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mts's Introduction

Man has gradually become less dependent upon absolute standards of conduct, universally binding ideals. He is held to be so completely free that he needs no standards except his own. Paradoxically, however, this increase of independence has led to a parallel increase of passivity. Shrewd as man's calculations have become as regards his means, his choice of ends, which was formerly correlated with belief in an objective truth, has become witless: the individual, purified of all remnants of mythologies, including the mythology of objective reason, reacts automatically, according to general patterns of adaptation. Economic and social forces take on the character of blind natural powers that man, in order to preserve himself, must dominate by adjusting himself to them. As the end result of the process, we have on the one hand the self, the abstract ego emptied of all substance except its attempt to transform everything in heaven and on earth into means for its preservation, and on the other hand an empty nature degraded to mere material, mere stuff to be dominated, without any other purpose than that of this very domination. For the average man self-preservation has become dependent upon the speed of his reflexes. Reason itself becomes identical with this adjustive faculty.

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mts's Issues

VARpred does not return pred when output=FALSE

The forecast is assigned to pred within the if (output) {...} statement, so that pred=NULL is returned when the argument output=FALSE is set in the call. The assignment of pred should probably be moved outside if (output){...}.

MTS/R/MTS.R

Line 885 in 26ecbaf

pred=px[(orig+1):(orig+h),]

data("mts-examples",package="MTS")
gdp=log(qgdp[,3:5])
zt=diffM(gdp)
m1=VAR(zt,p=2, output=FALSE)
vp=VARpred(m1,4, output=FALSE)
vp
$pred
NULL

$se.err
            [,1]        [,2]        [,3]
[1,] 0.005314548 0.005399830 0.005974661
[2,] 0.005803884 0.007165152 0.007076819
[3,] 0.006201846 0.007672211 0.007344704
[4,] 0.006484131 0.007784789 0.007442406

$rmse
            [,1]        [,2]        [,3]
[1,] 0.005461328 0.005548966 0.006139673
[2,] 0.038966588 0.078130775 0.063052185
[3,] 0.036637288 0.045955913 0.033289797
[4,] 0.031926258 0.023140137 0.021204768

VARMAcpp example not working

Hi Professor Tsay,

I am currently experimenting with your MTS package while I wait for your book to arrive, but I cannot get the VARMACpp example to work (p63 and 64 of the MTS.pdf file).

I note that the sample code is only for the VARMA fitting in R, and not the C++ version.

m3=VARMACpp(zt, p = 1, q = 1, include.mean = FALSE)
    Number of parameters:  8 
    initial estimates:  0.2443 0.2619 -0.6107 1.0816 -0.0072 0.0888 0.0628 -0.0545 
    Par. lower-bounds:  0.0092 0.1691 -0.8441 0.9895 -0.2695 -0.062 -0.1975 -0.2042 
    Par. upper-bounds:  0.4794 0.3547 -0.3773 1.1736 0.2551 0.2397 0.3232 0.0953 

    Error in do.call(rbind, ListResiduals) : second argument must be a list

Is this an error I can currently fix, or is this coed not yet operational?

Best,
Simon Hayward

Error in Kronfit

There is an error in the Kronfit() function when the Kronecker indices do not correspond to the "generic neighbourhood", e.g., when they are (1,2,1,1):

param_ar <- matrix(
  c(0.7, -0.3, -1, -0.1,
    0.1,  0.9, -0.4,  0,
    0, 0, 0.8, 0,
    0.1, -0.4, -0.1, 0.9), byrow = TRUE, nrow = 4)
param_ma <- matrix(
  c(0.1, 0.2, -1.8, 0.1,
     0.2, 0.2, -0.1, 0.1,
     -0.1, -0.1, 0.9, -0.1,
     -0.1, -0.1, -0.2, -0.2), byrow = TRUE, nrow = 4)
param_sigma <- diag(rep(1, 4))

mod_sim <- VARMAsim(300, arlags = 1, malags = 1, phi = param_ar, theta = param_ma, sigma = param_sigma)
Kronfit(mod_sim$series, kidx = c(1,2,1,1))

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