Question

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？

Answer 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：
＆＃34;不受限制的VAR =不受限制的VECM＆＃34;和
＆＃34;限制VAR =限制VECM＆＃34;。

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}}。

使用R？

1 个答案: