The model-confidence-set package provides a Python implementation of the Model Confidence Set (MCS) procedure (Hansen, Lunde, and Nason, 2011), a statistical method for comparing and selecting models based on their performance. It allows users to identify a set of models that are statistically indistinguishable from the best model, given a statistical confidence level.

This package

• supports both stationary and block bootstrap methods.
• implements two methods for p-value computation: relative and sequential.
• optionally displays progress during computation.

To install model-confidence-set, simply use pip:

pip install model-confidence-set

To use the Model Confidence Set in your Python code, follow the example below:

import numpy as np
import pandas as pd
from model_confidence_set import ModelConfidenceSet

# Example losses matrix where rows are observations and columns are models
losses = np.random.rand(100, 5)  # 100 observations for 5 models

# Initialize the MCS procedure (5'000 bootstrap iterations, 5% confidence level)
mcs = ModelConfidenceSet(losses, n_boot=5000, alpha=0.05, show_progress=True)

# Compute the MCS
mcs.compute()

# Retrieve the results as a pandas DataFrame (use as_dataframe=False for a dict)
results = mcs.results()
print(results)

• losses: A 2D numpy.ndarray or pandas.DataFrame containing loss values of models. Rows correspond to observations, and columns correspond to different models.
• n_boot: Number of bootstrap replications for computing p-values. Default is 5000.
• alpha: Significance level for determining model confidence set. Default is 0.05.
• block_len: The length of blocks for the block bootstrap. If None, it defaults to the square root of the number of observations.
• bootstrap_variant: Specifies the bootstrap variant to use. Options are 'stationary' or 'block'. Default is 'stationary'.
• method: The method used for p-value calculation. Options are 'R' for relative or 'SQ' for sequential. Default is 'R'.
• show_progress: Whether to show a progress bar during bootstrap computations. Default is False.

This package draws inspiration from

• the Matlab implementation by Kevin Sheppard
• the Python implementation by Michael Gong.