感谢您阅读我的问题。我正在尝试将VECM用于经济研究,我正在使用Rstudio上的vars和urca包。考虑到我没有固定的时间序列,两者都需要一个区别,两者都是I(1),我需要使用VECM方法,但我无法得到我需要的所有测试。
例如: 首先我加载库
library(vars)
library(urca)
并创建我的模型
data("Canada")
df <- Canada
VARselect(df)
vecm <- urca::ca.jo(df,K = 3)
model <- vec2var(vecm)
问题是,我无法获得“模块”值来证明稳定性,我知道我可以使用roots()函数从“varest”对象中获取此值,例如:
roots(VAR(df,3))
我的问题是: 我如何从我的vec2var对象获得模数,roots()不处理这种对象。我知道Gretl可以做到这一点(用单位圆来证明稳定性),所以从VECM获得这个值是否可行?我怎么能在R?
答案 0 :(得分:1)
从:
开始 data("Canada")
dim(Canada) #84observations x 4 variables
VARselect(Canada) # since in small samples, AIC>BIC; VAR(3) is chosen.
现在,数据集Canada:1980.1 - 2000.4(20年)的范围足够长,可用于建模。这20年的漫长时期肯定包含许多危机和干预措施。因此,必须搜索数据中的结构中断。这是必要的,因为在结构破坏的序列中,SB的存在改变了非平稳性测试的值(从而影响关于序列是否静止的决定)。
由于Narayan-Popp 2010多重结构性突破下的非平稳性测试在统计上与以前相比非常强大(Lee-Strazichic2003,Zivot-Andres1992),而且自从Joyeux 2007(在Rao2007中)证明了这些先前测试的不合逻辑性,以及NP2013已经证明了NP2010统计能力的优越性,必须使用NP2010。由于NP2010的Gauss代码对我来说似乎很难看,我将它转换为R代码,并且在ggplot2
的帮助下,结果更好。
[处理结构性断裂也是必须进行协整检查,因为Osterwald-Lenum1992 CVs忽略了SB,而Johansen-Mosconi-Nielsen2000 CVs关注SBs。]
Canada <- as.data.frame(Canada)
head(Canada)
e prod rw U
1 929.6105 405.3665 386.1361 7.53
2 929.8040 404.6398 388.1358 7.70
...................................
# Assign lexiographic row names for dates of observations
row.names(Canada) <- paste(sort(rep(seq(1980, 2000, 1), 4) ), rep(seq(1, 4, 1), 20), sep = ".")
# Insert lexiographic "date" column to the dataframe. This is necessary for creating intervention dummies.
DCanada <- data.frame(date=row.names(Canada),Canada) # dataset with obs dates in a column
head(DCanada)
date e prod rw U
1980.1 1980.1 929.6105 405.3665 386.1361 7.53
1980.2 1980.2 929.8040 404.6398 388.1358 7.70
对该系列进行Narayan-Popp 2010非平稳性测试:
[H0:&#34;(有2个结构断点)系列是非平稳的&#34 ;;
H1:&#34;(有2个结构断点)系列是静止的&#34;;
&#34;测试统计&gt;临界值&#34; =&GT; &#34;举行H0&#34 ;; &#34;测试统计&lt;临界值&#34; =&GT; &#34;持有H1&#34;]
library(causfinder)
narayanpopp(DCanada[,2]) # for e
narayanpopp(DCanada[,3]) # for prod
narayanpopp(DCanada[,4]) # for rw
narayanpopp(DCanada[,5]) # for U
Narayan-Popp 2010非平稳性测试结果(使用obs #s):
variable t stat lag SB1 SB2 Integration Order
e -4.164 2 37:946.86 43:948.03 I(1)
prod -3.325 1 24:406.77 44:405.43 I(1)
rw -5.087 0 36:436.15 44:446.96 I(0) <trend-stationary>
U -5.737 1 43:8.169 53:11.070 I(0) <stationary pattern> (M2 computationally singular; used M1 model)
(critical values (M2): (1%,5%,10%): -5.576 -4.937 -4.596)
(critical values (M1): (1%,5%,10%): -4.958 -4.316 -3.980
由于在VAR结构中,所有变量都被平等对待,在系统性地确定结构性断裂时继续进行相等处理:
mean(c(37,24,36,43)) # 35; SB1 of system=1988.3
mean(c(43,44,44,53)) # 46; SB2 of system=1990.2
以下是克服"In Ops.factor(left, right) : >= not meaningful for factors"
错误。在某些数据集中,我们需要执行以下操作:
library(readxl)
write.xlsx(Canada, file="data.xlsx", row.names=FALSE) # Take this to the below folder, add "date" column with values 1980.1,....,2000.4
mydata <- read_excel("D://eKitap//RAO 2007 Cointegration for the applied economist 2E//JoyeuxCalisma//Canada//data.xlsx")
