arch.univariate.distribution.Distribution

class arch.univariate.distribution.Distribution(random_state=None, *, seed=None)[source]

Template for subclassing only

Attributes
generator

The NumPy Generator or RandomState attached to the distribution

name

The name of the distribution

random_state

The NumPy RandomState attached to the distribution

Methods

bounds(resids)

Parameter bounds for use in optimization.

cdf(resids[, parameters])

Cumulative distribution function

constraints()

Construct arrays to use in constrained optimization.

loglikelihood(parameters, resids, sigma2[, …])

Loglikelihood evaluation.

moment(n[, parameters])

Moment of order n

parameter_names()

Names of distribution shape parameters

partial_moment(n[, z, parameters])

Order n lower partial moment from -inf to z

ppf(pits[, parameters])

Inverse cumulative density function (ICDF)

simulate(parameters)

Simulates i.i.d.

starting_values(std_resid)

Construct starting values for use in optimization.

Methods

bounds(resids)

Parameter bounds for use in optimization.

cdf(resids[, parameters])

Cumulative distribution function

constraints()

Construct arrays to use in constrained optimization.

loglikelihood(parameters, resids, sigma2[, …])

Loglikelihood evaluation.

moment(n[, parameters])

Moment of order n

parameter_names()

Names of distribution shape parameters

partial_moment(n[, z, parameters])

Order n lower partial moment from -inf to z

ppf(pits[, parameters])

Inverse cumulative density function (ICDF)

simulate(parameters)

Simulates i.i.d.

starting_values(std_resid)

Construct starting values for use in optimization.

Properties

generator

The NumPy Generator or RandomState attached to the distribution

name

The name of the distribution

random_state

The NumPy RandomState attached to the distribution