midas_lstr_sim: Simulate LSTR MIDAS regression model

In midasr: Mixed Data Sampling Regression

Description

Simulate LSTR MIDAS regression model

Usage

midas_lstr_sim(
  n,
  m,
  theta,
  intercept,
  plstr,
  ar.x,
  ar.y,
  rand.gen = rnorm,
  n.start = NA,
  ...
)

Arguments

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 rnorm
n.start	integer, length of a 'burn-in' period. If NA, the default, a reasonable value is computed.
...	additional parameters to rand.gen

Value

a list

Examples

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)

midasr documentation built on Feb. 23, 2021, 5:11 p.m.