Research Methods in Finance_Homework Individual
Chut
2024-08-09
Libraries and Dataset
library(readxl)
library(car)## Loading required package: carDatalibrary(fUnitRoots)## Warning: package 'fUnitRoots' was built under R version 4.4.1library(urca)##
## Attaching package: 'urca'## The following objects are masked from 'package:fUnitRoots':
##
## punitroot, qunitroot, unitrootTablelibrary(vars)## Warning: package 'vars' was built under R version 4.4.1## Loading required package: MASS## Loading required package: strucchange## Warning: package 'strucchange' was built under R version 4.4.1## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric## Loading required package: sandwich## Loading required package: lmtestlibrary(AER)## Warning: package 'AER' was built under R version 4.4.1## Loading required package: survivallibrary(lmtest)load("D:/IU/MA/3/Summer/R/Dataset/fred.RData")
load("D:/IU/MA/3/Summer/R/Dataset/macro.RData")
load("D:/IU/MA/3/Summer/R/Dataset/SandPhedge.RData")
load("D:/IU/MA/3/Summer/R/Dataset/UKHP.RData")Simultaneouls equations
inf_iv = ivreg(inflation ~ rsandp + dprod + dcredit + dmoney |
dcredit + dprod + rterm + dspread + dmoney, data = macro)
summary(inf_iv)##
## Call:
## ivreg(formula = inflation ~ rsandp + dprod + dcredit + dmoney |
## dcredit + dprod + rterm + dspread + dmoney, data = macro)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.40799 -0.33680 -0.01575 0.31978 2.61026
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.212932 0.037096 5.740 1.94e-08 ***
## rsandp 0.103674 0.033564 3.089 0.00216 **
## dprod 0.030860 0.050326 0.613 0.54012
## dcredit -0.005182 0.001917 -2.704 0.00717 **
## dmoney -0.002787 0.001062 -2.624 0.00905 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5428 on 379 degrees of freedom
## Multiple R-Squared: -1.827, Adjusted R-squared: -1.857
## Wald test: 5.375 on 4 and 379 DF, p-value: 0.0003213ret_iv = ivreg(rsandp ~ inflation + dprod + dspread + rterm |
dcredit + dprod + rterm + dspread + dmoney, data = macro)
summary(ret_iv)##
## Call:
## ivreg(formula = rsandp ~ inflation + dprod + dspread + rterm |
## dcredit + dprod + rterm + dspread + dmoney, data = macro)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.0829 -2.1293 0.2958 2.6311 11.9850
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.4802 0.6468 2.289 0.02266 *
## inflation -3.9592 2.8354 -1.396 0.16342
## dprod -0.1591 0.4045 -0.393 0.69437
## dspread -11.7333 3.7799 -3.104 0.00205 **
## rterm -0.3259 1.0564 -0.308 0.75791
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.391 on 379 degrees of freedom
## Multiple R-Squared: -0.01033, Adjusted R-squared: -0.02099
## Wald test: 3.612 on 4 and 379 DF, p-value: 0.006634inf_ols = lm(inflation ~ dprod + dspread + rterm + dcredit + dmoney, data = macro)
ret_ols = lm(rsandp ~ dprod + dspread + rterm + dcredit + dmoney , data = macro )
macro$inffit = c(NA, inf_ols$fitted.values )
macro$retfit = c(NA, ret_ols$fitted.values )summary(lm(inflation ~ dprod + dcredit + dmoney + rsandp + retfit , data = macro))##
## Call:
## lm(formula = inflation ~ dprod + dcredit + dmoney + rsandp +
## retfit, data = macro)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.68510 -0.15962 -0.01702 0.15680 0.99725
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2129317 0.0202279 10.527 < 2e-16 ***
