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- library(urca)
- data("UKpppuip")
- data = UKpppuip[,1:5] # n = 5.
- plot(ts(data))
- est = ca.jo(data, type = 'trace', K = 2, spec = "transitory", ecdet = "const")
- summary(est)
- # Eigenvectors, normalised to first column:
- # (These are the cointegration relations)
- #
- # p1.l1 p2.l1 e12.l1 i1.l1 i2.l1
- # p1.l1 1.0000000 1.0000000 1.000000 1.0000000 1.0000000
- # p2.l1 -0.7416024 -0.9458357 -1.397222 -1.9840046 -1.3797522
- # e12.l1 -1.0345469 -2.1091638 -2.333572 0.6975493 -0.6373520
- # i1.l1 -2.9971374 -22.8591633 10.827331 -0.8242170 2.0956201
- # i2.l1 -2.8613965 10.4695052 -12.817530 1.7284994 0.6975215
- #
- # Weights W:
- # (This is the loading matrix)
- #
- # p1.l1 p2.l1 e12.l1 i1.l1 i2.l1
- # p1.d -0.066164427 -0.003506203 0.002625976 0.0006857297 -0.013125003
- # p2.d -0.083429841 0.007530168 0.008082641 0.0317811111 0.006745236
- # e12.d -0.003804601 0.032942284 0.019624465 -0.1025097997 -0.035013372
- # i1.d 0.004030585 0.012228142 -0.001300394 0.0133270315 -0.022664334
- # i2.d 0.050269357 -0.003983539 0.017150064 0.0452773744 -0.011868206
- est@PI
- # p1.l1 p2.l1 e12.l1 i1.l1 i2.l1
- # p1.d -0.07948393 0.06546368 0.078561030 0.27881486 0.11098623
- # p2.d -0.02929068 -0.02890440 0.069438097 0.15337170 0.27360208
- # e12.d -0.08876102 0.19593347 -0.160529533 -0.51803397 -0.09737199
- # i1.d 0.00562103 -0.00790769 -0.003184998 -0.36416531 0.14038424
- # i2.d 0.09684505 -0.13092988 -0.044477555 0.06389597 -0.33538412
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