time series - vars package of R - AIC after restrict -


i fit vector autoregression model on canada data in vars package , restrict based on t-value of 1.64. 

library(vars) data("canada") var.can1 <- var(canada, p = 2, type = "none")  summary(var.can1)  var estimation results: =========================  endogenous variables: e, prod, rw, u  deterministic variables: none  sample size: 82  log likelihood: -184.045  roots of characteristic polynomial:     1 0.9783 0.9113 0.9113 0.7474 0.1613 0.1613 0.1572 call: var(y = canada, p = 2, type = "none")  # aic bic etc. varselect(canada, lag.max = 2, type = "none")$criteria  var.can2 <- restrict(var.can1, method = "ser", thresh = 1.64)  summary(var.can2)  var estimation results: =========================  endogenous variables: e, prod, rw, u  deterministic variables: none  sample size: 82  log likelihood: -191.376  roots of characteristic polynomial:     1 0.9742 0.9272 0.9272 0.7753 0.2105 0.2105 0.005071 call: var(y = canada, p = 2, type = "none")

i want obtain revised information criteria, cannot see way this. know how?

edit 1

so try derive aic unrestricted model:

vars::varselect(canada, lag.max = 2, type = "none")$criteria                   1            2 aic(n) -5.600280680 -6.082112784 hq(n)  -5.411741957 -5.705035337 sc(n)  -5.130676924 -5.142905272 fpe(n)  0.003697972  0.002289041  s <- summary(var.can1) s$covres                 e          prod          rw             u e     0.140560073  0.0056629572 -0.03893668 -0.0798565366 prod  0.005662957  0.4358209615  0.06689687 -0.0005118419 rw   -0.038936678  0.0668968657  0.60125872  0.0309232731 u    -0.079856537 -0.0005118419  0.03092327  0.0899478736

from new introduction multiple time series analysis luetkepohl, helmut 2007, pg 147:

$$aic(m) = ln(det(covres)) + \frac{2mk^2}{t}$$

m lag order, k number of series, t sample size

but get:

-6.451984 + 2*2*4^2/84 = -5.69

which not equal -5.600280680

digging around in code find residual covariance matrix reported in summary not used compute aic.

pretty frustrating, bug.

library(vars) data("canada") var.can1 <- var(canada, p = 2, type = "none")  s <- summary(var.can1)  # variance covariance matrix resid s$covres  # e          prod          rw             u # e     0.140560073  0.0056629572 -0.03893668 -0.0798565366 # prod  0.005662957  0.4358209615  0.06689687 -0.0005118419 # rw   -0.038936678  0.0668968657  0.60125872  0.0309232731 # u    -0.079856537 -0.0005118419  0.03092327  0.0899478736  vars::varselect(canada, lag.max = 2, type = "none")$criteria   # aic defined as: # aicm = ln(det(sigma)) + (2pk^2)/n # p = lag order, k = num series  p <- 2 k <- 4  n <- nrow(canada) - p (aic1 <- log(det(s$covres))  + (2*2*4^2)/n)  # [1] -5.671496 nothing -6.082112784   # residual covariance matrix # reported in summary not used # compute aic.   myresid <- residuals(var.can1) (mysigma <- crossprod(myresid) *  (1/n))  # e          prod          rw             u # e     0.12684690  0.0051104797 -0.03513798 -0.0720656604 # prod  0.00511048  0.3933018507  0.06037034 -0.0004619127 # rw   -0.03513798  0.0603703437  0.54259933  0.0279063671 # u    -0.07206566 -0.0004619127  0.02790637  0.0811724773  log(det(mysigma)) + (2* p * k^2)/n # [1] -6.082113, -6.082112784

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