Description Usage Arguments Details Value Author(s) Examples

Given the predictor variable and the coefficients simulate MIDAS regression response variable.

1 |

`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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
##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)))
``` |

midasr documentation built on May 29, 2017, 4:12 p.m.

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