Granger Result Year
2023-02-14
Granger Causality Test
##載入套件
library(lmtest)
library(dplyr)
library(ggplot2)
library(openxlsx)
library(vars)
library(tseries)
library(jtools)
library(forecast)
library(urca)##建立時間序列
year <- read.xlsx("E:/summer program/new data/byyear1.xlsx", sheet = '內插')
biomass.timeseries <- ts(year$aboveground距平, start = 1995)
leaf.timeseries <- ts(year$leaf距平, start = 1995)
cover.timeseries <- ts(year$cover距平, start = 1995)
relative.timeseries <- ts(year$relative距平, start = 1995)
pmmann.timeseries <- ts(year$PMMANN0, start = 1995)
wnp.timeseries <- ts(year$WNP0, start = 1995)
cpenso.timeseries <- ts(year$CPENSO0, start = 1995)
tempann.timeseries <- ts(year$TX年距平0, start = 1995)
salt.timeseries <- ts(year$Salt距平0, start = 1995)##檢測平穩性
ADF法
adf.test(na.omit(cover.timeseries))
adf.test(na.omit(leaf.timeseries))
adf.test(na.omit(relative.timeseries))
adf.test(na.omit(biomass.timeseries))
adf.test(na.omit(pmmann.timeseries))
adf.test(na.omit(cpenso.timeseries))
adf.test(na.omit(wnp.timeseries))
adf.test(na.omit(tempann.timeseries))
adf.test(na.omit(salt.timeseries))##
## Augmented Dickey-Fuller Test
##
## data: na.omit(cover.timeseries)
## Dickey-Fuller = -2.114, Lag order = 2, p-value = 0.5289
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: na.omit(leaf.timeseries)
## Dickey-Fuller = -2.2157, Lag order = 2, p-value = 0.4902
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: na.omit(relative.timeseries)
## Dickey-Fuller = -2.2034, Lag order = 2, p-value = 0.4949
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: na.omit(biomass.timeseries)
## Dickey-Fuller = -1.6929, Lag order = 2, p-value = 0.6894
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: na.omit(pmmann.timeseries)
## Dickey-Fuller = -2.2014, Lag order = 2, p-value = 0.4957
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: na.omit(cpenso.timeseries)
## Dickey-Fuller = -2.3858, Lag order = 2, p-value = 0.4254
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: na.omit(wnp.timeseries)
## Dickey-Fuller = -2.2173, Lag order = 2, p-value = 0.4896
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: na.omit(tempann.timeseries)
## Dickey-Fuller = -2.3302, Lag order = 2, p-value = 0.4466
## alternative hypothesis: stationary
##
##
## Augmented Dickey-Fuller Test
##
## data: na.omit(salt.timeseries)
## Dickey-Fuller = -2.2974, Lag order = 2, p-value = 0.4591
## alternative hypothesis: stationaryKPSS法
kpss.test(na.omit(cover.timeseries))
kpss.test(na.omit(leaf.timeseries))
kpss.test(na.omit(relative.timeseries))
kpss.test(na.omit(biomass.timeseries))
kpss.test(na.omit(pmmann.timeseries))
kpss.test(na.omit(cpenso.timeseries))
kpss.test(na.omit(wnp.timeseries))
kpss.test(na.omit(tempann.timeseries))
kpss.test(na.omit(salt.timeseries))##
## KPSS Test for Level Stationarity
##
## data: na.omit(cover.timeseries)
## KPSS Level = 0.34382, Truncation lag parameter = 2, p-value = 0.1
##
##
## KPSS Test for Level Stationarity
##
## data: na.omit(leaf.timeseries)
## KPSS Level = 0.1183, Truncation lag parameter = 2, p-value = 0.1
##
##
## KPSS Test for Level Stationarity
##
## data: na.omit(relative.timeseries)
## KPSS Level = 0.42795, Truncation lag parameter = 2, p-value = 0.06511
##
##
## KPSS Test for Level Stationarity
##
## data: na.omit(biomass.timeseries)
## KPSS Level = 0.22544, Truncation lag parameter = 2, p-value = 0.1
##
##
## KPSS Test for Level Stationarity
##
## data: na.omit(pmmann.timeseries)
## KPSS Level = 0.43649, Truncation lag parameter = 2, p-value = 0.06143
##
##
## KPSS Test for Level Stationarity
##
## data: na.omit(cpenso.timeseries)
## KPSS Level = 0.13865, Truncation lag parameter = 2, p-value = 0.1
##
##
## KPSS Test for Level Stationarity
##
## data: na.omit(wnp.timeseries)
## KPSS Level = 0.20561, Truncation lag parameter = 2, p-value = 0.1
##
##
## KPSS Test for Level Stationarity
##
## data: na.omit(tempann.timeseries)
## KPSS Level = 0.4036, Truncation lag parameter = 2, p-value = 0.0756
##
##
## KPSS Test for Level Stationarity
##
## data: na.omit(salt.timeseries)
## KPSS Level = 0.11672, Truncation lag parameter = 2, p-value = 0.1##檢測差分
ADF法
