statsmodels.tsa.tsatools.lagmat

statsmodels.tsa.tsatools.lagmat(x, maxlag, trim='forward', original='ex', use_pandas=False)

Create 2d array of lags

Parameters:

• x (array_like, 1d or 2d) – data; if 2d, observation in rows and variables in columns
• maxlag (int) – 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.

• use_pandas (bool, optional) – If true, returns a DataFrame when the input is a pandas Series or DataFrame. If false, return numpy ndarrays.

Returns:

• lagmat (2d array) – array with lagged observations
• y (2d array, optional) – Only returned if original == ‘sep’

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.]])

Notes

When using a pandas DataFrame or Series with use_pandas=True, trim can only be ‘forward’ or ‘both’ since it is not possible to consistently extend index values.