arch.bootstrap.StepM¶
- class
arch.bootstrap.
StepM
(benchmark, models, size=0.05, block_size=None, reps=1000, bootstrap='stationary', studentize=True, nested=False)[source]¶ StepM multiple comparison procedure of Romano and Wolf.
- Parameters
benchmark ({ndarray, Series}) – T element array of benchmark model losses
models ({ndarray, DataFrame}) – T by k element array of alternative model losses
size (float, optional) – Value in (0,1) to use as the test size when implementing the comparison. 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.
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
studentize (bool, optional) – Flag indicating to studentize loss differentials. Default is True
nested (bool, optional) – Flag indicating to use a nested bootstrap to compute variances for studentization. Default is False. Note that this can be slow since the procedure requires k extra bootstraps.
References
Romano, J. P., & Wolf, M. (2005). “Stepwise multiple testing as formalized data snooping.” Econometrica, 73(4), 1237-1282.
Notes
The size controls the Family Wise Error Rate (FWER) since this is a multiple comparison procedure. Uses SPA and the consistent selection procedure.
See 1 for detail.
See also
References
- 1
Romano, J. P., & Wolf, M. (2005). Stepwise multiple testing as formalized data snooping. Econometrica, 73(4), 1237-1282.
Methods
compute
()Compute the set of superior models.
reset
()Reset the bootstrap to it’s initial state.
seed
(value)Seed the bootstrap’s random number generator
Properties
List of the indices or column names of the superior models