midasr (version 0.2)

midas.auto.sim: Simulate autoregressive MIDAS model

Description

Given the predictor variable, the weights and autoregressive coefficients calculate MIDAS regression response variable.

Usage

midas.auto.sim(n, theta, alpha, x, eps.sd, n.start = NA)

Arguments

n

sample size

theta

a vector with MIDAS weights for predictor variable

alpha

autoregressive coefficients

x

a high frequency predictor variable

eps.sd

the standard error of the regression disturbances, which are assumed to be independent normal zero mean random variables

n.start

number of observations to omit for the burn.in

Value

• a ts object

Examples

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 <- simplearma.sim(list(ar=0.6),3000*12,1,12)

y <- midas.auto.sim(500,theta0,c(0.5),xx,1,n.start=100)
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)))