ndiffs(cover.timeseries, test="adf")
ndiffs(leaf.timeseries, test="adf")
ndiffs(relative.timeseries, test="adf")
ndiffs(biomass.timeseries, test="adf")
ndiffs(pmmann.timeseries, test="adf")
ndiffs(cpenso.timeseries, test="adf")
ndiffs(wnp.timeseries, test="adf")
ndiffs(tempann.timeseries, test="adf")
ndiffs(salt.timeseries, test="adf")## [1] 2
## [1] 1
## [1] 2
## [1] 2
## [1] 1
## [1] 0
## [1] 0
## [1] 1
## [1] 1KPSS法
ndiffs(cover.timeseries, test="kpss")
ndiffs(leaf.timeseries, test="kpss")
ndiffs(relative.timeseries, test="kpss")
ndiffs(biomass.timeseries, test="kpss")
ndiffs(pmmann.timeseries, test="kpss")
ndiffs(cpenso.timeseries, test="kpss")
ndiffs(wnp.timeseries, test="kpss")
ndiffs(tempann.timeseries, test="kpss")
ndiffs(salt.timeseries, test="kpss")## [1] 0
## [1] 0
## [1] 1
## [1] 0
## [1] 1
## [1] 0
## [1] 0
## [1] 1
## [1] 0relative, WNP, tempann做一次差分；adf的二次差分太難解釋 ##差分比較
relativediff <- diff(relative.timeseries)
par(mfrow=c(2,2))
plot.ts(relative.timeseries,main="原始relative")
plot.ts(relativediff,main="一階差分relative")
acf(relative.timeseries, na = na.omit, ,main="原始relative"); acf(relativediff, na = na.omit, main="一階差分relative")
wnpdiff <- diff(wnp.timeseries)
par(mfrow=c(2,2))
plot.ts(wnp.timeseries,main="原始WNP")
plot.ts(wnpdiff,main="一階差分WNP")
acf(wnp.timeseries, na = na.omit, ,main="原始WNP"); acf(wnpdiff, na = na.omit, main="一階差分WNP")
tempanndiff <- diff(tempann.timeseries)
par(mfrow=c(2,2))
plot.ts(tempann.timeseries,main="原始relative")
plot.ts(tempanndiff,main="一階差分relative")
acf(tempann.timeseries, na = na.omit, ,main="原始tempann"); acf(tempanndiff, na = na.omit, main="一階差分rtempann")
##建立校準時間序列
因為差分的原理為第一項與第二項之差為新序列之第一項，因此計算a與b之格蘭傑因果時，其結果會與現實差了一個時間單位。然而當此差距會包含0時，將導致一盲區無法被計算(沒有自回歸)，因此需要建立一個向後推移一單位之時間序列。
relative1.timeseries <- ts(year$relative距平1, start = 1995)
wnp1.timeseries <- ts(year$WNP1, start = 1995)
tempann1.timeseries <- ts(year$TX年距平1, start = 1995)
salt1.timeseries <- ts(year$Salt距平1, start = 1995)##建立校準差分序列
relative1diff <- diff(relative1.timeseries)
wnp1diff <- diff(wnp1.timeseries)
tempann1diff <- diff(tempann1.timeseries)
##Biomass與PMM的滯後選擇 & Granger Causality Test
BIOMASS與PMMANN
bio_pmm <- cbind(pmmann.timeseries, biomass.timeseries)
VARselect(y=na.omit(bio_pmm), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 2 1 1 1
##
## $criteria
## 1 2 3
## AIC(n) -1.3212675 -1.3259652 -0.9646238
## HQ(n) -1.2803441 -1.2577595 -0.8691358
## SC(n) -1.0244770 -0.8313142 -0.2721125
## FPE(n) 0.2684771 0.2736278 0.4155226AIC best at 2, BIC(SC) best at 1.
grangertest(biomass.timeseries ~ pmmann.timeseries, order = 1, data = bio_pmm)
grangertest(pmmann.timeseries ~ biomass.timeseries, order = 1, data = bio_pmm)
grangertest(biomass.timeseries ~ pmmann.timeseries, order = 2, data = bio_pmm)
grangertest(pmmann.timeseries ~ biomass.timeseries, order = 2, data = bio_pmm)## Granger causality test
##
## Model 1: biomass.timeseries ~ Lags(biomass.timeseries, 1:1) + Lags(pmmann.timeseries, 1:1)
## Model 2: biomass.timeseries ~ Lags(biomass.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 17
## 2 18 -1 7.2897 0.01518 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Granger causality test
##
## Model 1: pmmann.timeseries ~ Lags(pmmann.timeseries, 1:1) + Lags(biomass.timeseries, 1:1)
## Model 2: pmmann.timeseries ~ Lags(pmmann.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 17
## 2 18 -1 0.5411 0.472
## Granger causality test
##
## Model 1: biomass.timeseries ~ Lags(biomass.timeseries, 1:2) + Lags(pmmann.timeseries, 1:2)
## Model 2: biomass.timeseries ~ Lags(biomass.timeseries, 1:2)
## Res.Df Df F Pr(>F)
## 1 14
## 2 16 -2 4.9436 0.02375 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Granger causality test
##
## Model 1: pmmann.timeseries ~ Lags(pmmann.timeseries, 1:2) + Lags(biomass.timeseries, 1:2)
## Model 2: pmmann.timeseries ~ Lags(pmmann.timeseries, 1:2)
## Res.Df Df F Pr(>F)
## 1 14
## 2 16 -2 0.1063 0.8999PMM 領先 Biomass 1年/2年.
##Biomass與CPENSO的滯後選擇 & Granger Causality Test
BIOMASS與CPENSO
bio_cp <- cbind(cpenso.timeseries, biomass.timeseries)
VARselect(y=na.omit(bio_cp), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 2 2 1 2
