arch.bootstrap.MCS¶

class arch.bootstrap.MCS(losses, size, reps=1000, block_size=None, method='R', bootstrap='stationary')[source]

Model Confidence Set (MCS) of Hansen, Lunde and Nason.

Parameters
• losses ({ndarray, DataFrame}) – T by k array containing losses from a set of models

• size (float, optional) – Value in (0,1) to use as the test size when implementing the mcs. Default value is 0.05.

• block_size (int, optional) – Length of window to use in the bootstrap. If not provided, sqrt(T) is used. In general, this should be provided and chosen to be appropriate for the data.

• method ({'max', 'R'}, optional) – MCS test and elimination implementation method, either ‘max’ or ‘R’. Default is ‘R’.

• reps (int, optional) – Number of bootstrap replications to uses. Default is 1000.

• bootstrap (str, optional) – Bootstrap to use. Options are ‘stationary’ or ‘sb’: Stationary bootstrap (Default) ‘circular’ or ‘cbb’: Circular block bootstrap ‘moving block’ or ‘mbb’: Moving block bootstrap

Notes

See 1 for details.

References

1

Hansen, P. R., Lunde, A., & Nason, J. M. (2011). The model confidence set. Econometrica, 79(2), 453-497.

Methods

 Compute the set of models in the confidence set. Reset the bootstrap to it’s initial state. seed(value) Seed the bootstrap’s random number generator

Properties

 excluded List of model indices that are excluded from the MCS included List of model indices that are included in the MCS pvalues Model p-values for inclusion in the MCS