statsmodels.tsa.tsatools.lagmat¶
-
statsmodels.tsa.tsatools.
lagmat
(x, maxlag, trim='forward', original='ex', use_pandas=False)[source]¶ Create 2d array of lags.
Parameters: x : array_like
Data; if 2d, observation in rows and variables in columns.
maxlag : int
All lags from zero to maxlag are included.
trim : {‘forward’, ‘backward’, ‘both’, ‘none’, None}
The trimming method to use.
- ‘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 : {‘ex’,’sep’,’in’}
How the original is treated.
- ‘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
If true, returns a DataFrame when the input is a pandas Series or DataFrame. If false, return numpy ndarrays.
Returns: lagmat : ndarray
The array with lagged observations.
y : ndarray, optional
Only returned if original == ‘sep’.
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.
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.]])