##
## $criteria
## 1 2 3
## AIC(n) -2.039630 -2.1091423 -1.8611326
## HQ(n) -1.998706 -2.0409366 -1.7656446
## SC(n) -1.742839 -1.6144913 -1.1686212
## FPE(n) 0.130896 0.1250347 0.1695297AIC best at 2, BIC(SC) best at 1.
grangertest(biomass.timeseries ~ cpenso.timeseries, order = 1, data = bio_cp)
grangertest(cpenso.timeseries ~ biomass.timeseries, order = 1, data = bio_cp)
grangertest(biomass.timeseries ~ cpenso.timeseries, order = 2, data = bio_cp)
grangertest(cpenso.timeseries ~ biomass.timeseries, order = 2, data = bio_cp)## Granger causality test
##
## Model 1: biomass.timeseries ~ Lags(biomass.timeseries, 1:1) + Lags(cpenso.timeseries, 1:1)
## Model 2: biomass.timeseries ~ Lags(biomass.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 17
## 2 18 -1 2.3342 0.1449
## Granger causality test
##
## Model 1: cpenso.timeseries ~ Lags(cpenso.timeseries, 1:1) + Lags(biomass.timeseries, 1:1)
## Model 2: cpenso.timeseries ~ Lags(cpenso.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 17
## 2 18 -1 1.0433 0.3214
## Granger causality test
##
## Model 1: biomass.timeseries ~ Lags(biomass.timeseries, 1:2) + Lags(cpenso.timeseries, 1:2)
## Model 2: biomass.timeseries ~ Lags(biomass.timeseries, 1:2)
## Res.Df Df F Pr(>F)
## 1 14
## 2 16 -2 0.9942 0.3947
## Granger causality test
##
## Model 1: cpenso.timeseries ~ Lags(cpenso.timeseries, 1:2) + Lags(biomass.timeseries, 1:2)
## Model 2: cpenso.timeseries ~ Lags(cpenso.timeseries, 1:2)
## Res.Df Df F Pr(>F)
## 1 14
## 2 16 -2 3.0116 0.08169 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Biomass 領先 CPENSO 1年.
##Biomass與WNP的滯後選擇 & Granger Causality Test
BIOMASS與WNP差分
bio_wnpdiff <- cbind(biomass.timeseries, wnpdiff)
VARselect(y=na.omit(bio_wnpdiff), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 1 1 1 1
##
## $criteria
## 1 2 3
## AIC(n) -4.50456238 -4.35725912 -4.37961085
## HQ(n) -4.46363896 -4.28905342 -4.28412287
## SC(n) -4.20777180 -3.86260815 -3.68709948
## FPE(n) 0.01112807 0.01320341 0.01366107AIC, BIC(SC) best at 1.
grangertest(biomass.timeseries ~ wnpdiff, order = 1, data = bio_wnpdiff)
grangertest(wnpdiff ~ biomass.timeseries, order = 1, data = bio_wnpdiff)## Granger causality test
##
## Model 1: biomass.timeseries ~ Lags(biomass.timeseries, 1:1) + Lags(wnpdiff, 1:1)
## Model 2: biomass.timeseries ~ Lags(biomass.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 17
## 2 18 -1 0.5058 0.4866
## Granger causality test
##
## Model 1: wnpdiff ~ Lags(wnpdiff, 1:1) + Lags(biomass.timeseries, 1:1)
## Model 2: wnpdiff ~ Lags(wnpdiff, 1:1)
## Res.Df Df F Pr(>F)
## 1 17
## 2 18 -1 1.5272 0.2333No significant result.
BIOMASS與WNP1差分
bio_wnp1diff <- cbind(biomass.timeseries, wnp1diff)
VARselect(y=na.omit(bio_wnp1diff), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 1 1 1 1
##
## $criteria
## 1 2 3
## AIC(n) -4.699018956 -4.57090636 -4.4646396
## HQ(n) -4.658095537 -4.50270066 -4.3691516
## SC(n) -4.402228370 -4.07625538 -3.7721282
## FPE(n) 0.009161539 0.01066351 0.0125475AIC, BIC(SC) best at 1.
grangertest(biomass.timeseries ~ wnp1diff, order = 1, data = bio_wnp1diff)
grangertest(wnp1diff ~ biomass.timeseries, order = 1, data = bio_wnp1diff)## Granger causality test
##
## Model 1: biomass.timeseries ~ Lags(biomass.timeseries, 1:1) + Lags(wnp1diff, 1:1)
## Model 2: biomass.timeseries ~ Lags(biomass.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 17
## 2 18 -1 0.0147 0.9049
## Granger causality test
##
## Model 1: wnp1diff ~ Lags(wnp1diff, 1:1) + Lags(biomass.timeseries, 1:1)
## Model 2: wnp1diff ~ Lags(wnp1diff, 1:1)
## Res.Df Df F Pr(>F)
## 1 17
## 2 18 -1 0.1686 0.6865No significant result.
##Biomass與Temp的滯後選擇 & Granger Causality Test
BIOMASS與tempann差分
bio_tempanndiff <- cbind(biomass.timeseries, tempanndiff)
VARselect(y=na.omit(bio_tempanndiff), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 1 1 1 1
##
## $criteria
## 1 2 3
## AIC(n) -4.01389169 -3.74463410 -3.59865995
## HQ(n) -3.97296827 -3.67642840 -3.50317197
## SC(n) -3.71710110 -3.24998312 -2.90614859
## FPE(n) 0.01817672 0.02436383 0.02982959AIC, BIC(SC) best at 1.
grangertest(biomass.timeseries ~ tempanndiff, order = 1, data = bio_tempanndiff)
grangertest(tempanndiff ~ biomass.timeseries, order = 1, data = bio_tempanndiff)## Granger causality test
