wildboottest 0.1.3

Wild Cluster Bootstrap Inference for Linear Models in Python

wildboottest

wildboottest implements multiple fast wild cluster bootstrap algorithms as developed in Roodman et al (2019) and MacKinnon, Nielsen & Webb (2022).

It has similar, but more limited functionality than Stata's boottest, R's fwildcusterboot or Julia's WildBootTests.jl. It supports

• The wild cluster bootstrap for OLS (Cameron, Gelbach & Miller 2008, Roodman et al (2019)).
• Multiple new versions of the wild cluster bootstrap as described in MacKinnon, Nielsen & Webb (2022), including the WCR13, WCR31, WCR33, WCU13, WCU31 and WCU33.
• CRV1 and CRV3 robust variance estimation, including the CRV3-Jackknife as described in MacKinnon, Nielsen & Webb (2022).

At the moment, wildboottest only computes wild cluster bootstrapped p-values, and no confidence intervals.

Other features that are currently not supported:

• The (non-clustered) wild bootstrap for OLS (Wu, 1986).
• The subcluster bootstrap (MacKinnon and Webb 2018).
• Confidence intervals formed by inverting the test and iteratively searching for bounds.
• Multiway clustering.

Direct support for statsmodels and linearmodels is work in progress.

If you'd like to cooperate, either send us an email or comment in the issues section!

Example

from wildboottest.wildboottest import wildboottest
import statsmodels.api as sm
import numpy as np
import pandas as pd

# create data
np.random.seed(12312312)
N = 1000
k = 10
G = 25
X = np.random.normal(0, 1, N * k).reshape((N,k))
X = pd.DataFrame(X)
X.rename(columns = {0:"X1"}, inplace = True)
beta = np.random.normal(0,1,k)
beta[0] = 0.005
u = np.random.normal(0,1,N)
Y = 1 + X @ beta + u
cluster = np.random.choice(list(range(0,G)), N)

# estimation
model = sm.OLS(Y, X)

wildboottest(model, param = "X1", cluster = cluster, B = 9999, bootstrap_type = "11")
#   param              statistic   p-value
# 0    X1  [-1.0530803154504016]  0.308831

wildboottest(model, param = "X1", cluster = cluster, B = 9999, bootstrap_type = "31")
#   param              statistic   p-value
# 0    X1  [-1.0530803154504016]  0.307631

wildboottest(model, param = "X1", cluster = cluster, B = 9999, bootstrap_type = "33")
#   param              statistic   p-value
# 0    X1  [-1.0394791020434824]  0.294286


wildboottest(model, cluster = cluster, B = 9999)
#   param              statistic   p-value
# 0    X1  [-1.0530803154504016]  0.315132
# 1     1    [-18.5149486170657]  0.000000
# 2     2    [7.831855813581191]  0.000000
# 3     3   [-16.85188951397906]  0.000000
# 4     4  [-12.721095348008182]  0.000000
# 5     5    [1.200524160940055]  0.243624
# 6     6    [6.870946666836135]  0.000000
# 7     7   [-31.31653422266621]  0.000000
# 8     8    [10.26443257212472]  0.000000
# 9     9  [-20.650361366939535]  0.000000