# arrange your path accordingly in the above line.
mydata <- as.data.frame(mydata)
library(lubridate); library(zoo)
row.names(mydata) <- as.yearqtr(seq(ymd('1980-01-01'), by = '1 quarter', length.out=(84)))
Dmydata <- mydata # Hold it in a variable
使用2个SB(35:1988.3和46:1990.2)定义干预虚拟矩阵,如下所示:
library(data.table)
DataTable <- data.table(Dmydata, keep.rownames=FALSE)
Dt <- cbind("bir"=1, # intervention dummies matrix
"D2t" = as.numeric(ifelse( DataTable[,c("date"), with=FALSE] >= "1988.3" & DataTable[,c("date"), with=FALSE] <= "1990.1", 1 , 0)),
"D3t" = as.numeric(ifelse( DataTable[,c("date"), with=FALSE] >= "1990.2" & DataTable[,c("date"), with=FALSE] <= "2000.4", 1 , 0)))
伴随干预假人的飞行指示变量:
OnTheFlyIndicator <- cbind(
"I2t" = as.numeric(DataTable[, c("date"), with=FALSE] == "1988.3"),
"I3t" = as.numeric(DataTable[, c("date"), with=FALSE] == "1990.2"))
myTimeTrend <- as.matrix(cbind("TimeTrend" = as.numeric(1:nrow(Dt))))
zyDt <- Dt * as.vector(myTimeTrend) # TimeTrendDavranisDegisimleri
colnames(zyDt) <- paste(colnames(myTimeTrend), colnames(Dt), sep="*")
mydata <- mydata[,-1]
选择VAR顺序:
library(vars)
# Lag order selection with the effects of intervention dummies
VARselect(mydata, lag.max=5, "both", exogen=cbind(zyDt[drop=FALSE], Dt[drop=FALSE], OnTheFlyIndicator)) # Take VAR(3)
Joyeux2007索引技术的Lagger矩阵:
lagmatrix <- function(x, maxlag){
x <- as.matrix(x)
if(is.null(colnames(x))== TRUE){ colnames(x) <- "VarCol0" }
DondurulenDizey <- embed(c(rep(NA,maxlag),x),maxlag+1)
dimnames(DondurulenDizey)[[2]] <- c(colnames(x)[1, drop = FALSE], paste(colnames(x)[1,drop=FALSE],".",1:maxlag,"l", sep = ""))
return(DondurulenDizey)
}
分配VAR延迟和否。子样本:
VARlag <- 3
Subsamples <- 3 # subsamples = no. of str breaks +1
2个结构性断裂的虚拟矩阵:
dummymatrix2SB <- matrix(NA,DataTable[,.N], 10)
dummymatrix2SB <- cbind(myTimeTrend,
lagmatrix(zyDt[,c("TimeTrend*D2t"), drop=FALSE], maxlag=VARlag)[,1+VARlag, drop=FALSE],
lagmatrix(zyDt[,c("TimeTrend*D3t"), drop=FALSE], maxlag=VARlag)[,1+VARlag, drop=FALSE],
lagmatrix(Dt[,c("D2t"), drop=FALSE], maxlag=VARlag)[,1+VARlag, drop=FALSE],
lagmatrix(Dt[,c("D3t"), drop=FALSE], maxlag=VARlag)[,1+VARlag, drop=FALSE],
lagmatrix(OnTheFlyIndicator[,c("I2t"), drop=FALSE], maxlag=VARlag-1),
lagmatrix(OnTheFlyIndicator[,c("I3t"), drop=FALSE], maxlag=VARlag-1))
dummymatrix2SB[is.na(dummymatrix2SB)] <- 0 # replace NAs with 0
dummymatrix2SB # Print dummy matrix for 2 str breaks to make sure all are OK
TimeTrend TimeTrend.D2t.3l TimeTrend.D3t.3l D2t.3l D3t.3l I2t I2t.1l I2t.2l I3t I3t.1l I3t.2l
1 0 0 0 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0 0 0
...........................................
