midas_auto_sim | R Documentation |
Given the predictor variable, the weights and autoregressive coefficients, simulate MIDAS regression response variable.
midas_auto_sim( n, alpha, x, theta, rand_gen = rnorm, innov = rand_gen(n, ...), n_start = NA, ... )
n |
sample size. |
alpha |
autoregressive coefficients. |
x |
a high frequency predictor variable. |
theta |
a vector with MIDAS weights for predictor variable. |
rand_gen |
a function to generate the innovations, default is the normal distribution. |
innov |
an optional time series of innovations. |
n_start |
number of observations to omit for the burn.in. |
... |
additional arguments to function |
a ts
object
Virmantas Kvedaras, Vaidotas Zemlys
theta_h0 <- function(p, dk) { i <- (1:dk-1)/100 pol <- p[3]*i + p[4]*i^2 (p[1] + p[2]*i)*exp(pol) } ##Generate coefficients theta0 <- theta_h0(c(-0.1,10,-10,-10),4*12) ##Generate the predictor variable xx <- ts(arima.sim(model = list(ar = 0.6), 1000 * 12), frequency = 12) y <- midas_auto_sim(500, 0.5, xx, theta0, n_start = 200) x <- window(xx, start=start(y)) midas_r(y ~ mls(y, 1, 1) + fmls(x, 4*12-1, 12, theta_h0), start = list(x = c(-0.1, 10, -10, -10)))
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