statsmodels.tsa.tests.test_arima.Arma

class statsmodels.tsa.tests.test_arima.Arma(endog, exog=None)[source]

univariate Autoregressive Moving Average model, conditional on initial values

The ARMA model is estimated either with conditional Least Squares or with conditional Maximum Likelihood. The implementation is using scipy.filter.lfilter which makes it faster than the Kalman Filter Implementation. The Kalman Filter Implementation however uses the exact Maximum Likelihood and will be more accurate, statistically more efficent in small samples.

In large samples conditional LS, conditional MLE and exact MLE should be very close to each other, they are equivalent asymptotically.

Notes

this can subclass TSMLEModel

TODO:

  • CondLS return raw estimation results
  • needs checking that there is no wrong state retained, when running fit several times with different options
  • still needs consistent order options.
  • Currently assumes that the mean is zero, no mean or effect of exogenous variables are included in the estimation.
__init__(endog, exog=None)[source]

Methods

__init__(endog[, exog])
expandparams(params) expand to full parameter array when some parameters are fixed
fit([order, start_params, method]) Estimate lag coefficients of an ARIMA process.
fit_mle([order, start_params, method, ...]) Estimate an ARMA model with given order using Conditional Maximum Likelihood
forecast([ar, ma, nperiod]) nperiod ahead forecast at the end of the data period
forecast2([step_ahead, start, end, endog]) rolling h-period ahead forecast without reestimation, 1 period ahead only
forecast3([step_ahead, start]) another try for h-step ahead forecasting
from_formula(formula, data[, subset]) Create a Model from a formula and dataframe.
generate_sample(ar, ma, nsample[, std])
geterrors(params)
hessian(params) Hessian of log-likelihood evaluated at params
information(params) Fisher information matrix of model
initialize()
jac(*args, **kwds) jac is deprecated, use score_obs instead!
loglike(params) Loglikelihood for arma model
loglikeobs(params)
nloglike(params)
nloglikeobs(params) Loglikelihood for arma model
predict(params[, exog]) After a model has been fit predict returns the fitted values.
predicted([ar, ma]) past predicted values of time series
reduceparams(params)
score(params) Gradient of log-likelihood evaluated at params
score_obs(params, **kwds) Jacobian/Gradient of log-likelihood evaluated at params for each observation.