arima.sim
Simulate a Univariate ARIMA Series

### Description

Simulates a univariate ARIMA time series. By default the innovations are Gaussian.

### Usage

    arima.sim(model, n, rand.gen = rnorm, innov = rand.gen(n, ...),
        n.start = NA, start.innov = rand.gen(n.start, ...), ...)

### Arguments

model | a list specifying an ARIMA model. The components ar and ma, if present, should be numeric vectors giving the autoregression and moving average parameters for the model. If you also want a difference parameter, supply the order component, a three-long integer vector giving the length of the ar component, the number of differences, and the length of the ma component, respecively. (If supplied, the first and last elements of order must match the lengths of the ar and ma components.)
n | the length of the series to be simulated.
innov | a univariate time series or vector of innovations to produce the series. If not provided, innov is generated using rand.gen. Missing values (NAs) are not allowed. If provided, its length must match the n argument.
n.start | the number of start-up innovations. The start-up innovations are generated by rand.gen if start.innov is not provided. n.start must be as long as ar + ma.
start.innov | a univariate time series or vector of innovations to be used as start up values. Missing values (NAs) are not allowed. These are transformed by the Choleski "square root" of the correlation matrix corresponding to the model so the simulated process begins with something close to its stationary state if you supply iid (independent and identically distributed) innovations.
rand.gen | a function that is called to generate the innovations. Usually, rand.gen is a random number generator.
... | additional arguments to pass to rand.gen

Value
returns a univariate time series, the simulated series, with start==1 and frequency==1.
References
Jones, R. H. (1980). Maximum likelihood fitting of ARIMA models to time series with missing observations. Technometrics 22, 389-395.
See Also
filter, ts.
Examples

    # Simulate an ARIMA(1,0,2) process
    x <- arima.sim(n=100, model=list(ar=.5, ma=c(-.6, .6)))
    # Generate the impulse response for the same model
    ix <- arima.sim(n=30, model=list(ar=.5, ma=c(-.6, .6)), innov=(1:30)==5, start.innov=rep(0,4))
    # Simulate an ARIMA(0,2,3) process
    y <- arima.sim(n=100, model=list(ma=c(0.5, 0.5, -0.1), order=c(0,2,3)))

Package stats version 6.1.5-18
Package Index