## dprod 0.0308597 0.0274420 1.125 0.261
## dcredit -0.0051825 0.0010452 -4.958 1.08e-06 ***
## dmoney -0.0027873 0.0005793 -4.811 2.17e-06 ***
## rsandp -0.0025990 0.0035490 -0.732 0.464
## retfit 0.1062734 0.0186425 5.701 2.41e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.296 on 378 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1616, Adjusted R-squared: 0.1505
## F-statistic: 14.57 on 5 and 378 DF, p-value: 4.567e-13summary(lm(rsandp ~ dprod + dspread + rterm + inflation + inffit , data = macro))##
## Call:
## lm(formula = rsandp ~ dprod + dspread + rterm + inflation + inffit,
## data = macro)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.8624 -2.3098 0.4615 2.6055 11.3393
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.4802 0.6313 2.345 0.01957 *
## dprod -0.1591 0.3948 -0.403 0.68728
## dspread -11.7333 3.6895 -3.180 0.00159 **
## rterm -0.3259 1.0312 -0.316 0.75217
## inflation -0.5708 0.7617 -0.749 0.45410
## inffit -3.3885 2.8705 -1.180 0.23856
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.286 on 378 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.03994, Adjusted R-squared: 0.02724
## F-statistic: 3.145 on 5 and 378 DF, p-value: 0.008537Testing for Unit Roots
adfTest(UKHP$hp, lags = 10, type = "c")##
## Title:
## Augmented Dickey-Fuller Test
##
## Test Results:
## PARAMETER:
## Lag Order: 10
## STATISTIC:
## Dickey-Fuller: -0.3948
## P VALUE:
## 0.9039
##
## Description:
## Thu Aug 22 23:14:30 2024 by user: LENOVOadfTest(UKHP$dhp, lags = 10, type = "c")##
## Title:
## Augmented Dickey-Fuller Test
##
## Test Results:
## PARAMETER:
## Lag Order: 10
## STATISTIC:
## Dickey-Fuller: -3.1191
## P VALUE:
## 0.02672
##
## Description:
## Thu Aug 22 23:14:30 2024 by user: LENOVOadfgls = urersTest(UKHP$hp, type ="DF-GLS", model = "trend", lag.max = 10)
#doplot = F
adfgls@test$test@teststat## [1] -1.681065adfgls@test$test@cval## 1pct 5pct 10pct
## critical values -3.48 -2.89 -2.57Cointegration
Sandphedge.RData
SandPhedge$rspot = c(NA,100*diff(log(SandPhedge$Spot)))
SandPhedge$rfutures = c(NA,100*diff(log(SandPhedge$Futures)))
SandPhedge$lspot = log(SandPhedge$Spot)
SandPhedge$lfutures = log(SandPhedge$Futures)adfTest(SandPhedge$lspot, lags = 10, type = "c")##
## Title:
## Augmented Dickey-Fuller Test
##
## Test Results:
## PARAMETER:
## Lag Order: 10
## STATISTIC:
## Dickey-Fuller: -0.5569
## P VALUE:
## 0.8491
##
## Description:
## Thu Aug 22 23:14:30 2024 by user: LENOVOadfTest(SandPhedge$lfutures, lags = 10, type = "c")##
## Title:
## Augmented Dickey-Fuller Test
##
## Test Results:
## PARAMETER:
## Lag Order: 10
## STATISTIC:
## Dickey-Fuller: -0.5378
## P VALUE:
## 0.8562
##
## Description:
## Thu Aug 22 23:14:30 2024 by user: LENOVOadfTest(diff(SandPhedge$lspot), lags = 10, type = "c")## Warning in adfTest(diff(SandPhedge$lspot), lags = 10, type = "c"): p-value
## smaller than printed p-value##
## Title:
## Augmented Dickey-Fuller Test
##
## Test Results:
## PARAMETER:
## Lag Order: 10
## STATISTIC:
## Dickey-Fuller: -4.1973
## P VALUE:
## 0.01
##
## Description:
## Thu Aug 22 