Cluster robust wild bootstrap for linear models | R Documentation |

Cluster robust wild bootstrap for linear models.

```
wild.boot(y, x, cluster, ind = NULL, R = 999, parallel = FALSE)
```

`y` |
The dependent variable, a numerical vector with numbers. |

`x` |
A matrix or a data.frame with the indendent variables. |

`cluster` |
A vector indicating the clusters. |

`ind` |
A vector with the indices of the variables for which wild bootstrap p-values will be computed. If NULL (default value), the p-values are computed for each variable. |

`R` |
The number of bootstrap replicates to perform. |

`parallel` |
Do you want the process to take place in parallel? If yes, then set this equal to TRUE. |

A linear regression model for clustered data is fitted. For more information see Chapter 4.21 of Hansen (2019).

A matrix with 5 columns, the estimated coefficients of the linear model, their cluster robust standard error, their cluster robust test statistic, their cluster robust p-value, and their cluster robust wild bootstrap p-value.

Michail Tsagris and Stefanos Fafalios.

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr and Stefanos Fafalios stefanosfafalios@gmail.com.

Cameron A. Colin, Gelbach J.B., and Miller D.L. (2008). Bootstrap-Based Improvements for Inference with Clustered Errors. The Review of Economics and Statistics 90(3): 414-427.

` gee.reg, cluster.lm `

```
y <- rnorm(200)
id <- sample(1:20, 200, replace = TRUE)
x <- matrix( rnorm(200 * 3), ncol = 3 )
wild.boot(y, x, cluster = id)
```

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