##
## Model 1: biomass.timeseries ~ Lags(biomass.timeseries, 1:1) + Lags(tempanndiff, 1:1)
## Model 2: biomass.timeseries ~ Lags(biomass.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 17
## 2 18 -1 0.2167 0.6475
## Granger causality test
##
## Model 1: tempanndiff ~ Lags(tempanndiff, 1:1) + Lags(biomass.timeseries, 1:1)
## Model 2: tempanndiff ~ Lags(tempanndiff, 1:1)
## Res.Df Df F Pr(>F)
## 1 17
## 2 18 -1 0.5242 0.4789No significant result.
BIOMASS與tempann1差分
bio_tempann1diff <- cbind(biomass.timeseries, tempann1diff)
VARselect(y=na.omit(bio_tempann1diff), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 1 1 1 1
##
## $criteria
## 1 2 3
## AIC(n) -4.05214877 -3.79537660 -3.5205777
## HQ(n) -4.01122535 -3.72717090 -3.4250897
## SC(n) -3.75535818 -3.30072563 -2.8280663
## FPE(n) 0.01749446 0.02315839 0.0322521AIC, BIC(SC) best at 1.
grangertest(biomass.timeseries ~ tempann1diff, order = 1, data = bio_tempann1diff)
grangertest(tempann1diff ~ biomass.timeseries, order = 1, data = bio_tempann1diff)## Granger causality test
##
## Model 1: biomass.timeseries ~ Lags(biomass.timeseries, 1:1) + Lags(tempann1diff, 1:1)
## Model 2: biomass.timeseries ~ Lags(biomass.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 17
## 2 18 -1 0.0525 0.8216
## Granger causality test
##
## Model 1: tempann1diff ~ Lags(tempann1diff, 1:1) + Lags(biomass.timeseries, 1:1)
## Model 2: tempann1diff ~ Lags(tempann1diff, 1:1)
## Res.Df Df F Pr(>F)
## 1 17
## 2 18 -1 1.0721 0.315No significant result.
##Biomass與Salinity的滯後選擇 & Granger Causality Test
BIOMASS與Salt
bio_salt <- cbind(salt.timeseries, biomass.timeseries)
VARselect(y=na.omit(bio_salt), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 1 1 1 1
##
## $criteria
## 1 2 3
## AIC(n) -3.31985556 -2.99584703 -2.86288112
## HQ(n) -3.27893214 -2.92764133 -2.76739314
## SC(n) -3.02306498 -2.50119606 -2.17036975
## FPE(n) 0.03638576 0.05151571 0.06225754AIC, BIC(SC) best at 1.
grangertest(biomass.timeseries ~ salt.timeseries, order = 1, data = bio_salt)
grangertest(salt.timeseries ~ biomass.timeseries, order = 1, data = bio_salt)## Granger causality test
##
## Model 1: biomass.timeseries ~ Lags(biomass.timeseries, 1:1) + Lags(salt.timeseries, 1:1)
## Model 2: biomass.timeseries ~ Lags(biomass.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 17
## 2 18 -1 8.7 0.008968 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Granger causality test
##
## Model 1: salt.timeseries ~ Lags(salt.timeseries, 1:1) + Lags(biomass.timeseries, 1:1)
## Model 2: salt.timeseries ~ Lags(salt.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 17
## 2 18 -1 0.012 0.9141Salinity 領先 Biomass 1年.
##Relative與PMM的滯後選擇 & Granger Causality Test
Relative差分與PMMANN
reldiff_pmm <- cbind(pmmann.timeseries, relativediff)
VARselect(y=na.omit(reldiff_pmm), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 1 1 1 1
##
## $criteria
## 1 2 3
## AIC(n) 5.685226 5.992419 6.266269
## HQ(n) 5.714457 6.041139 6.334476
## SC(n) 5.979301 6.482545 6.952444
## FPE(n) 296.691556 414.975877 584.143899AIC, BIC(SC) best at 1.
grangertest(relativediff ~ pmmann.timeseries, order = 1, data = reldiff_pmm)
grangertest(pmmann.timeseries ~ relativediff, order = 1, data = reldiff_pmm)## Granger causality test
##
## Model 1: relativediff ~ Lags(relativediff, 1:1) + Lags(pmmann.timeseries, 1:1)
## Model 2: relativediff ~ Lags(relativediff, 1:1)
## Res.Df Df F Pr(>F)
## 1 16
## 2 17 -1 0.427 0.5228
## Granger causality test
##
## Model 1: pmmann.timeseries ~ Lags(pmmann.timeseries, 1:1) + Lags(relativediff, 1:1)
## Model 2: pmmann.timeseries ~ Lags(pmmann.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 16
## 2 17 -1 0 0.9955No significant result.
Relative1差分與PMMANN
reldiff1_pmm <- cbind(pmmann.timeseries, relative1diff)
VARselect(y=na.omit(reldiff1_pmm), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 1 1 1 1
##
## $criteria
## 1 2 3
## AIC(n) 5.600412 5.854165 6.254868
## HQ(n) 5.629644 5.902884 6.323075
## SC(n) 5.894487 6.344290 6.941043
## FPE(n) 272.565642 361.392929 577.521901AIC, BIC(SC) best at 1.
grangertest(relative1diff ~ pmmann.timeseries, order = 1, data = reldiff1_pmm)
grangertest(pmmann.timeseries ~ relative1diff, order = 1, data = reldiff1_pmm)## Granger causality test
##
## Model 1: relative1diff ~ Lags(relative1diff, 1:1) + Lags(pmmann.timeseries, 1:1)
## Model 2: relative1diff ~ Lags(relative1diff, 1:1)
## Res.Df Df F Pr(>F)
## 1 16
## 2 17 -1 0.1572 0.697
## Granger causality test
##
## Model 1: pmmann.timeseries ~ Lags(pmmann.timeseries, 1:1) + Lags(relative1diff, 1:1)
## Model 2: pmmann.timeseries ~ Lags(pmmann.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 16
## 2 17 -1 0.7166 0.4097No significant result.
##Relative與CPENSO的滯後選擇 & Granger Causality Test
Relative差分與CPENSO
reldiff_cp <- cbind(cpenso.timeseries, relativediff)
VARselect(y=na.omit(reldiff_cp), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 1 1 1 1
##
## $criteria
## 1 2 3
## AIC(n) 4.663445 5.037655 5.289515
## HQ(n) 4.692677 5.086374 5.357723
## SC(n) 4.957521 5.527780 5.975691
## FPE(n) 106.795190 159.725316 219.948655AIC, BIC(SC) best at 1.
grangertest(relativediff ~ cpenso.timeseries, order = 1, data = reldiff_cp)
grangertest(cpenso.timeseries ~ relativediff, order = 1, data = reldiff_cp)## Granger causality test
