# VAR.boot: Simulate or bootstrap a VAR model In tsDyn: Nonlinear Time Series Models with Regime Switching

## Description

Allow to either simulate from scratch (by providing coefficients) or bootstrap from an estimated VAR model,

## Usage

 ```1 2 3 4 5 6 7``` ```VAR.sim(B, n = 200, lag = 1, include = c("const", "trend", "none", "both"), starting = NULL, innov = rmnorm(n, varcov = varcov), varcov = diag(1, nrow(B)), show.parMat = FALSE, returnStarting = FALSE, ...) VAR.boot(VARobject, boot.scheme = c("resample", "wild1", "wild2", "check"), seed, ...) ```

## Arguments

 `B` Matrix of coefficients. `n` Number of observations to simulate `lag` Number of lags of the VAR to simulate `include` Type of deterministic regressors to include in the VAR to simulate `starting` Starting values (matrix of dimension lag x k) for the VAR to simulate. If not given, set to zero. `innov` Innovations used for in the VAR to simulate. Should be matrix of dim n x k. By default multivariate normal. `varcov` Variance-covariance matrix for the innovations. By default identity matrix. `show.parMat` Logical. Should the parameter matrix be shown? Useful to understand how to give right input `returnStarting` Whether starting values are returned. Default to FALSE `...` Further arguments passed to the underlying (un-exported) `VAR.gen` function `VARobject` Object of class ` VAR` generated by function `lineVar` `boot.scheme` The bootstrap scheme. See details. `seed` Optional. Seed for the random resampling function.

## Details

For the bootstrap, the function resamples data from a given VAR model generated by `lineVar`, returning the resampled data. A residual recursive bootstrap is used, where one uses either a simple resampling, or the Wild bootstrap, either with a normal distribution (wild1) or inverting the sign randomly (wild2)

## Value

A matrix with the resampled series.

## Author(s)

Matthieu Stigler

`lineVar` to estimate the VAR. Similar `TVECM.sim` and `TVECM.boot` for `TVECM`, `TVAR.sim` and `TVAR.boot` for `TVAR` models.
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```## VAR.sim: simulate VAR as in Enders 2004, p 268 B1<-matrix(c(0.7, 0.2, 0.2, 0.7), 2) var1 <- VAR.sim(B=B1, n=100, include="none") ts.plot(var1, type="l", col=c(1,2)) B2<-rbind(c(0.5, 0.5, 0.5), c(0, 0.5, 0.5)) varcov<-matrix(c(1,0.2, 0.3, 1),2) var2 <- VAR.sim(B=B2, n=100, include="const", varcov=varcov) ts.plot(var2, type="l", col=c(1,2)) ## VAR.boot: Bootstrap a VAR data(zeroyld) mod <- lineVar(data=zeroyld,lag=1) VAR.boot(mod) ```