34 0 0 0 0 0 0 0 0 0 0
35 0 0 0 0 1 0 0 0 0 0
36 0 0 0 0 0 1 0 0 0 0
37 0 0 0 0 0 0 1 0 0 0
38 35 0 1 0 0 0 0 0 0 0
39 36 0 1 0 0 0 0 0 0 0
40 37 0 1 0 0 0 0 0 0 0
41 38 0 1 0 0 0 0 0 0 0
42 39 0 1 0 0 0 0 1 0 0
43 40 0 1 0 0 0 0 0 1 0
44 41 0 1 0 0 0 0 0 0 1
45 0 42 0 1 0 0 0 0 0 0
46 0 43 0 1 0 0 0 0 0 0
............................................
83 0 80 0 1 0 0 0 0 0 0
84 0 81 0 1 0 0 0 0 0 0
VAR的稳定性:
维克多,理论上你错了。即使在受限(协整)VAR模型的情况下,也从VAR侧检查稳定性。有关详细信息,请参阅Joyeux2007。此外,双方的估计是相同的:因此,检查无限制VAR的稳定性等于检查无限制VECM的稳定性,反之亦然。它们是平等的数学,它们只是不同的表示形式。
此外,检查受限制的VAR的稳定性等于检查受限制的VECM的稳定性,反之亦然。它们是平等的数学,它们只是不同的表征。但是,由于我们在可行VAR的子空间中进行冲浪,因此您不需要检查受限制的VECM情况。也就是说,如果对应于休息的VeCM的原始unr VAR是稳定的,那么一切都OK。
如果您的系列是协整的,那么即使在这种情况下也可以检查VAR侧的稳定性!如果您想知道是否应该检查受限制的VECM&#34;的稳定性,答案是否定的。你不应该检查。因为,在协整的情况下,您处于可行解的子空间中。也就是说,如果你坚持要检查受限(协整)VECM的稳定性,你仍然可以通过urca :: ca.jo extentions和vars :: vec2var extentions来实现这一点:
print(roots(VAR(mydata, p=3, "both", exogen=cbind(zyDt[drop=FALSE], Dt[drop=FALSE], OnTheFlyIndicator)), modulus=TRUE))
# [1] 0.96132524 0.77923543 0.68689517 0.68689517 0.67578368 0.67578368
[7] 0.59065419 0.59065419 0.55983617 0.55983617 0.33700725 0.09363846
print(max(roots(VAR(mydata, p=3, "both", exogen=cbind(zyDt[drop=FALSE], Dt[drop=FALSE], OnTheFlyIndicator)), modulus=TRUE)))
#0.9613252
(可选)通过OLS-CUSUM检查稳定性:
plot(stability(VAR(mydata, p=3, "both", exogen=cbind(zyDt[drop=FALSE], Dt[drop=FALSE], OnTheFlyIndicator)), type="OLS-CUSUM"))
VAR残差的非自相关测试:
for (j in as.integer(1:5)){
print(paste("VAR's lag no:", j))
print(serial.test(VAR(mydata, p=j, "both", exogen=cbind(zyDt[drop=FALSE], Dt[drop=FALSE], OnTheFlyIndicator)), lags.bg=4, type= c("ES")))
# lags.bg: AR order of VAR residuals
}
VAR残差检验的正常性:
print(normality.test(VAR(mydata, p=3, "both", exogen=cbind(zyDt[drop=FALSE], Dt[drop=FALSE], OnTheFlyIndicator)), multivariate=TRUE))
library(normtest)
