statsmodels.tsa.regime_switching.markov_autoregression.MarkovAutoregression.fit

MarkovAutoregression.fit(start_params=None, transformed=True, cov_type='approx', cov_kwds=None, method='bfgs', maxiter=100, full_output=1, disp=0, callback=None, return_params=False, em_iter=5, search_reps=0, search_iter=5, search_scale=1.0, **kwargs)

Fits the model by maximum likelihood via Hamilton filter.

Parameters:
start_paramsarray_like, optional

Initial guess of the solution for the loglikelihood maximization. If None, the default is given by Model.start_params.

transformedbool, optional

Whether or not start_params is already transformed. Default is True.

cov_typestr, optional

The type of covariance matrix estimator to use. Can be one of ‘approx’, ‘opg’, ‘robust’, or ‘none’. Default is ‘approx’.

cov_kwdsdict or None, optional

Keywords for alternative covariance estimators

methodstr, optional

The method determines which solver from scipy.optimize is used, and it can be chosen from among the following strings:

  • ‘newton’ for Newton-Raphson, ‘nm’ for Nelder-Mead

  • ‘bfgs’ for Broyden-Fletcher-Goldfarb-Shanno (BFGS)

  • ‘lbfgs’ for limited-memory BFGS with optional box constraints

  • ‘powell’ for modified Powell’s method

  • ‘cg’ for conjugate gradient

  • ‘ncg’ for Newton-conjugate gradient

  • ‘basinhopping’ for global basin-hopping solver

The explicit arguments in fit are passed to the solver, with the exception of the basin-hopping solver. Each solver has several optional arguments that are not the same across solvers. See the notes section below (or scipy.optimize) for the available arguments and for the list of explicit arguments that the basin-hopping solver supports.

maxiterint, optional

The maximum number of iterations to perform.

full_outputbool, optional

Set to True to have all available output in the Results object’s mle_retvals attribute. The output is dependent on the solver. See LikelihoodModelResults notes section for more information.

dispbool, optional

Set to True to print convergence messages.

callbackcallable callback(xk), optional

Called after each iteration, as callback(xk), where xk is the current parameter vector.

return_paramsbool, optional

Whether or not to return only the array of maximizing parameters. Default is False.

em_iterint, optional

Number of initial EM iteration steps used to improve starting parameters.

search_repsint, optional

Number of randomly drawn search parameters that are drawn around start_params to try and improve starting parameters. Default is 0.

search_iterint, optional

Number of initial EM iteration steps used to improve each of the search parameter repetitions.

search_scalefloat or array, optional.

Scale of variates for random start parameter search.

**kwargs

Additional keyword arguments to pass to the optimizer.

Returns:
MarkovSwitchingResults

Last update: Jun 14, 2024