4.8.10.1.3. statsmodels.tsa.varma_process.lagmat¶

statsmodels.tsa.varma_process.lagmat(x, maxlag, trim='forward', original='ex')[source]¶

create 2d array of lags

Parameters:

x : array_like, 1d or 2d data; if 2d, observation in rows and variables in columns maxlag : int or sequence of ints all lags from zero to maxlag are included trim : str {‘forward’, ‘backward’, ‘both’, ‘none’} or None ‘forward’ : trim invalid observations in front ‘backward’ : trim invalid initial observations ‘both’ : trim invalid observations on both sides ‘none’, None : no trimming of observations original : str {‘ex’,’sep’,’in’} ‘ex’ : drops the original array returning only the lagged values. ‘in’ : returns the original array and the lagged values as a single array. ‘sep’ : returns a tuple (original array, lagged values). The original array is truncated to have the same number of rows as the returned lagmat.

Returns:

lagmat : 2d array array with lagged observations y : 2d array, optional Only returned if original == ‘sep’

Notes

TODO: * allow list of lags additional to maxlag * create varnames for columns

Examples

>>> from statsmodels.tsa.tsatools import lagmat
>>> import numpy as np
>>> X = np.arange(1,7).reshape(-1,2)
>>> lagmat(X, maxlag=2, trim="forward", original='in')
array([[ 1.,  2.,  0.,  0.,  0.,  0.],
   [ 3.,  4.,  1.,  2.,  0.,  0.],
   [ 5.,  6.,  3.,  4.,  1.,  2.]])

>>> lagmat(X, maxlag=2, trim="backward", original='in')
array([[ 5.,  6.,  3.,  4.,  1.,  2.],
   [ 0.,  0.,  5.,  6.,  3.,  4.],
   [ 0.,  0.,  0.,  0.,  5.,  6.]])

>>> lagmat(X, maxlag=2, trim="both", original='in')
array([[ 5.,  6.,  3.,  4.,  1.,  2.]])

>>> lagmat(X, maxlag=2, trim="none", original='in')
array([[ 1.,  2.,  0.,  0.,  0.,  0.],
   [ 3.,  4.,  1.,  2.,  0.,  0.],
   [ 5.,  6.,  3.,  4.,  1.,  2.],
   [ 0.,  0.,  5.,  6.,  3.,  4.],
   [ 0.,  0.,  0.,  0.,  5.,  6.]])