for (i in as.integer(1:4)){ # there are 4 variables
print(skewness.norm.test(resid(VAR(mydata, p=3, "both", exogen=cbind(zyDt[drop=FALSE], Dt[drop=FALSE], OnTheFlyIndicator)))[,i]))
print(kurtosis.norm.test(resid(VAR(mydata, p=3, "both", exogen=cbind(zyDt[drop=FALSE], Dt[drop=FALSE], OnTheFlyIndicator)))[,i]))
print(jb.norm.test(resid(VAR(mydata, p=3, "both", exogen=cbind(zyDt[drop=FALSE], Dt[drop=FALSE], OnTheFlyIndicator)))[,i]))
}
VAR残差试验的同性恋:
print(arch.test(VAR(mydata, p=3, "both", exogen=cbind(zyDt[drop=FALSE], Dt[drop=FALSE], OnTheFlyIndicator))), lags.multi=6, multivariate.only=TRUE)
由于系列的整合订单不同,因此无法将它们整合在一起。那说, 假设有一段时间我都是I(1)并使用Johansen-Mosconi-Nielsen 2000 CV进行多重结构断裂的协整检验: (将urca :: cajo扩展到causfinder :: ykJohEsbInc(即,添加处理1 SB和2 SB的功能))
summary(ykJohEsbInc(mydata, type="trace", ecdet="zamanda2yk", K=3, spec="longrun", dumvar=dummymatrix2SB[,c(-1,-2,-3)]))
# summary(ykJohEsbInc(mydata, type="trace", ecdet="zamanda2yk", K=3, spec="transitory", dumvar=dummymatrix2SB[,c(-1,-2,-3)])) gives the exactly same result.
由于系统中有2个SB(1988.3,1990.2),因此有q = 2 + 1 = 3个子样本。
第一SB比率:v1 =(35-1)/ 84 = 0.4047619
第二SB比率:v2 =(46-1)/ 84 = 0.5357143
因此,JMN2000 CV用于2 SB的协整测试:
(以下是TR本地化。可以在Giles网站上找到原始的EN-本地代码)
library(gplots)
# Johansen vd. (2000) nin buldugu, yapisal kirilmalarin varliginda esbutunlesim incelemesinin degistirilmis iz sinamalarinin yanasik p degerleri ve karar degerlerini hesaplama kodu
# Ryan Godwin & David Giles (Dept. of Economics, Univesity of Victoria, Canada), 29.06.2011
# Kullanici asagidaki 4 degeri atamalidir
#======================================
degiskensayisi <- 4 # p
q<- 3 # q: verideki farkli donemlerin sayisi; q=1: 1 donem, hicbir yapisal kirilma yok demek oldugundan v1 ve v2 nin degerleri ihmal edilir
v1<- 0.4047619 # (35-1)/84 # 1.yk anı=34+1=35. Johansen et. al 2000 v1 def'n , v1: SB1 - 1
v2<- 0.5357143 # (46-1)/84 # 2nd SB moment 45+1=46.
