midas_lstr_sim | R Documentation |
Simulate LSTR MIDAS regression model
midas_lstr_sim( n, m, theta, intercept, plstr, ar.x, ar.y, rand.gen = rnorm, n.start = NA, ... )
n |
number of observations to simulate. |
m |
integer, frequency ratio |
theta |
vector, restriction coefficients for high frequency variable |
intercept |
vector of length 1, intercept for the model. |
plstr |
vector of length 4, slope for the LSTR term and LSTR parameters |
ar.x |
vector, AR parameters for simulating high frequency variable |
ar.y |
vector, AR parameters for AR part of the model |
rand.gen |
function, a function for generating the regression innovations, default is |
n.start |
integer, length of a 'burn-in' period. If NA, the default, a reasonable value is computed. |
... |
additional parameters to rand.gen |
a list
nnbeta <- function(p, k) nbeta(c(1, p), k) dgp <- midas_lstr_sim(250, m = 12, theta = nnbeta(c(2, 4), 24), intercept = c(1), plstr = c(1.5, 1, log(1), 1), ar.x = 0.9, ar.y = 0.5, n.start = 100 ) z <- cbind(1, mls(dgp$y, 1:2, 1)) colnames(z) <- c("Intercept", "y1", "y2") X <- mls(dgp$x, 0:23, 12) lstr_mod <- midas_lstr_plain(dgp$y, X, z, nnbeta, start_lstr = c(1.5, 1, 1, 1), start_x = c(2, 4), start_z = c(1, 0.5, 0) ) coef(lstr_mod)
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