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Last data update: 2014.03.03

R: Simulate simple autoregressive MIDAS model

midas_auto_sim

R Documentation

Simulate simple autoregressive MIDAS model

Description

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

Usage

midas_auto_sim(n, alpha, x, theta, rand_gen = rnorm, innov = rand_gen(n,
  ...), n_start = NA, ...)

Arguments

`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 `rand_gen`.

Value

a ts object

Author(s)

Virmantas Kvedaras, Vaidotas Zemlys

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 <- 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)))