midas_mmm_sim: Simulate MMM MIDAS regression model

In mpiktas/midasr: Mixed Data Sampling Regression

Simulate MMM MIDAS regression model

Description

Simulate MMM MIDAS regression model

Usage

midas_mmm_sim(
  n,
  m,
  theta,
  intercept,
  pmmm,
  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.
pmmm	vector of length 2, slope for the MMM term and MMM parameter
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_mmm_sim(250,
  m = 12, theta = nnbeta(c(2, 4), 24),
  intercept = c(1), pmmm = c(1.5, 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)

mmm_mod <- midas_mmm_plain(dgp$y, X, z, nnbeta,
  start_mmm = c(1.5, 1),
  start_x = c(2, 4), start_z = c(1, 0.5, 0)
)

coef(mmm_mod)

mpiktas/midasr documentation built on Aug. 24, 2022, 2:32 p.m.