23:14:30 2024 by user: LENOVOadfTest(diff(SandPhedge$lfutures), lags = 10, type = "c")## Warning in adfTest(diff(SandPhedge$lfutures), lags = 10, type = "c"): p-value
## smaller than printed p-value##
## Title:
## Augmented Dickey-Fuller Test
##
## Test Results:
## PARAMETER:
## Lag Order: 10
## STATISTIC:
## Dickey-Fuller: -4.1957
## P VALUE:
## 0.01
##
## Description:
## Thu Aug 22 23:14:30 2024 by user: LENOVOlog_lm = lm(lspot ~ lfutures, data = SandPhedge)par(lwd=2,cex.axis = 2)
plot(SandPhedge$Date,SandPhedge$lspot,type = "l",xlab = "",ylab = "",col="red")
lines(SandPhedge$Date,log_lm$fitted.values)
par(new=T)
plot(SandPhedge$Date,log_lm$residuals,col="blue",axes=F,type="l",xlab = "",ylab = "")
axis(side=4, at = pretty(range(log_lm$residuals)))
legend("bottomleft", legend=c("Actual", "Fitted"),col=c("black","red"),lty= 1)
legend("bottomright", legend=c("Resid"),col=c("blue"),lty= 1)
urersTest(log_lm$residuals, type = "DF-GLS", model = "trend", lag.max = 12)@test$test@teststat## [1] -1.458417urersTest(log_lm$residuals, type = "DF-GLS", model = "trend", lag.max = 12)@test$test@cval
## 1pct 5pct 10pct
## critical values -3.48 -2.89 -2.57summary(lm(SandPhedge$rspot[-1] ~ SandPhedge$rfutures[-1] + log_lm$residuals[-247]))##
## Call:
## lm(formula = SandPhedge$rspot[-1] ~ SandPhedge$rfutures[-1] +
## log_lm$residuals[-247])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.44248 -0.06592 0.04480 0.17764 1.58732
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.009331 0.025210 0.370 0.712
## SandPhedge$rfutures[-1] 0.984771 0.005781 170.344 <2e-16 ***
## log_lm$residuals[-247] -55.060243 5.784972 -9.518 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3936 on 243 degrees of freedom
## Multiple R-squared: 0.9918, Adjusted R-squared: 0.9917
## F-statistic: 1.473e+04 on 2 and 243 DF, p-value: < 2.2e-16Johansen Test fred.Data
pca = prcomp(fred[c("GS3M","GS6M","GS1","GS3","GS5","GS10")],scale. = T,retx = T)
plot(fred$Date , fred$GS3M , type ="l", xlab ="", ylab ="")
lines(fred$Date , fred$GS6M , col = "red")
lines(fred$Date , fred$GS1, col = "blue")
lines(fred$Date , fred$GS3, col = "brown")
lines(fred$Date , fred$GS5, col = "orange")
lines(fred$Date , fred$GS10, col ="darkgreen")
adfTest(fred$GS3M, lags = 10, type = "c")##
## Title:
## Augmented Dickey-Fuller Test
##
## Test Results:
## PARAMETER:
## Lag Order: 10
## STATISTIC:
## Dickey-Fuller: -1.7014
## P VALUE:
## 0.4243
##
## Description:
## Thu Aug 22 23:14:31 2024 by user: LENOVOadfTest(diff(fred$GS3M), lags = 10, type = "c")## Warning in adfTest(diff(fred$GS3M), lags = 10, type = "c"): p-value smaller
## than printed p-value##
## Title:
## Augmented Dickey-Fuller Test
##
## Test Results:
## PARAMETER:
## Lag Order: 10
## STATISTIC:
## Dickey-Fuller: -5.936
## P VALUE:
## 0.01
##
## Description:
## Thu Aug 22 23:14:31 2024 by user: LENOVOadfTest(fred$GS6M, lags = 10, type = "c")##
## Title:
## Augmented Dickey-Fuller Test
##
## Test Results:
## PARAMETER:
## Lag Order: 10
## STATISTIC:
## Dickey-Fuller: -1.7147
## P VALUE:
## 0.4193
##
## Description:
## Thu Aug 22 23:14:31 2024 by user: LENOVOadfTest(diff(fred$GS6M), lags = 10, type = "c")## Warning in adfTest(diff(fred$GS6M), lags = 10, type = "c"): p-value smaller
## than printed p-value##
## Title:
## Augmented Dickey-Fuller Test
##
## Test Results:
## PARAMETER:
## Lag Order: 10
## STATISTIC:
## Dickey-Fuller: -6.0334
## P VALUE:
## 0.01
##
## Description:
## Thu Aug 22 23:14:31 2024 by user: LENOVOadfTest(fred$GS1, lags = 10, type = "c")##
## Title:
## Augmented Dickey-Fuller Test
##
## Test Results:
## PARAMETER:
## Lag Order: 10
## STATISTIC:
## Dickey-Fuller: -1.7484
## P VALUE:
## 0.4068
##
## Description:
## Thu Aug 22 23:14:31 2024 by user: LENOVOadfTest(diff(fred$GS1), lags = 10, type = "c")## Warning in adfTest(diff(fred$GS1), lags = 10, type = "c"): p-value smaller than
## printed p-value##
## Title:
## Augmented Dickey-Fuller Test
##
## Test Results:
## PARAMETER:
## Lag Order: 10
## STATISTIC:
## Dickey-Fuller: -5.9981
## P VALUE:
## 0.01
##
## Description:
## Thu Aug 22 23:14:31 2024 by user: LENOVOadfTest(fred$GS3, lags = 10, type = "c")##
## Title:
## Augmented Dickey-Fuller Test
##
## Test Results:
## PARAMETER:
## Lag Order: 10
## STATISTIC:
## Dickey-Fuller: -1.6986
## P VALUE:
## 0.4254
##
## Description:
## Thu Aug 22 23:14:31 2024 by user: LENOVOadfTest(diff(fred$GS3), lags = 10, type = "c")## Warning in adfTest(diff(fred$GS3), lags = 10, type = "c"): p-value smaller than
## printed p-value##
## Title:
## Augmented Dickey-Fuller Test
##
## Test Results:
## PARAMETER:
## Lag Order: 10
## STATISTIC:
## Dickey-Fuller: -6.1679
## P VALUE:
## 0.01
##
## Description:
## Thu Aug 22 23:14:31 2024 by user: LENOVOadfTest(fred$GS5, lags = 10, type = "c")##
## Title:
## Augmented Dickey-Fuller Test
##
## Test Results:
## PARAMETER:
## Lag Order: 10
## STATISTIC:
## Dickey-Fuller: -1.7323
## P VALUE:
## 0.4128
##
## Description:
## Thu Aug 22 23:14:31 2024 by user: LENOVOadfTest(diff(fred$GS5), lags = 10, type = "c")## Warning in adfTest(diff(fred$GS5), lags = 10, type = "c"): p-value smaller than
## printed p-value##
## Title:
## Augmented Dickey-Fuller Test
##
## Test Results:
## PARAMETER:
## Lag Order: 10
## STATISTIC:
## Dickey-Fuller: -6.3315
## P VALUE:
## 0.01
##
## Description:
## Thu Aug 22 23:14:31 2024 by user: LENOVOadfTest(fred$GS10, lags = 10, type = "c")##
## Title:
## Augmented Dickey-Fuller Test
##
## Test Results:
## PARAMETER:
## Lag Order: 10
## STATISTIC:
## Dickey-Fuller: -1.6556
## P VALUE:
## 0.4414
##
## Description:
## Thu Aug 22 23:14:31 2024 by user: LENOVOadfTest(diff(fred$GS10), lags = 10, type = "c")## Warning in adfTest(diff(fred$GS10), lags = 10, type = "c"): p-value smaller
## than printed p-value##
## Title:
## Augmented Dickey-Fuller Test
##
## Test Results:
## PARAMETER:
## Lag Order: 10
## STATISTIC:
## Dickey-Fuller: -6.5163
## P VALUE:
## 0.01
##
## Description:
## Thu Aug 22 23:14:31 2024 by user: LENOVOVECM
VARselect(fred[c("GS3M", "GS6M", "GS1", "GS3", "GS5", "GS10")], lag.max = 12)## $selection