##
## Model 1: relativediff ~ Lags(relativediff, 1:1) + Lags(cpenso.timeseries, 1:1)
## Model 2: relativediff ~ Lags(relativediff, 1:1)
## Res.Df Df F Pr(>F)
## 1 16
## 2 17 -1 0.4595 0.5075
## Granger causality test
##
## Model 1: cpenso.timeseries ~ Lags(cpenso.timeseries, 1:1) + Lags(relativediff, 1:1)
## Model 2: cpenso.timeseries ~ Lags(cpenso.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 16
## 2 17 -1 0.0863 0.7727No significant result.
Relative1差分與CPENSO
reldiff1_cp <- cbind(cpenso.timeseries, relative1diff)
VARselect(y=na.omit(reldiff1_cp), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 1 1 1 1
##
## $criteria
## 1 2 3
## AIC(n) 4.686459 5.086344 5.397108
## HQ(n) 4.715690 5.135063 5.465316
## SC(n) 4.980534 5.576469 6.083284
## FPE(n) 109.281418 167.694661 244.933580AIC, BIC(SC) best at 1.
grangertest(relative1diff ~ cpenso.timeseries, order = 1, data = reldiff1_cp)
grangertest(cpenso.timeseries ~ relative1diff, order = 1, data = reldiff1_cp)## Granger causality test
##
## Model 1: relative1diff ~ Lags(relative1diff, 1:1) + Lags(cpenso.timeseries, 1:1)
## Model 2: relative1diff ~ Lags(relative1diff, 1:1)
## Res.Df Df F Pr(>F)
## 1 16
## 2 17 -1 0.0313 0.8618
## Granger causality test
##
## Model 1: cpenso.timeseries ~ Lags(cpenso.timeseries, 1:1) + Lags(relative1diff, 1:1)
## Model 2: cpenso.timeseries ~ Lags(cpenso.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 16
## 2 17 -1 0.1313 0.7218No significant result.
##Relative與WNP的滯後選擇 & Granger Causality Test
Relative差分與WNP差分
reldiff_wnpdiff <- cbind(relativediff, wnpdiff)
VARselect(y=na.omit(reldiff_wnpdiff), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 3 3 1 3
##
## $criteria
## 1 2 3
## AIC(n) 1.501655 1.548706 1.219419
## HQ(n) 1.530887 1.597425 1.287626
## SC(n) 1.795730 2.038831 1.905594
## FPE(n) 4.522761 4.876885 3.755785AIC, BIC(SC) best at 1.
grangertest(relativediff ~ wnpdiff, order = 1, data = reldiff_wnpdiff)
grangertest(wnpdiff ~ relativediff, order = 1, data = reldiff_wnpdiff)## Granger causality test
##
## Model 1: relativediff ~ Lags(relativediff, 1:1) + Lags(wnpdiff, 1:1)
## Model 2: relativediff ~ Lags(relativediff, 1:1)
## Res.Df Df F Pr(>F)
## 1 16
## 2 17 -1 0.049 0.8277
## Granger causality test
##
## Model 1: wnpdiff ~ Lags(wnpdiff, 1:1) + Lags(relativediff, 1:1)
## Model 2: wnpdiff ~ Lags(wnpdiff, 1:1)
## Res.Df Df F Pr(>F)
## 1 16
## 2 17 -1 8.9926 0.008501 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Relative年間差 領先 WNP年間差 1年.
##Relative與Temp的滯後選擇 & Granger Causality Test
Relative差分與tempann差分
reldiff_tempanndiff <- cbind(relativediff, tempanndiff)
VARselect(y=na.omit(reldiff_tempanndiff), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 1 1 1 1
##
## $criteria
## 1 2 3
## AIC(n) 2.281784 2.527749 2.524327
## HQ(n) 2.311016 2.576468 2.592534
## SC(n) 2.575860 3.017874 3.210502
## FPE(n) 9.867552 12.981817 13.848892AIC, BIC(SC) best at 1.
grangertest(relativediff ~ tempanndiff, order = 1, data = reldiff_tempanndiff)
grangertest(tempanndiff ~ relativediff, order = 1, data = reldiff_tempanndiff)## Granger causality test
##
## Model 1: relativediff ~ Lags(relativediff, 1:1) + Lags(tempanndiff, 1:1)
## Model 2: relativediff ~ Lags(relativediff, 1:1)
## Res.Df Df F Pr(>F)
## 1 16
## 2 17 -1 0.3677 0.5528
## Granger causality test
##
## Model 1: tempanndiff ~ Lags(tempanndiff, 1:1) + Lags(relativediff, 1:1)
## Model 2: tempanndiff ~ Lags(tempanndiff, 1:1)
## Res.Df Df F Pr(>F)
## 1 16
## 2 17 -1 1.7255 0.2075No significant result.
##Relative與WNP的滯後選擇 & Granger Causality Test
Relative差分與Salinity
reldiff_salt <- cbind(salt.timeseries, relativediff)
VARselect(y=na.omit(reldiff_salt), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 1 1 1 1
##
## $criteria
## 1 2 3
## AIC(n) 3.304684 3.406447 3.600331
## HQ(n) 3.333916 3.455166 3.668538
## SC(n) 3.598760 3.896572 4.286507
## FPE(n) 27.444120 31.257131 40.617930AIC, BIC(SC) best at 1.
grangertest(relativediff ~ salt.timeseries, order = 1, data = reldiff_salt)
grangertest(salt.timeseries ~ relativediff, order = 1, data = reldiff_salt)## Granger causality test
