statsmodels.tsa.arima_process.arma_periodogram¶

statsmodels.tsa.arima_process.arma_periodogram(ar, ma, worN=None, whole=0)[source]¶

Periodogram for ARMA process given by lag-polynomials ar and ma.

Parameters:¶

ar : array_like¶

The autoregressive lag-polynomial with leading 1 and lhs sign.

ma : array_like¶

The moving average lag-polynomial with leading 1.

worN : {None, int}, optional¶

An option for scipy.signal.freqz (read “w or N”). If None, then compute at 512 frequencies around the unit circle. If a single integer, the compute at that many frequencies. Otherwise, compute the response at frequencies given in worN.

whole : {0,1}, optional¶

An options for scipy.signal.freqz/ Normally, frequencies are computed from 0 to pi (upper-half of unit-circle. If whole is non-zero compute frequencies from 0 to 2*pi.

Returns:¶

• w (ndarray) – The frequencies.

• sd (ndarray) – The periodogram, also known as the spectral density.

Notes

Normalization ?

This uses signal.freqz, which does not use fft. There is a fft version somewhere.