## AIC(n) HQ(n) SC(n) FPE(n)
## 2 2 2 2
##
## $criteria
## 1 2 3 4 5
## AIC(n) -3.048588e+01 -3.088381e+01 -3.087091e+01 -3.087216e+01 -3.082098e+01
## HQ(n) -3.032768e+01 -3.059002e+01 -3.044151e+01 -3.030717e+01 -3.012039e+01
## SC(n) -3.008544e+01 -3.014014e+01 -2.978399e+01 -2.944201e+01 -2.904759e+01
## FPE(n) 5.756508e-14 3.867052e-14 3.918238e-14 3.915114e-14 4.123784e-14
## 6 7 8 9 10
## AIC(n) -3.083823e+01 -3.081106e+01 -3.081602e+01 -3.074647e+01 -3.070735e+01
## HQ(n) -3.000205e+01 -2.983928e+01 -2.970863e+01 -2.950349e+01 -2.932877e+01
## SC(n) -2.872161e+01 -2.835121e+01 -2.801292e+01 -2.760014e+01 -2.721778e+01
## FPE(n) 4.057676e-14 4.175760e-14 4.163494e-14 4.474874e-14 4.668409e-14
## 11 12
## AIC(n) -3.065424e+01 -3.058976e+01
## HQ(n) -2.914007e+01 -2.893998e+01
## SC(n) -2.682144e+01 -2.641372e+01
## FPE(n) 4.942441e-14 5.296642e-14summary(ca.jo(fred[c("GS3M", "GS6M", "GS1", "GS3", "GS5", "GS10")], K = 2, ecdet = "const", type ="trace"))##
## ######################
## # Johansen-Procedure #
## ######################
##
## Test type: trace statistic , without linear trend and constant in cointegration
##
## Eigenvalues (lambda):
## [1] 1.933608e-01 1.441940e-01 1.112046e-01 4.191212e-02 1.951328e-02
## [6] 1.837159e-02 -6.011223e-17
##
## Values of teststatistic and critical values of test:
##
## test 10pct 5pct 1pct
## r <= 5 | 8.07 7.52 9.24 12.97
## r <= 4 | 16.64 17.85 19.96 24.60
## r <= 3 | 35.26 32.00 34.91 41.07
## r <= 2 | 86.54 49.65 53.12 60.16
## r <= 1 | 154.28 71.86 76.07 84.45
## r = 0 | 247.75 97.18 102.14 111.01
##
## Eigenvectors, normalised to first column:
## (These are the cointegration relations)
##
## GS3M.l2 GS6M.l2 GS1.l2 GS3.l2 GS5.l2 GS10.l2
## GS3M.l2 1.0000000 1.0000000 1.0000000 1.00000 1.0000000 1.0000000
## GS6M.l2 -1.8361728 -1.7240301 -7.8260887 -45.44358 -2.2414315 -2.1083618
## GS1.l2 0.6584909 1.5591332 10.3520558 64.84908 1.0119005 0.4455494
## GS3.l2 0.7935402 -2.0124537 -9.3559469 36.99190 0.9399850 0.6478099
## GS5.l2 -0.7572572 1.4945981 8.5581204 -111.48938 -1.0083630 -7.7888536
## GS10.l2 0.1275615 -0.2493571 -2.7878414 55.56632 0.4129490 8.1180175
## constant 0.1581499 -0.1180595 0.5424579 -9.32844 -0.5161628 -5.7936204
## constant
## GS3M.l2 1.0000000
## GS6M.l2 0.7974764
## GS1.l2 -2.7026864
## GS3.l2 3.1335814
## GS5.l2 -11.5201333
## GS10.l2 11.7683970
## constant -28.8343237
##
## Weights W:
## (This is the loading matrix)
##
## GS3M.l2 GS6M.l2 GS1.l2 GS3.l2 GS5.l2
## GS3M.d 0.4401418 -0.27515704 0.03139674 0.0004933753 -0.04251249
## GS6M.d 0.6134722 -0.12926923 0.01290303 0.0000976568 -0.03596756
## GS1.d 0.5562986 -0.04534895 -0.01178610 -0.0001540930 -0.04416737
## GS3.d 0.4869795 0.16241326 -0.01436364 0.0006761659 -0.05816293
## GS5.d 0.5302487 0.18077756 -0.02830696 0.0011619122 -0.05582945
## GS10.d 0.5536302 0.18370135 -0.02705497 0.0006235189 -0.05394235
## GS10.l2 constant
## GS3M.d 0.0042441020 1.645681e-16
## GS6M.d 0.0053450249 3.471301e-16
## GS1.d 0.0059033018 3.844782e-16
## GS3.d 0.0046972595 6.173475e-16
## GS5.d 0.0025825107 7.030048e-16
## GS10.d -0.0001778005 5.828148e-16vecm = ca.jo(fred[c("GS3M", "GS6M", "GS1", "GS3", "GS5", "GS10")], K = 2, ecdet = "const", type ="trace")
cajorls(vecm,r=3)## $rlm
