class_docstr: Model Confidence Set (MCS) of Hansen, Lunde and Nason.
  
  Parameters
  ----------
  losses : {ndarray, DataFrame}
      T by k array containing losses from a set of models
  size : float, optional
      Value in (0,1) to use as the test size when implementing the
      mcs. Default value is 0.05.
  block_size : int, optional
      Length of window to use in the bootstrap.  If not provided, sqrt(T)
      is used.  In general, this should be provided and chosen to be
      appropriate for the data.
  method : {'max', 'R'}, optional
      MCS test and elimination implementation method, either 'max' or 'R'.
      Default is 'R'.
  reps : int, optional
      Number of bootstrap replications to uses.  Default is 1000.
  bootstrap : str, optional
      Bootstrap to use.  Options are
      'stationary' or 'sb': Stationary bootstrap (Default)
      'circular' or 'cbb': Circular block bootstrap
      'moving block' or 'mbb': Moving block bootstrap
  seed : {int, Generator, RandomState}, optional
      Seed value to use when creating the bootstrap used in the comparison.
      If an integer or None, the NumPy default_rng is used with the seed
      value.  If a Generator or a RandomState, the argument is used.
  
  Notes
  -----
  See [1]_ for details.
  
  References
  ----------
  .. [1] Hansen, P. R., Lunde, A., & Nason, J. M. (2011). The model confidence set.
     Econometrica, 79(2), 453-497.
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docstr: Model p-values for inclusion in the MCS
  
  Returns
  -------
  pvalues : DataFrame
      DataFrame where the index is the model index (column or name)
      containing the smallest size where the model is in the MCS.
docstr_tok: ["Model","p-values","for","inclusion","in","the","MCS","Returns","--","--","--","-","pvalues",":","DataFrame","DataFrame","where","the","index","is","the","model","index","(","column","or","name",")","containing","the","smallest","size","where","the","model","is","in","the","MCS","."]
code: def pvalues(self) -> pd.DataFrame:
      """
      Model p-values for inclusion in the MCS
  
      Returns
      -------
      pvalues : DataFrame
          DataFrame where the index is the model index (column or name)
          containing the smallest size where the model is in the MCS.
      """
      self._has_been_computed()
      return self._pvalues
  
imports: from __future__ import annotations
  from collections.abc import Hashable, Sequence
  import copy
  from typing import cast
  import warnings
  import numpy
  import pandas
  from arch.bootstrap.base import CircularBlockBootstrap, MovingBlockBootstrap, StationaryBootstrap
  from arch.typing import ArrayLike, ArrayLike2D, BoolArray, Float64Array, Int64Array, IntArray, Literal, Uint32Array
  from arch.utility.array import DocStringInheritor, ensure2d
label_desc: Use image node_id 9 for calling the MCS obj's underlying member method code with example usage: obj.pvalues() and returns: self