arch.bootstrap.SPA¶
- class
arch.bootstrap.
SPA
(benchmark, models, block_size=None, reps=1000, bootstrap='stationary', studentize=True, nested=False)[source]¶ Test of Superior Predictive Ability (SPA) of White and Hansen.
The SPA is also known as the Reality Check or Bootstrap Data Snooper.
- Parameters
benchmark ({ndarray, Series}) – T element array of benchmark model losses
models ({ndarray, DataFrame}) – T by k element array of alternative model losses
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) – Flag indicating to studentize loss differentials. Default is True
nested=False – 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
White, H. (2000). “A reality check for data snooping.” Econometrica 68, no. 5, 1097-1126.
Hansen, P. R. (2005). “A test for superior predictive ability.” Journal of Business & Economic Statistics, 23(4)
Notes
- The three p-value correspond to different re-centering decisions.
Upper : Never recenter to all models are relevant to distribution
Consistent : Only recenter if closer than a log(log(t)) bound
Lower : Never recenter a model if worse than benchmark
See also
References
- 1
Hansen, P. R. (2005). A test for superior predictive ability. Journal of Business & Economic Statistics, 23(4), 365-380.
- 2
White, H. (2000). A reality check for data snooping. Econometrica, 68(5), 1097-1126.
Methods
better_models
([pvalue, pvalue_type])Returns set of models rejected as being equal-or-worse than the benchmark
compute
()Compute the bootstrap pvalue.
critical_values
([pvalue])Returns data-dependent critical values
reset
()Reset the bootstrap to its initial state.
seed
(value)Seed the bootstrap’s random number generator
subset
(selector)Sets a list of active models to run the SPA on.
Properties
P-values corresponding to the lower, consistent and upper p-values.