.23e-06 ***
## d_birate.l2 -0.13502 0.19363 -0.697 0.4865
## d_lnkurs.l2 1.50424 1.73704 0.866 0.3877
## const 0.30126 0.04147 7.264 1.17e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Residual standard error: 0.4125 on 176 degrees of freedom
## Multiple R-Squared: 0.2112, Adjusted R-squared: 0.1844
## F-statistic: 7.856 on 6 and 176 DF, p-value: 1.657e-07
##
##
## Estimation results for equation d_birate:
## =========================================
## d_birate = inflasi.l1 + d_birate.l1 + d_lnkurs.l1 + inflasi.l2 + d_birate.l2 + d_lnkurs.l2 + const
##
## Estimate Std. Error t value Pr(>|t|)
## inflasi.l1 0.007871 0.028172 0.279 0.78028
## d_birate.l1 0.375371 0.076049 4.936 1.84e-06 ***
## d_lnkurs.l1 1.853583 0.680548 2.724 0.00711 **
## inflasi.l2 -0.071144 0.028430 -2.502 0.01325 *
## d_birate.l2 0.196247 0.076056 2.580 0.01069 *
## d_lnkurs.l2 0.073145 0.682305 0.107 0.91475
## const 0.011723 0.016290 0.720 0.47271
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Residual standard error: 0.162 on 176 degrees of freedom
## Multiple R-Squared: 0.2884, Adjusted R-squared: 0.2642
## F-statistic: 11.89 on 6 and 176 DF, p-value: 3.491e-11
##
##
## Estimation results for equation d_lnkurs:
## =========================================
## d_lnkurs = inflasi.l1 + d_birate.l1 + d_lnkurs.l1 + inflasi.l2 + d_birate.l2 + d_lnkurs.l2 + const
##
## Estimate Std. Error t value Pr(>|t|)
## inflasi.l1 0.004146 0.002963 1.399 0.163499
## d_birate.l1 0.006895 0.007999 0.862 0.389865
## d_lnkurs.l1 0.257069 0.071585 3.591 0.000427 ***
## inflasi.l2 0.004520 0.002990 1.511 0.132465
## d_birate.l2 0.006280 0.008000 0.785 0.433551
## d_lnkurs.l2 -0.306843 0.071770 -4.275 3.12e-05 ***
## const 0.001496 0.001714 0.873 0.383821
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Residual standard error: 0.01704 on 176 degrees of freedom
## Multiple R-Squared: 0.1765, Adjusted R-squared: 0.1484
## F-statistic: 6.287 on 6 and 176 DF, p-value: 5.242e-06
##
##
##
## Covariance matrix of residuals:
## inflasi d_birate d_lnkurs
## inflasi 0.17019 0.0129879 0.0005700
## d_birate 0.01299 0.0262582 0.0003963
## d_lnkurs 0.00057 0.0003963 0.0002905
##
## Correlation matrix of residuals:
## inflasi d_birate d_lnkurs
## inflasi 1.00000 0.1943 0.08106
## d_birate 0.19429 1.0000 0.14347
## d_lnkurs 0.08106 0.1435 1.00000
# ============================================================
# 11. UJI STABILITAS
# ============================================================
roots(model_var)
## [1] 0.6805548 0.6462041 0.6462041 0.4845371 0.4845371 0.3228033
# ============================================================
# 12. UJI AUTOKORELASI
# ============================================================
serial.test(
model_var,
lags.pt = 12,
type = "PT.asymptotic"
)
##
## Portmanteau Test (asymptotic)
##
## data: Residuals of VAR object model_var
## Chi-squared = 89.488, df = 90, p-value = 0.4954
# ============================================================
# 13. UJI ARCH
# ============================================================
arch.test(
model_var,
lags.multi = 12
)
##
## ARCH (multivariate)
##
## data: Residuals of VAR object model_var
## Chi-squared = 344.08, df = 432, p-value = 0.9993
# ============================================================
# 14. UJI NORMALITAS
# ============================================================
normality.test(model_var)
## $JB
##
## JB-Test (multivariate)
##
## data: Residuals of VAR object model_var
## Chi-squared = 2550.3, df = 6, p-value < 2.2e-16
##
##
## $Skewness
##
## Skewness only (multivariate)
##
## data: Residuals of VAR object model_var
## Chi-squared = 205.7, df = 3, p-value < 2.2e-16
##
##
## $Kurtosis
##
## Kurtosis only (multivariate)
##
## data: Residuals of VAR object model_var
## Chi-squared = 2344.6, df = 3, p-value < 2.2e-16
# ============================================================
# 15. IRF
# ============================================================
# Shock BI Rate
irf_bi <- irf(
model_var,
impulse = "d_birate",
response = c("inflasi", "d_birate", "d_lnkurs"),
n.ahead = 24,
ortho = TRUE,
boot = TRUE,
runs = 1000
)
plot(irf_bi)
# Shock Kurs
irf_kurs <- irf(
model_var,
impulse = "d_lnkurs",
response = c("inflasi", "d_birate", "d_lnkurs"),
n.ahead = 24,
ortho = TRUE,
boot = TRUE,
runs = 1000
)
plot(irf_kurs)
# Shock Inflasi
irf_inflasi <- irf(
model_var,
impulse = "inflasi",
response = c("d_birate", "d_lnkurs"),
n.ahead = 24,
ortho = TRUE,
boot = TRUE,
runs = 1000
)
plot(irf_inflasi)
# Semua IRF
irf_all <- irf(
model_var,
n.ahead = 24,
ortho = TRUE,
boot = TRUE,
runs = 1000
)
plot(irf_all)
