midas_sim | R Documentation |
Given the predictor variable and the coefficients simulate MIDAS regression response variable.
midas_sim(n, x, theta, rand_gen = rnorm, innov = rand_gen(n, ...), ...)
n |
The sample size |
x |
a |
theta |
a vector with MIDAS regression coefficients |
rand_gen |
the function which generates the sample of innovations, the default is |
innov |
the vector with innovations, the default is NULL, i.e. innovations are generated using argument |
... |
additional arguments to |
MIDAS regression with one predictor variable has the following form:
y_t=∑_{j=0}^{h}θ_jx_{tm-j}+u_t,
where m is the frequency ratio and h is the number of high frequency lags included in the regression.
MIDAS regression involves times series with different frequencies. In R
the frequency property is set when creating time series objects
ts
. Hence the frequency ratio m which figures in MIDAS regression is calculated from frequency property of time series objects supplied.
a ts
object
Virmantas Kvedaras, Vaidotas Zemlys
##The parameter function 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) ##Plot the coefficients plot(theta0) ##Generate the predictor variable, leave 4 low frequency lags of data for burn-in. xx <- ts(arima.sim(model = list(ar = 0.6), 600 * 12), frequency = 12) ##Simulate the response variable y <- midas_sim(500, xx, theta0) 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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