# FCVARsimBS: Draw Bootstrap Samples from the FCVAR Model In FCVAR: Estimation and Inference for the Fractionally Cointegrated VAR

 FCVARsimBS R Documentation

## Draw Bootstrap Samples from the FCVAR Model

### Description

`FCVARsimBS` simulates the FCVAR model as specified by input `model` and starting values specified by `data`. It creates a wild bootstrap sample by augmenting each iteration with a bootstrap error. The errors are sampled from the residuals specified under the `model` input and have a positive or negative sign with equal probability (the Rademacher distribution).

### Usage

```FCVARsimBS(data, model, NumPeriods)
```

### Arguments

 `data` A T x p matrix of starting values for the simulated realizations. `model` A list of estimation results, just as if estimated from `FCVARest`. The parameters in `model` can also be set or adjusted by assigning new values. `NumPeriods` The number of time periods in the simulation.

### Value

A `NumPeriods` by p matrix `xBS` of simulated bootstrap values.

`FCVARoptions` to set default estimation options. `FCVARestn` for the specification of the `model`. Use `FCVARsim` to draw a sample from the FCVAR model. For simulations intended for bootstrapping statistics, use `FCVARsimBS`.

Other FCVAR auxiliary functions: `FCVARforecast()`, `FCVARlikeGrid()`, `FCVARsim()`, `FracDiff()`, `plot.FCVAR_grid()`

### Examples

```
opt <- FCVARoptions()
opt\$gridSearch   <- 0 # Disable grid search in optimization.
opt\$dbMin        <- c(0.01, 0.01) # Set lower bound for d,b.
opt\$dbMax        <- c(2.00, 2.00) # Set upper bound for d,b.
opt\$constrained  <- 0 # Impose restriction dbMax >= d >= b >= dbMin ? 1 <- yes, 0 <- no.
x <- votingJNP2014[, c("lib", "ir_can", "un_can")]
results <- FCVARestn(x, k = 2, r = 1, opt)
xBS <- FCVARsimBS(x[1:10, ], results, NumPeriods = 100)

```

FCVAR documentation built on May 5, 2022, 9:06 a.m.