##
## Call:
## lm(formula = substitute(form1), data = data.mat)
##
## Coefficients:
## GS3M.d GS6M.d GS1.d GS3.d GS5.d
## ect1 0.1963815 0.4971060 0.4991636 0.6350292 0.6827193
## ect2 -0.5795111 -1.0045571 -0.8510384 -1.0617728 -1.0637614
## ect3 0.1858437 0.3359907 0.1736022 0.4252023 0.3379851
## GS3M.dl1 0.8046578 1.0052603 0.7937785 0.7297938 0.7851838
## GS6M.dl1 -1.3002259 -1.7480493 -1.3158642 -1.4676407 -1.4515766
## GS1.dl1 0.7261699 1.0482154 0.7538492 0.9610662 0.7470956
## GS3.dl1 0.3414507 -0.0154097 -0.0679247 -0.2322351 -0.0695104
## GS5.dl1 -0.1705721 0.2690776 0.4427480 0.5318636 0.3428756
## GS10.dl1 -0.0816765 -0.1924225 -0.2043656 -0.1335948 -0.0008461
## GS10.d
## ect1 0.7102766
## ect2 -1.1215328
## ect3 0.3709008
## GS3M.dl1 0.7296653
## GS6M.dl1 -1.3219279
## GS1.dl1 0.6686458
## GS3.dl1 -0.3048205
## GS5.dl1 0.4572949
## GS10.dl1 0.0573718
##
##
## $beta
## ect1 ect2 ect3
## GS3M.l2 1.000000e+00 -2.220446e-16 -3.122502e-17
## GS6M.l2 0.000000e+00 1.000000e+00 5.551115e-17
## GS1.l2 -4.440892e-16 -2.220446e-16 1.000000e+00
## GS3.l2 -2.499094e+00 -2.786098e+00 -2.768639e+00
## GS5.l2 1.573232e+00 2.073284e+00 2.242124e+00
## GS10.l2 9.893932e-02 -1.585894e-01 -3.987532e-01
## constant -5.346689e-01 -4.664695e-01 -2.485984e-01cajorls(vecm,r=3)$rlm##
## Call:
## lm(formula = substitute(form1), data = data.mat)
##
## Coefficients:
## GS3M.d GS6M.d GS1.d GS3.d GS5.d
## ect1 0.1963815 0.4971060 0.4991636 0.6350292 0.6827193
## ect2 -0.5795111 -1.0045571 -0.8510384 -1.0617728 -1.0637614
## ect3 0.1858437 0.3359907 0.1736022 0.4252023 0.3379851
## GS3M.dl1 0.8046578 1.0052603 0.7937785 0.7297938 0.7851838
## GS6M.dl1 -1.3002259 -1.7480493 -1.3158642 -1.4676407 -1.4515766
## GS1.dl1 0.7261699 1.0482154 0.7538492 0.9610662 0.7470956
## GS3.dl1 0.3414507 -0.0154097 -0.0679247 -0.2322351 -0.0695104
## GS5.dl1 -0.1705721 0.2690776 0.4427480 0.5318636 0.3428756
## GS10.dl1 -0.0816765 -0.1924225 -0.2043656 -0.1335948 -0.0008461
## GS10.d
## ect1 0.7102766
## ect2 -1.1215328
## ect3 0.3709008
## GS3M.dl1 0.7296653
## GS6M.dl1 -1.3219279
## GS1.dl1 0.6686458
## GS3.dl1 -0.3048205
## GS5.dl1 0.4572949
## GS10.dl1 0.0573718cajorls(vecm,r=3)$beta## ect1 ect2 ect3
## GS3M.l2 1.000000e+00 -2.220446e-16 -3.122502e-17
## GS6M.l2 0.000000e+00 1.000000e+00 5.551115e-17
## GS1.l2 -4.440892e-16 -2.220446e-16 1.000000e+00
## GS3.l2 -2.499094e+00 -2.786098e+00 -2.768639e+00
## GS5.l2 1.573232e+00 2.073284e+00 2.242124e+00
## GS10.l2 9.893932e-02 -1.585894e-01 -3.987532e-01
## constant -5.346689e-01 -4.664695e-01 -2.485984e-01H = matrix(c(0,0,1,0,0,0,0), c(7,1))
A = matrix(c(0,0,1,0,0,0,0), c(7,1))summary(blrtest(vecm,H=H, r=1))##
## ######################
## # Johansen-Procedure #
## ######################
##
## Estimation and testing under linear restrictions on beta
##
## The VECM has been estimated subject to:
## beta=H*phi and/or alpha=A*psi
##
## [,1]
## [1,] 0
## [2,] 0
## [3,] 1
## [4,] 0
## [5,] 0
## [6,] 0
## [7,] 0
##
## Eigenvalues of restricted VAR (lambda):
## [1] 0.0297
##
## The value of the likelihood ratio test statistic:
## 80.36 distributed as chi square with 6 df.
## The p-value of the test statistic is: 0
##
## Eigenvectors, normalised to first column
## of the restricted VAR:
##
## [,1]
## [1,] NaN
## [2,] NaN
## [3,] Inf
## [4,] NaN
## [5,] NaN
## [6,] NaN
## [7,] NaN
##
## Weights W of the restricted VAR:
##
## [,1]
## GS3M.d NaN
## GS6M.d NaN
## GS1.d NaN
## GS3.d NaN
## GS5.d NaN
## GS10.d NaN