arch.bootstrap.CircularBlockBootstrap¶

class arch.bootstrap.CircularBlockBootstrap(block_size: int, *args: ndarray | DataFrame | Series, seed: int | Generator | RandomState | None = None, **kwargs: ndarray | DataFrame | Series)[source]¶

Bootstrap using blocks of the same length with end-to-start wrap around

Parameters:¶

block_size: int¶

Size of block to use

*args: ndarray | DataFrame | Series¶

Positional arguments to bootstrap

seed: int | Generator | RandomState | None = None¶

Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy.

**kwargs: ndarray | DataFrame | Series¶

Keyword arguments to bootstrap

data¶

Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)

Type:¶

tuple

pos_data¶

Tuple containing the positional arguments (in the order entered)

Type:¶

tuple

kw_data¶

Dictionary containing the keyword arguments

Type:¶

dict

Notes

Supports numpy arrays and pandas Series and DataFrames. Data returned has the same type as the input date.

Data entered using keyword arguments is directly accessibly as an attribute.

To ensure a reproducible bootstrap, you must set the seed attribute after the bootstrap has been created. See the example below. Note that seed is a reserved keyword and any variable passed using this keyword must be an integer, a Generator or a RandomState.

See also

arch.bootstrap.optimal_block_length

Optimal block length estimation

arch.bootstrap.StationaryBootstrap

Politis and Romano’s bootstrap with exp. distributed block lengths

Examples

Data can be accessed in a number of ways. Positional data is retained in the same order as it was entered when the bootstrap was initialized. Keyword data is available both as an attribute or using a dictionary syntax on kw_data.

>>> from arch.bootstrap import CircularBlockBootstrap
>>> from numpy.random import standard_normal
>>> y = standard_normal((500, 1))
>>> x = standard_normal((500, 2))
>>> z = standard_normal(500)
>>> bs = CircularBlockBootstrap(17, x, y=y, z=z)
>>> for data in bs.bootstrap(100):
...     bs_x = data[0][0]
...     bs_y = data[1]['y']
...     bs_z = bs.z

Set the seed if reproducibility is required

>>> from numpy.random import default_rng
>>> gen = default_rng(1234)
>>> bs = CircularBlockBootstrap(17, x, y=y, z=z, seed=gen)

Methods

apply(func[, reps, extra_kwargs])	Applies a function to bootstrap replicated data
bootstrap(reps)	Iterator for use when bootstrapping
clone(*args[, seed])	Clones the bootstrap using different data with a fresh prng.
conf_int(func[, reps, method, size, tail, ...])
cov(func[, reps, recenter, extra_kwargs])	Compute parameter covariance using bootstrap
reset([use_seed])	Resets the bootstrap to either its initial state or the last seed.
update_indices()	Update indices for the next iteration of the bootstrap.
var(func[, reps, recenter, extra_kwargs])	Compute parameter variance using bootstrap

Properties

generator	Set or get the instance PRNG
index	The current index of the bootstrap
state	Set or get the generator's state