midas_auto_sim: Simulate simple autoregressive MIDAS model

In midasr: Mixed Data Sampling Regression

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)))

Example output

Loading required package: sandwich
Loading required package: optimx
Loading required package: quantreg
Loading required package: SparseM

Attaching package: 'SparseM'

The following object is masked from 'package:base':

    backsolve


MIDAS regression model with "ts" data:
Start = 504, End = 1000
 model: y ~ mls(y, 1, 1) + fmls(x, 4 * 12 - 1, 12, theta_h0) 
(Intercept)           y          x1          x2          x3          x4 
  -0.004993    0.487545   -0.046211    8.165974   -8.315616  -12.032461 

Function optim was used for fitting

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