# Semiparametric Bootstraps¶

Functions for semi-parametric bootstraps differ from those used in nonparametric bootstraps. At a minimum they must accept the keyword argument params which will contain the parameters estimated on the original (non-bootstrap) data. This keyword argument must be optional so that the function can be called without the keyword argument to estimate parameters. In most applications other inputs will also be needed to perform the semi-parametric step - these can be input using the extra_kwargs keyword input.

For simplicity, consider a semiparametric bootstrap of an OLS regression. The bootstrap step will combine the original parameter estimates and original regressors with bootstrapped residuals to construct a bootstrapped regressand. The bootstrap regressand and regressors can then be used to produce a bootstrapped parameter estimate.

The user-provided function must:

• Estimate the parameters when params is not provided

• Estimate residuals from bootstrapped data when params is provided to construct bootstrapped residuals, simulate the regressand, and then estimate the bootstrapped parameters

import numpy as np
def ols(y, x, params=None, x_orig=None):
if params is None:
return np.linalg.pinv(x).dot(y).ravel()

# When params is not None
# Bootstrap residuals
resids = y - x.dot(params)
# Simulated data
y_star = x_orig.dot(params) + resids
# Parameter estimates
return np.linalg.pinv(x_orig).dot(y_star).ravel()


Note

The function should return a 1-dimensional array. ravel is used above to ensure that the parameters estimated are 1d.

This function can then be used to perform a semiparametric bootstrap

from arch.bootstrap import IIDBootstrap
x = np.random.randn(100, 3)
e = np.random.randn(100, 1)
b = np.arange(1, 4)[:, None]
y = x.dot(b) + e
bs = IIDBootstrap(y, x)
ci = bs.conf_int(ols, 1000, method='percentile',
sampling='semi', extra_kwargs={'x_orig': x})


## Using partial instead of extra_kwargs¶

functools.partial can be used instead to provide a wrapper function which can then be used in the bootstrap. This example fixed the value of x_orig so that it is not necessary to use extra_kwargs.

from functools import partial
ols_partial = partial(ols, x_orig=x)
ci = bs.conf_int(ols_partial, 1000, sampling='semi')


## Semiparametric Bootstrap (Alternative Method)¶

Since semiparametric bootstraps are effectively bootstrapping residuals, an alternative method can be used to conduct a semiparametric bootstrap. This requires passing both the data and the estimated residuals when initializing the bootstrap.

First, the function used must be account for this structure.

def ols_semi_v2(y, x, resids=None, params=None, x_orig=None):
if params is None:
return np.linalg.pinv(x).dot(y).ravel()

# Simulated data if params provided
y_star = x_orig.dot(params) + resids
# Parameter estimates
return np.linalg.pinv(x_orig).dot(y_star).ravel()


This version can then be used to directly implement a semiparametric bootstrap, although ultimately it is not meaningfully simpler than the previous method.

resids = y - x.dot(ols_semi_v2(y,x))
bs = IIDBootstrap(y, x, resids=resids)
bs.conf_int(ols_semi_v2, 1000, sampling='semi', extra_kwargs={'x_orig': x})


Note

This alternative method is more useful when computing residuals is relatively expensive when compared to simulating data or estimating parameters. These circumstances are rarely encountered in actual problems.

# Parametric Bootstraps¶

Parametric bootstraps are meaningfully different from their nonparametric or semiparametric cousins. Instead of sampling the data to simulate the data (or residuals, in the case of a semiparametric bootstrap), a parametric bootstrap makes use of a fully parametric model to simulate data using a pseudo-random number generator.

Warning

Parametric bootstraps are model-based methods to construct exact confidence intervals through integration. Since these confidence intervals should be exact, bootstrap methods which make use of asymptotic normality are required (and may not be desirable).

Implementing a parametric bootstrap, like implementing a semi-parametric bootstrap, requires specific keyword arguments. The first is params, which, when present, will contain the parameters estimated on the original data. The second is rng which will contain the numpy.random.RandomState instance that is used by the bootstrap. This is provided to facilitate simulation in a reproducible manner.

A parametric bootstrap function must:

• Estimate the parameters when params is not provided

• Simulate data when params is provided and then estimate the bootstrapped parameters on the simulated data

This example continues the OLS example from the semiparametric example, only assuming that residuals are normally distributed. The variance estimator is the MLE.

def ols_para(y, x, params=None, state=None, x_orig=None):
if params is None:
beta = np.linalg.pinv(x).dot(y)
e = y - x.dot(beta)
sigma2 = e.T.dot(e) / e.shape[0]
return np.r_[beta.ravel(), sigma2.ravel()]

beta = params[:-1]
sigma2 = params[-1]
e = state.standard_normal(x_orig.shape[0])
ystar = x_orig.dot(beta) + np.sqrt(sigma2) * e

# Use the plain function to compute parameters
return ols_para(ystar, x_orig)


This function can then be used to form parametric bootstrap confidence intervals.

bs = IIDBootstrap(y,x)
ci = bs.conf_int(ols_para, 1000, method='percentile',
sampling='parametric', extra_kwargs={'x_orig': x})


Note

The parameter vector in this example includes the variance since this is required when specifying a complete model.