#======================================
# iz istatistiginin biri veya her ikisi icin p degerlerinin olmasi istendiginde, sonraki 2 satirin biri veya her ikisini degistir
izZ <- 15.09 # Vz(r) istatistiginin degeri
izK <- 114.7 # Vk(r) istatistiginin degeri
#=========================================
enbuyuk_p_r<- degiskensayisi # "p-r > 10" olmasın; bkz: Johansen vd. (2000)
# "a" ve "b" nin değerleri yapısal kırılmaların sayısına (q-1) bağlıdır
# q=1 iken, hiçbir yapısal kırılma olmadığı bu durumda a=b=0 ata
# q=2 iken, 1 yapısal kırılma olduğu bu durumda a=0 (Johansen vd. 2000 4.Tabloda) ve b=min[V1 , (1-V1)] ata
# q=3 iken, 2 yapısal kırılma olduğu bu durumda a=min[V1, (V2-V1), (1-V2)] ve b=min[geriye kalan iki V ifadesi] ata
a = c(0, 0, min(v1, v2-v1, 1-v2))[q]
b = c(0, min(v1, 1-v1), median(c(v1,v2-v1,1-v2)))[q]
# YanDagOrtLog: yanaşık dağılımın ortalamasının logaritması
# YanDagDegLog: yanaşık dağılımın değişmesinin logaritması
# V(Zamanyönsemsi) veya V(Kesme) sınamalarını yansıtmak üzere adlara z veya k ekle.
# Bkz. Johansen vd. (2000) 4. Tablo.
# Önce Vz(r) sınamasının sonra Vk(r) sınamasının karar değerlerini oluştur
pr<- c(1:enbuyuk_p_r)
YanDagOrtLogZ <- 3.06+0.456*pr+1.47*a+0.993*b-0.0269*pr^2-0.0363*a*pr-0.0195*b*pr-4.21*a^2-2.35*b^2+0.000840*pr^3+6.01*a^3-1.33*a^2*b+2.04*b^3-2.05/pr-0.304*a/pr+1.06*b/pr
+9.35*a^2/pr+3.82*a*b/pr+2.12*b^2/pr-22.8*a^3/pr-7.15*a*b^2/pr-4.95*b^3/pr+0.681/pr^2-0.828*b/pr^2-5.43*a^2/pr^2+13.1*a^3/pr^2+1.5*b^3/pr^2
YanDagDegLogZ <- 3.97+0.314*pr+1.79*a+0.256*b-0.00898*pr^2-0.0688*a*pr-4.08*a^2+4.75*a^3-0.587*b^3-2.47/pr+1.62*a/pr+3.13*b/pr-4.52*a^2/pr-1.21*a*b/pr-5.87*b^2/pr+4.89*b^3/pr
+0.874/pr^2-0.865*b/pr^2
OrtalamaZ<- exp(YanDagOrtLogZ)-(3-q)*pr
DegismeZ<- exp(YanDagDegLogZ)-2*(3-q)*pr
# Sinama istatistiginin yanasik dagilimina yaklasmakta kullanilacak Gama dagiliminin sekil ve olcek degiskelerini elde etmek icin yanasik ortalama ve degismeyi kullanarak
# V0 varsayimi altinda istenen quantilelari elde et:
# quantilelar: olasilik dagiliminin araligini veya bir ornekteki gozlemleri, esit olasiliklara sahip birbirlerine bitisik araliklarla bolen kesim noktalari.
tetaZ <- DegismeZ/OrtalamaZ
kZ <- OrtalamaZ^2/DegismeZ
YanDagOrtLogK<- 2.80+0.501*pr+1.43*a+0.399*b-0.0309*pr^2-0.0600*a*pr-5.72*a^2-1.12*a*b-1.70*b^2+0.000974*pr^3+0.168*a^2*pr+6.34*a^3+1.89*a*b^2+1.85*b^3-2.19/pr-0.438*a/pr
+1.79*b/pr+6.03*a^2/pr+3.08*a*b/pr-1.97*b^2/pr-8.08*a^3/pr-5.79*a*b^2/pr+0.717/pr^2-1.29*b/pr^2-1.52*a^2/pr^2+2.87*b^2/pr^2-2.03*b^3/pr^2
YanDagDegLogK<- 3.78+0.346*pr+0.859*a-0.0106*pr^2-0.0339*a*pr-2.35*a^2+3.95*a^3-0.282*b^3-2.73/pr+0.874*a/pr+2.36*b/pr-2.88*a^2/pr-4.44*b^2/pr+4.31*b^3/pr+1.02/pr^2-0.807*b/pr^2
OrtalamaK <- exp(YanDagOrtLogK)-(3-q)*pr
DegismeK <- exp(YanDagDegLogK)-2*(3-q)*pr
# Sinama istatistiginin yanasik dagilimina yaklasmakta kullanilacak Gama dagiliminin sekil ve olcek degiskelerini elde etmek icin yanasik ortalama ve degismeyi kullanarak
# V0 varsayimi altinda istenen quantilelari elde et:
# quantilelar: olasilik dagiliminin araligini veya bir ornekteki gozlemleri, esit olasiliklara sahip birbirlerine bitisik araliklarla bolen kesim noktalari.