##
## Model 1: relativediff ~ Lags(relativediff, 1:1) + Lags(salt.timeseries, 1:1)
## Model 2: relativediff ~ Lags(relativediff, 1:1)
## Res.Df Df F Pr(>F)
## 1 16
## 2 17 -1 1.0195 0.3277
## Granger causality test
##
## Model 1: salt.timeseries ~ Lags(salt.timeseries, 1:1) + Lags(relativediff, 1:1)
## Model 2: salt.timeseries ~ Lags(salt.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 16
## 2 17 -1 1.9615 0.1804No significant result.
Relative1差分與Salt
reldiff1_salt <- cbind(salt.timeseries, relative1diff)
VARselect(y=na.omit(reldiff1_salt), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 2 2 1 2
##
## $criteria
## 1 2 3
## AIC(n) 3.479530 3.428275 3.588885
## HQ(n) 3.508762 3.476995 3.657092
## SC(n) 3.773606 3.918401 4.275060
## FPE(n) 32.687670 31.946941 40.155662AIC best at 2, BIC(SC) best at 1.
grangertest(relative1diff ~ salt.timeseries, order = 1, data = reldiff1_salt)
grangertest(salt.timeseries ~ relative1diff, order = 1, data = reldiff1_salt)
grangertest(relative1diff ~ salt.timeseries, order = 2, data = reldiff1_salt)
grangertest(salt.timeseries ~ relative1diff, order = 2, data = reldiff1_salt)## Granger causality test
##
## Model 1: relative1diff ~ Lags(relative1diff, 1:1) + Lags(salt.timeseries, 1:1)
## Model 2: relative1diff ~ Lags(relative1diff, 1:1)
## Res.Df Df F Pr(>F)
## 1 16
## 2 17 -1 0.4396 0.5168
## Granger causality test
##
## Model 1: salt.timeseries ~ Lags(salt.timeseries, 1:1) + Lags(relative1diff, 1:1)
## Model 2: salt.timeseries ~ Lags(salt.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 16
## 2 17 -1 0.1373 0.7158
## Granger causality test
##
## Model 1: relative1diff ~ Lags(relative1diff, 1:2) + Lags(salt.timeseries, 1:2)
## Model 2: relative1diff ~ Lags(relative1diff, 1:2)
## Res.Df Df F Pr(>F)
## 1 13
## 2 15 -2 0.6105 0.5579
## Granger causality test
##
## Model 1: salt.timeseries ~ Lags(salt.timeseries, 1:2) + Lags(relative1diff, 1:2)
## Model 2: salt.timeseries ~ Lags(salt.timeseries, 1:2)
## Res.Df Df F Pr(>F)
## 1 13
## 2 15 -2 0.9658 0.4064No significant result.
##Temp與PMM的滯後選擇 & Granger Causality Test
Tempann差分與PMM
tempdiff_pmm <- cbind(pmmann.timeseries, tempanndiff)
VARselect(y=na.omit(tempdiff_pmm), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 1 1 1 1
##
## $criteria
## 1 2 3
## AIC(n) -0.11526586 0.1379399 0.1491686
## HQ(n) -0.05049783 0.2458866 0.3002940
## SC(n) 0.18316912 0.6353315 0.8455169
## FPE(n) 0.89464321 1.1694980 1.2240067AIC, BIC(SC) best at 1.
grangertest(tempanndiff ~ pmmann.timeseries, order = 1, data = tempdiff_pmm)
grangertest(pmmann.timeseries ~ tempanndiff, order = 1, data = tempdiff_pmm)## Granger causality test
##
## Model 1: tempanndiff ~ Lags(tempanndiff, 1:1) + Lags(pmmann.timeseries, 1:1)
## Model 2: tempanndiff ~ Lags(tempanndiff, 1:1)
## Res.Df Df F Pr(>F)
## 1 20
## 2 21 -1 0.1325 0.7197
## Granger causality test
##
## Model 1: pmmann.timeseries ~ Lags(pmmann.timeseries, 1:1) + Lags(tempanndiff, 1:1)
## Model 2: pmmann.timeseries ~ Lags(pmmann.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 20
## 2 21 -1 0.635 0.4349No significant result.
Tempann1差分與PMM
tempdiff1_pmm <- cbind(pmmann.timeseries, tempann1diff)
VARselect(y=na.omit(tempdiff1_pmm), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 1 1 1 1
##
## $criteria
## 1 2 3
## AIC(n) -0.09414944 0.0784403 0.1174841
## HQ(n) -0.02938141 0.1863870 0.2686095
## SC(n) 0.20428555 0.5758319 0.8138323
## FPE(n) 0.91373575 1.1019430 1.1858325AIC best at 2, BIC(SC) best at 1.
grangertest(tempann1diff ~ pmmann.timeseries, order = 1, data = tempdiff1_pmm)
grangertest(pmmann.timeseries ~ tempann1diff, order = 1, data = tempdiff1_pmm)
grangertest(tempann1diff ~ pmmann.timeseries, order = 2, data = tempdiff1_pmm)
grangertest(pmmann.timeseries ~ tempann1diff, order = 2, data = tempdiff1_pmm)## Granger causality test
