Description Usage Arguments Details Value See Also Examples

The fixed regression coefficients and the random effects variance are repeatedly estimated using the wild bootstrap to estimate the sampling distribution of the estimators.

1 |

`object` |
a model estimated using a model fitting function |

`B` |
the number of bootstrap replications. |

`data` |
an optional data frame containing the variables in the model. If relevant variables are not found in |

`h0` |
a set of bandwidths for nonparametric kernel regression to reduce the model. |

`...` |
other arguments. |

`plmm.bs`

computes bootstrap estimates of the fixed regression coefficients and the random effects variance using the wild bootstrap resampling technique. The user can modify bandwidths in a list object created by `select.h0`

and pass the object to `plmm.bs`

. In the wild bootstrap procedure, only the bandwidth for the kernel estimation of the response is recalculated while those for the fixed parametric component remain the same as given in `h0`

. `plmm.bs`

returns an object of `bs.plmm`

class for which the `summary`

method is available.

A list of a matrix of `B`

rows. Its columns contain `B`

bootstrap estimates of the fixed regression coefficients and random effects variance.

1 2 3 4 5 6 7 8 9 | ```
data(plmm.data)
h0 <- select.h0(y1~x1+x2+x3|t1, data=plmm.data)
model <- plmm(y1~x1+x2+x3|t1, h0=h0, random=cluster, data=plmm.data)
model2 <- wplmm(model, heteroX=x3, data=plmm.data)
## Not run:
bs <- plmm.bs(model, B=500, data=plmm.data, h0=h0)
bs2 <- plmm.bs(model2, B=500, data=plmm.data, h0=h0)
## End(Not run)
``` |

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