tetaK <- DegismeK/OrtalamaK
kK <- OrtalamaK^2/DegismeK
# (izZ veya izK den biri 0 dan farklı ise) karar değerlerini ve p değerlerini tablolaştır:
windows(6,3.8)
KararDegerleri <- cbind(sapply(c(.90,.95,.99) , function(x) sprintf("%.2f",round(c(qgamma(x, shape=kZ,scale=tetaZ)),2))),
sapply(c(.9,.95,.99) , function(x) sprintf("%.2f",round(c(qgamma(x, shape=kK,scale=tetaK)),2))))
colnames(KararDegerleri) <- rep(c(0.90,0.95,0.99),2)
# rownames(KararDegerleri) <- pr
rownames(KararDegerleri) <- c(sapply((degiskensayisi -1):1, function(i) paste(degiskensayisi - i, " ","(r<=", i, ")",sep="")), paste(degiskensayisi, " ( r=0)", sep=""))
textplot(KararDegerleri, cex=1)
text(.064,.91,"p-r",font=2)
text(.345,1,expression(paste(plain(V)[z],"(r) test")),col=2)
text(.821,1,expression(paste(plain(V)[k],"(r) test")),col=4)
title("Yanasik Karar Degerleri \n (p:duzendeki degisken sayisi; r:esbutunlesim ranki)")
if(izZ!=0){
windows(4,3.8)
pDegerleri <- matrix(sprintf("%.3f",round(1 - pgamma(izZ, shape=kZ, scale = tetaZ),3)))
# rownames(pDegerleri) <- pr
rownames(pDegerleri) <- c(sapply((degiskensayisi -1):1, function(i) paste(degiskensayisi - i, " ","(r<=", i, ")",sep="")), paste(degiskensayisi, " ( r=0)", sep=""))
textplot(pDegerleri,cex=1,show.colnames=F)
text(.69,.96,substitute(paste("Pr(",plain(V)[z],">",nn,")"),list(nn=izZ)),col=2)
text(.45,.96,"p-r",font=2)
title("Yanasik p Degerleri \n (p:duzendeki degisken sayisi; \n r:esbutunlesim ranki)")
}
if(izK!=0){
windows(3,3.8)
pDegerleri <- matrix(sprintf("%.3f",round(1 - pgamma(izK, shape=kK, scale = tetaK),3)))
#rownames(pDegerleri) <- pr
rownames(pDegerleri) <- c(sapply((degiskensayisi -1):1, function(i) paste(degiskensayisi - i, " ","(r<=", i, ")",sep="")), paste(degiskensayisi, " ( r=0)", sep=""))
textplot(pDegerleri,cex=1,show.colnames=F)
text(.78,.96,substitute(paste("Pr(",plain(V)[k],">",nn,")"),list(nn=izK)),col=4)
text(.43,.96,"p-r",font=2)
title("Yanasik p Degerleri \n (p:duzendeki degisken sayisi; \n r:esbutunlesim ranki)")
}
因此,根据JMN2000简历,也没有协整。所以,你对vec2var的使用毫无意义。因为,在协整案例中需要vec2var。再次,假设所有系列都是协整的,以使你快乐(创建需要使用vec2var)并继续最困难的情况(具有多个结构中断的系列的协整);也就是说,我们正在继续&#34;一个雄心勃勃地撒尿的人#34;逻辑。
将vars :: vec2var扩展为causfinder :: vec2var_ykJohEsbInc以处理&#34;多个结构中断下的转换&#34;案件有相关干预假人。上面的JMN2000应用显示协整等级r不在[1,4-1] = [1,3]范围内。尽管假设JMN2000 CV在上面导致r = 1,但为了论证。
