Description Usage Arguments Details Value Author(s) References Examples

Performs model averaging on a set of (linear) candidate models with the weight vector chosen such that the leave-one-out cross validation error is minimized.

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`y` |
The response vector. |

`x` |
A matrix or dataframe containing the covariates. |

`method` |
A character vector specifiying whether jackknife model averaging (JMA) or
Mallow's model averaging (see |

`subset` |
A character vector specifiying whether all 2^p candidate models should be considered or the nested subset of p models. |

`factor.variables` |
A (vector of) string(s) specifying which variables should be treated as factors, i.e. recoded into dummy variables. Factor variables will automatically be recoded if not specified here. |

`pd` |
A logical value specifying whether messages should be printed or not. |

`variance` |
A character vector specifying whether no variance should be estimated or based on bootstrapping ( |

`bsa` |
A positive integer specifying the number of bootstrap samples used if |

This function utilizes Jackknife model averaging as described in Hansen and Racine (2012), see reference below.

If `subset`

`=`

`"all"`

, then 2^p candidate models are being evaluated. This means p can't be too large (say<20) and
occasionally the residual matrix used in the quadtratic programming problem may not be positive definite. In the latter case, this matrix
is altered by `jma`

such that it is positive definite, but results should be interpreted with care.

Returns an object of `class`

‘jma’:

`betahat` |
estimates coefficients |

`se` |
standarde error |

`lci` |
lower confidence limit |

`uci` |
upper confidence limit |

`weight` |
JMA weight vector |

`yhat` |
fitted values |

`ehat` |
fitted residuals |

`y` |
outcome variable |

`x` |
matrix of covariates |

Michael Schomaker (based on the file of Bruce Hansen at https://www.ssc.wisc.edu/~bhansen/progs/joe_12.html)

Hansen, B. and Racine, J. (2012), *Jackknife Model Averaging*, Journal of Econometrics, 167:38-46

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