arch.bootstrap.StationaryBootstrap

class arch.bootstrap.StationaryBootstrap(block_size, *args, random_state=None, **kwargs)[source]

Politis and Romano (1994) bootstrap with expon distributed block sizes

Parameters
block_sizeint

Average size of block to use

args

Positional arguments to bootstrap

kwargs

Keyword arguments to bootstrap

See also

arch.bootstrap.optimal_block_length

Optimal block length estimation

arch.bootstrap.CircularBlockBootstrap

Circular (wrap-around) bootstrap

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 random_state attribute after the bootstrap has been created. See the example below. Note that random_state is a reserved keyword and any variable passed using this keyword must be an instance of RandomState.

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 StationaryBootstrap
>>> from numpy.random import standard_normal
>>> y = standard_normal((500, 1))
>>> x = standard_normal((500,2))
>>> z = standard_normal(500)
>>> bs = StationaryBootstrap(12, 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 random_state if reproducibility is required

>>> from numpy.random import RandomState
>>> rs = RandomState(1234)
>>> bs = StationaryBootstrap(12, x, y=y, z=z, random_state=rs)
Attributes
datatuple

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

pos_datatuple

Tuple containing the positional arguments (in the order entered)

kw_datadict

Dictionary containing the keyword arguments

Methods

apply(func[, reps, extra_kwargs])

Applies a function to bootstrap replicated data

bootstrap(reps)

Iterator for use when bootstrapping

clone(*args, **kwargs)

Clones the bootstrap using different data with a fresh RandomState.

conf_int(func[, reps, method, size, tail, …])

Parameters

cov(func[, reps, recenter, extra_kwargs])

Compute parameter covariance using bootstrap

get_state()

Gets the state of the bootstrap’s random number generator

reset([use_seed])

Resets the bootstrap to either its initial state or the last seed.

seed(value)

Seeds the bootstrap’s random number generator

set_state(state)

Sets the state of the bootstrap’s random number generator

update_indices()

Update indices for the next iteration of the bootstrap.

var(func[, reps, recenter, extra_kwargs])

Compute parameter variance using bootstrap

Methods

apply(func[, reps, extra_kwargs])

Applies a function to bootstrap replicated data

bootstrap(reps)

Iterator for use when bootstrapping

clone(*args, **kwargs)

Clones the bootstrap using different data with a fresh RandomState.

conf_int(func[, reps, method, size, tail, …])

Parameters

cov(func[, reps, recenter, extra_kwargs])

Compute parameter covariance using bootstrap

get_state()

Gets the state of the bootstrap’s random number generator

reset([use_seed])

Resets the bootstrap to either its initial state or the last seed.

seed(value)

Seeds the bootstrap’s random number generator

set_state(state)

Sets the state of the bootstrap’s random number generator

update_indices()

Update indices for the next iteration of the bootstrap.

var(func[, reps, recenter, extra_kwargs])

Compute parameter variance using bootstrap

Properties

index

The current index of the bootstrap

random_state

Set or get the instance random state