get_arfima_d_estimates.py

def d_estimates_db(DF,minimum,maximum):
    # Create the output dataframe
    out=pd.DataFrame(columns=['adfStat','pVal','lags',\
                              'nObs','95% conf','corr'])
    # Copy the dataframe in order to not change it
    df0=DF.copy()
    # Create a range of posible d values and loop throughout them
    for d in np.linspace(minimum,maximum,11):
        # Create the log of close prices
        df1=np.log(df0['Close'])
        # Estimate the ARFIMA residuals
        df2=fracDiff_FFD(df1,d,thres=1e-5)
        # Compute the correlation between the log prices and the ARFIMA residuals
        corr=np.corrcoef(df1.loc[df2.index],df2['Close'])[0,1]
        # Estimate a unit root test to the ARFIMA residuals
        df2=adfuller(df2['Close'],regression='c',autolag='AIC')
        # Save all the results in the out dataframe
        out.loc[d]=list(df2[:4])+[df2[4]['5%']]+[corr] # with critical value
    # Return the out dataframe            
    return out