因此,要将受限制的VECM转换为受限制的VAR(在多个= 2个结构中断下),请应用:
vec2var_ykJohEsbInc(ykJohEsbInc(mydata, type="trace", ecdet="zamanda2yk", K=3, spec="longrun", dumvar=dummymatrix2SB[,c(-1,-2,-3)]),r=1)
这些结果如下:
Deterministic coefficients (detcoeffs):
e prod rw U
kesme 22.6612871 -0.215892151 32.0610121 -9.26649249 #(const)
zyonsemesi 0.2505164 -0.009900004 0.3503561 -0.10494714 #(trend)
zy*D2t_3 0.2238060 -0.008844454 0.3130007 -0.09375756
zy*D3t_3 -0.1234803 0.004879743 -0.1726916 0.05172878
$deterministic
kesme zyonsemesi zy*D2t_3 zy*D3t_3 D2t.3l D3t.3l
e 22.6612871 0.250516390 0.223806048 -0.123480327 -8.8012612 5.3052074
prod -0.2158922 -0.009900004 -0.008844454 0.004879743 -0.1157137 -0.3396206
rw 32.0610121 0.350356063 0.313000702 -0.172691620 -12.5838458 7.2201840
U -9.2664925 -0.104947142 -0.093757559 0.051728781 3.5836119 -2.2921099
I2t I2t.1l I2t.2l I3t I3t.1l I3t.2l
e -0.2584379 0.08470453 0.2102661 -0.51366831 -1.0110891 -2.08728944
prod 0.3013044 0.25103445 -0.8640467 0.08804425 -0.2362783 -0.05606892
rw -0.5838161 0.28400182 1.2073483 -0.67760848 -2.2650094 -0.70586316
U 0.1305258 0.03559119 0.1476985 0.14614290 0.6847273 1.27469940
$A
$A$A1
e.1g prod.1g rw.1g U.1g
e 1.4817704 0.1771082 -0.2274936 0.2332402
prod -0.1605790 1.1846699 0.0406294 -0.9398689
rw -0.8366449 -0.1910611 0.9774874 0.4667430
U -0.4245817 -0.1498295 0.1226085 0.7557885
$A$A2
e.2g prod.2g rw.2g U.2g
e -0.8441175 -0.04277845 0.01128282 -0.01896916
prod -0.3909984 -0.25960184 -0.20426749 0.79420691
rw 1.4181448 -0.03659278 -0.12240211 -0.06579174
U 0.4299422 0.09070905 0.04935195 -0.12691817
$A$A3
e.3g prod.3g rw.3g U.3g
e 0.40149641+0i -0.07067529+0i -0.008175418-0i 0.2286283+0i
prod 0.55003024+0i 0.07241639+0i 0.172505474-0i 0.1281593+0i
rw -0.52674826+0i 0.31667695+0i -0.168897398-0i 0.2184591+0i
U -0.02176108-0i 0.03245409-0i -0.077959841+0i 0.1855889-0i
所以,现在,检查根:
print(roots(vec2var_ykJohEsbInc(ykJohEsbInc(mydata, type="trace", ecdet="zamanda2yk", K=3, spec="longrun", dumvar=dummymatrix2SB[,c(-1,-2,-3)]),r=1), modulus=TRUE))
结果在&#34; Please provide an object of class 'varest', generated by 'VAR()'.
&#34;因为vars::roots
没有扩展,因为:我们不需要这个扩展!正如我之前所说,即使在VECM受限的情况下,也会从VAR侧检查稳定性。您必须逐行阅读Joyeux2007才能看到这一点。
我将继续提供上述功能的输出(打印屏幕)以进一步澄清。
我也会因为教学原因而将vars::root
扩展到{{1}}。