ENH: Bootstrap framework

Introduces a bootstrap framework for use by multiple comparison procedures

README.md

@@ -1,4 +1,4 @@
-[![Documentation Status](https://readthedocs.org/projects/arch/badge/?version=latest)](https://readthedocs.org/projects/arch/?badge=latest)
+[![Documentation Status](https://readthedocs.org/projects/arch/badge/?version=latest)](http://arch.readthedocs.org/en/latest/)
 [![CI Status](https://travis-ci.org/bashtage/arch.svg?branch=master)](https://travis-ci.org/bashtage/arch)
 [![Coverage Status](https://coveralls.io/repos/bashtage/arch/badge.png?branch=master)](https://coveralls.io/r/bashtage/arch?branch=master)
 
@@ -11,20 +11,20 @@ This is a work-in-progress for ARCH and related models, written in Python
 ## What is this repository for?
 
 * Mean models
-  * Constant mean
-  * Heterogeneous Autoregression (HAR)
-  * Autoregression (AR)
-  * Zero mean
-  * Models with and without exogensou regressors
+    * Constant mean
+    * Heterogeneous Autoregression (HAR)
+    * Autoregression (AR)
+    * Zero mean
+    * Models with and without exogenous regressors
 * Volatility models
-  * ARCH
-  * GARCH
-  * TARCH
-  * EGARCH
-  * EWMA/RiskMetrics
+    * ARCH
+    * GARCH
+    * TARCH
+    * EGARCH
+    * EWMA/RiskMetrics
 * Distributions
-  * Normal
-  * Student's T
+    * Normal
+    * Student's T
 
 ## Examples
 

arch/bootstrap/__init__.py

@@ -1 +1,2 @@
-__author__ = 'kevin.sheppard'
+from .base import IIDBootstrap, CircularBlockBootstrap, MovingBlockBootstrap, \
+    StationaryBootstrap

arch/bootstrap/_samplers.pyx

@@ -0,0 +1,23 @@
+import numpy as np
+cimport numpy as np
+cimport cython
+
+__all__ = ['harch_recursion','arch_recursion','garch_recursion',
+           'egarch_recursion']
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+@cython.cdivision(True)
+def stationary_bootstrap_sample(int[:] indices,
+                                double[:] u,
+                                double p):
+    cdef Py_ssize_t num_items, i
+    num_items = indices.shape[0]
+
+    for i in range(1, num_items):
+        if u[i] > p:
+            indices[i] = indices[i - 1] + 1
+            if indices[i] == num_items:
+                indices[i] = 0
+
+    return indices

arch/bootstrap/_samplers_python.py

@@ -0,0 +1,14 @@
+from __future__ import absolute_import, division
+from ..compat.numba import jit
+
+
+@jit
+def stationary_bootstrap_sample(indices, u, p):
+    num_items = indices.shape[0]
+    for i in range(1, num_items):
+        if u[i] > p:
+            indices[i] = indices[i - 1] + 1
+            if indices[i] == num_items:
+                indices[i] = 0
+
+    return indices