# arch.univariate.Normal¶

class `arch.univariate.``Normal`(random_state=None)[source]

Standard normal distribution for use with ARCH models

Attributes
`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 Construct arrays to use in constrained optimization. `loglikelihood`(parameters, resids, sigma2[, …]) Computes the log-likelihood of assuming residuals are normally distributed, conditional on the variance `moment`(n[, parameters]) Moment of order n 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 Construct arrays to use in constrained optimization. `loglikelihood`(parameters, resids, sigma2[, …]) Computes the log-likelihood of assuming residuals are normally distributed, conditional on the variance `moment`(n[, parameters]) Moment of order n 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

 `name` The name of the distribution `random_state` The NumPy RandomState attached to the distribution