##
## Model 1: tempann1diff ~ Lags(tempann1diff, 1:1) + Lags(pmmann.timeseries, 1:1)
## Model 2: tempann1diff ~ Lags(tempann1diff, 1:1)
## Res.Df Df F Pr(>F)
## 1 20
## 2 21 -1 0.0172 0.8969
## Granger causality test
##
## Model 1: pmmann.timeseries ~ Lags(pmmann.timeseries, 1:1) + Lags(tempann1diff, 1:1)
## Model 2: pmmann.timeseries ~ Lags(pmmann.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 20
## 2 21 -1 0.4775 0.4975
## Granger causality test
##
## Model 1: tempann1diff ~ Lags(tempann1diff, 1:2) + Lags(pmmann.timeseries, 1:2)
## Model 2: tempann1diff ~ Lags(tempann1diff, 1:2)
## Res.Df Df F Pr(>F)
## 1 17
## 2 19 -2 0.0811 0.9225
## Granger causality test
##
## Model 1: pmmann.timeseries ~ Lags(pmmann.timeseries, 1:2) + Lags(tempann1diff, 1:2)
## Model 2: pmmann.timeseries ~ Lags(pmmann.timeseries, 1:2)
## Res.Df Df F Pr(>F)
## 1 17
## 2 19 -2 0.6855 0.5172No significant result.
##Temp與CPENSO的滯後選擇 & Granger Causality Test
Tempann差分與CPENSO
tempdiff_cpenso <- cbind(cpenso.timeseries, tempanndiff)
VARselect(y=na.omit(tempdiff_cpenso), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 1 1 1 1
##
## $criteria
## 1 2 3
## AIC(n) -1.3075352 -1.0839928 -0.8184296
## HQ(n) -1.2492220 -0.9868041 -0.6823654
## SC(n) -1.0088155 -0.5861267 -0.1214170
## FPE(n) 0.2717226 0.3456455 0.4692336AIC, BIC(SC) best at 1.
grangertest(tempanndiff ~ cpenso.timeseries, order = 1, data = tempdiff_cpenso)
grangertest(cpenso.timeseries ~ tempanndiff, order = 1, data = tempdiff_cpenso)## Granger causality test
##
## Model 1: tempanndiff ~ Lags(tempanndiff, 1:1) + Lags(cpenso.timeseries, 1:1)
## Model 2: tempanndiff ~ Lags(tempanndiff, 1:1)
## Res.Df Df F Pr(>F)
## 1 19
## 2 20 -1 1.043 0.3199
## Granger causality test
##
## Model 1: cpenso.timeseries ~ Lags(cpenso.timeseries, 1:1) + Lags(tempanndiff, 1:1)
## Model 2: cpenso.timeseries ~ Lags(cpenso.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 19
## 2 20 -1 4.2086 0.05426 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Tempann 與 CPENSO 同時
Tempann1差分與CPENSO
tempdiff1_cpenso <- cbind(cpenso.timeseries, tempann1diff)
VARselect(y=na.omit(tempdiff1_cpenso), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 1 1 1 1
##
## $criteria
## 1 2 3
## AIC(n) -1.2432004 -1.0989387 -0.8340392
## HQ(n) -1.1784324 -0.9909920 -0.6829138
## SC(n) -0.9447654 -0.6015471 -0.1376909
## FPE(n) 0.2895970 0.3394923 0.4579121AIC, BIC(SC) best at 1.
grangertest(tempann1diff ~ cpenso.timeseries, order = 1, data = tempdiff1_cpenso)
grangertest(cpenso.timeseries ~ tempann1diff, order = 1, data = tempdiff1_cpenso)## Granger causality test
##
## Model 1: tempann1diff ~ Lags(tempann1diff, 1:1) + Lags(cpenso.timeseries, 1:1)
## Model 2: tempann1diff ~ Lags(tempann1diff, 1:1)
## Res.Df Df F Pr(>F)
## 1 20
## 2 21 -1 0.0407 0.8421
## Granger causality test
##
## Model 1: cpenso.timeseries ~ Lags(cpenso.timeseries, 1:1) + Lags(tempann1diff, 1:1)
## Model 2: cpenso.timeseries ~ Lags(cpenso.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 20
## 2 21 -1 2.168 0.1565No significant result.
##Temp與WNP的滯後選擇 & Granger Causality Test
Tempann差分與WNP差分
tempdiff_wnpdiff <- cbind(wnpdiff, tempanndiff)
VARselect(y=na.omit(tempdiff_wnpdiff), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 2 1 1 2
##
## $criteria
## 1 2 3
## AIC(n) -3.9598531 -3.99088886 -3.80671875
## HQ(n) -3.8950851 -3.88294215 -3.65559334
## SC(n) -3.6614182 -3.49349723 -3.11037045
## FPE(n) 0.0191411 0.01883094 0.02342954AIC best at 2, BIC(SC) best at 1.
grangertest(wnpdiff ~ tempanndiff, order = 1, data = tempdiff_wnpdiff)
grangertest(tempanndiff ~ wnpdiff, order = 1, data = tempdiff_wnpdiff)
grangertest(wnpdiff ~ tempanndiff, order = 2, data = tempdiff_wnpdiff)
grangertest(tempanndiff ~ wnpdiff, order = 2, data = tempdiff_wnpdiff)## Granger causality test
##
## Model 1: wnpdiff ~ Lags(wnpdiff, 1:1) + Lags(tempanndiff, 1:1)
## Model 2: wnpdiff ~ Lags(wnpdiff, 1:1)
## Res.Df Df F Pr(>F)
## 1 20
## 2 21 -1 4.8575 0.03941 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Granger causality test
##
## Model 1: tempanndiff ~ Lags(tempanndiff, 1:1) + Lags(wnpdiff, 1:1)
## Model 2: tempanndiff ~ Lags(tempanndiff, 1:1)
## Res.Df Df F Pr(>F)
## 1 20
## 2 21 -1 0.0095 0.9235
## Granger causality test
##
## Model 1: wnpdiff ~ Lags(wnpdiff, 1:2) + Lags(tempanndiff, 1:2)
## Model 2: wnpdiff ~ Lags(wnpdiff, 1:2)
## Res.Df Df F Pr(>F)
## 1 17
## 2 19 -2 1.0354 0.3764
## Granger causality test
##
## Model 1: tempanndiff ~ Lags(tempanndiff, 1:2) + Lags(wnpdiff, 1:2)
## Model 2: tempanndiff ~ Lags(tempanndiff, 1:2)
## Res.Df Df F Pr(>F)
## 1 17
## 2 19 -2 3.4939 0.05357 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1WNP 領先 Temp 2年；Temp 領先 WNP 1年
##Salt與PMM的滯後選擇 & Granger Causality Test
Salt與PMM
salt_pmm <- cbind(pmmann.timeseries, salt.timeseries)
VARselect(y=na.omit(salt_pmm), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 1 1 1 1
##
## $criteria
## 1 2 3
## AIC(n) 0.9040178 1.219647 1.516012
## HQ(n) 0.9449412 1.287852 1.611500
## SC(n) 1.2008084 1.714298 2.208524
## FPE(n) 2.4850571 3.489035 4.965022AIC, BIC(SC) best at 1.
grangertest(salt.timeseries ~ pmmann.timeseries, order = 1, data = salt_pmm)
grangertest(pmmann.timeseries ~ salt.timeseries, order = 1, data = salt_pmm)## Granger causality test
