Augmented Dickey-Fuller unit root test

adf.py

import numpy as np
from statsmodels.regression.linear_model import OLS
from statsmodels.tsa.tsatools import lagmat, add_trend
from statsmodels.tsa.adfvalues import mackinnonp

def adf(ts):
    """
    Augmented Dickey-Fuller unit root test
    """
    # make sure we are working with an array, convert if necessary
    ts = np.asarray(ts)
    
    # Get the dimension of the array
    nobs = ts.shape[0]
    
    # We use 1 as maximum lag in our calculations
    maxlag = 1
    
    # Calculate the discrete difference
    tsdiff = np.diff(ts)
    
    # Create a 2d array of lags, trim invalid observations on both sides
    tsdall = lagmat(tsdiff[:, None], maxlag, trim='both', original='in')
    # Get dimension of the array
    nobs = tsdall.shape[0] 
    
    # replace 0 xdiff with level of x
    tsdall[:, 0] = ts[-nobs - 1:-1]  
    tsdshort = tsdiff[-nobs:]
    
    # Calculate the linear regression using an ordinary least squares model    
    results = OLS(tsdshort, add_trend(tsdall[:, :maxlag + 1], 'c')).fit()
    adfstat = results.tvalues[0]
    
    # Get approx p-value from a precomputed table (from stattools)
    pvalue = mackinnonp(adfstat, 'c', N=1)
    return pvalue