##
## Model 1: salt.timeseries ~ Lags(salt.timeseries, 1:1) + Lags(pmmann.timeseries, 1:1)
## Model 2: salt.timeseries ~ Lags(salt.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 17
## 2 18 -1 0.7659 0.3937
## Granger causality test
##
## Model 1: pmmann.timeseries ~ Lags(pmmann.timeseries, 1:1) + Lags(salt.timeseries, 1:1)
## Model 2: pmmann.timeseries ~ Lags(pmmann.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 17
## 2 18 -1 0.0585 0.8118No significant result.
##Salt與CPENSO的滯後選擇 & Granger Causality Test
Salt與CPENSO
salt_cpenso <- cbind(cpenso.timeseries, salt.timeseries)
VARselect(y=na.omit(salt_cpenso), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 1 1 1 1
##
## $criteria
## 1 2 3
## AIC(n) -0.06432534 -0.02888074 0.3042032
## HQ(n) -0.02340192 0.03932496 0.3996912
## SC(n) 0.23246524 0.46577024 0.9967146
## FPE(n) 0.94360509 1.00109847 1.4778802AIC, BIC(SC) best at 1.
grangertest(salt.timeseries ~ cpenso.timeseries, order = 1, data = salt_cpenso)
grangertest(cpenso.timeseries ~ salt.timeseries, order = 1, data = salt_cpenso)## Granger causality test
##
## Model 1: salt.timeseries ~ Lags(salt.timeseries, 1:1) + Lags(cpenso.timeseries, 1:1)
## Model 2: salt.timeseries ~ Lags(salt.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 17
## 2 18 -1 0.0592 0.8107
## Granger causality test
##
## Model 1: cpenso.timeseries ~ Lags(cpenso.timeseries, 1:1) + Lags(salt.timeseries, 1:1)
## Model 2: cpenso.timeseries ~ Lags(cpenso.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 17
## 2 18 -1 2.4229 0.138No significant result.
##Salt與WNP的滯後選擇 & Granger Causality Test
Salt與WNP差分
salt_wnpdiff <- cbind(wnpdiff, salt.timeseries)
VARselect(y=na.omit(salt_wnpdiff), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 2 1 1 1
##
## $criteria
## 1 2 3
## AIC(n) -2.58238320 -2.5956690 -2.2309684
## HQ(n) -2.54145978 -2.5274633 -2.1354804
## SC(n) -2.28559261 -2.1010180 -1.5384570
## FPE(n) 0.07606969 0.0768661 0.1171192AIC best at 2, BIC(SC) best at 1.
grangertest(wnpdiff ~ salt.timeseries, order = 1, data = salt_wnpdiff)
grangertest(salt.timeseries ~ wnpdiff, order = 1, data = salt_wnpdiff)
grangertest(wnpdiff ~ salt.timeseries, order = 2, data = salt_wnpdiff)
grangertest(salt.timeseries ~ wnpdiff, order = 2, data = salt_wnpdiff)## Granger causality test
##
## Model 1: wnpdiff ~ Lags(wnpdiff, 1:1) + Lags(salt.timeseries, 1:1)
## Model 2: wnpdiff ~ Lags(wnpdiff, 1:1)
## Res.Df Df F Pr(>F)
## 1 17
## 2 18 -1 0.6116 0.4449
## Granger causality test
##
## Model 1: salt.timeseries ~ Lags(salt.timeseries, 1:1) + Lags(wnpdiff, 1:1)
## Model 2: salt.timeseries ~ Lags(salt.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 17
## 2 18 -1 1.6202 0.2202
## Granger causality test
##
## Model 1: wnpdiff ~ Lags(wnpdiff, 1:2) + Lags(salt.timeseries, 1:2)
## Model 2: wnpdiff ~ Lags(wnpdiff, 1:2)
## Res.Df Df F Pr(>F)
## 1 14
## 2 16 -2 1.9857 0.1741
## Granger causality test
##
## Model 1: salt.timeseries ~ Lags(salt.timeseries, 1:2) + Lags(wnpdiff, 1:2)
## Model 2: salt.timeseries ~ Lags(salt.timeseries, 1:2)
## Res.Df Df F Pr(>F)
## 1 14
## 2 16 -2 1.3883 0.2818No significant result.
Salt與WNP1差分
salt_wnp1diff <- cbind(wnp1diff, salt.timeseries)
VARselect(y=na.omit(salt_wnp1diff), lag.max = 3, type = c("const"))## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 3 3 1 3
##
## $criteria
## 1 2 3
## AIC(n) -2.74276980 -2.64446024 -2.85507955
## HQ(n) -2.70184638 -2.57625454 -2.75959157
## SC(n) -2.44597921 -2.14980927 -2.16256819
## FPE(n) 0.06479726 0.07320573 0.06274515AIC, BIC(SC) best at 1.
grangertest(wnp1diff ~ salt.timeseries, order = 1, data = salt_wnp1diff)
grangertest(salt.timeseries ~ wnp1diff, order = 1, data = salt_wnp1diff)## Granger causality test
##
## Model 1: wnp1diff ~ Lags(wnp1diff, 1:1) + Lags(salt.timeseries, 1:1)
## Model 2: wnp1diff ~ Lags(wnp1diff, 1:1)
## Res.Df Df F Pr(>F)
## 1 17
## 2 18 -1 3.265 0.0885 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Granger causality test
##
## Model 1: salt.timeseries ~ Lags(salt.timeseries, 1:1) + Lags(wnp1diff, 1:1)
## Model 2: salt.timeseries ~ Lags(salt.timeseries, 1:1)
## Res.Df Df F Pr(>F)
## 1 17
## 2 18 -1 3.0062 0.101Salt 領先 WNP 一年