def fit_cochrane_orcutt(ts, regressors, maxIter=10, sc=None):
    """
    Fit linear regression model with AR(1) errors , for references on Cochrane Orcutt model:
    See [[https://onlinecourses.science.psu.edu/stat501/node/357]]
    See : Applied Linear Statistical Models - Fifth Edition - Michael H. Kutner , page 492
    The method assumes the time series to have the following model
    
    Y_t = B.X_t + e_t
    e_t = rho*e_t-1+w_t
    e_t has autoregressive structure , where w_t is iid ~ N(0,&sigma 2)
    
    Outline of the method :
    1) OLS Regression for Y (timeseries) over regressors (X)
    2) Apply auto correlation test (Durbin-Watson test) over residuals , to test whether e_t still
       have auto-regressive structure
    3) if test fails stop , else update update coefficients (B's) accordingly and go back to step 1)
    
    Parameters
    ----------
    ts:
        Vector of size N for time series data to create the model for as a Numpy array
    regressors:
        Matrix N X K for the timed values for K regressors over N time points as a Numpy array
    maxIter:
        maximum number of iterations in iterative cochrane-orchutt estimation
    
    Returns instance of class [[RegressionARIMAModel]]
    """
    
    assert sc != None, "Missing SparkContext"
    
    jvm = sc._jvm
    
    fnord = _nparray2breezematrix(sc, regressors)
    print(fnord)
    
    jmodel = jvm.com.cloudera.sparkts.models.RegressionARIMA.fitCochraneOrcutt(_nparray2breezevector(sc, ts), _nparray2breezematrix(sc, regressors), maxIter)
    return RegressionARIMAModel(jmodel=jmodel, sc=sc)
Fit linear regression model with AR(1) errors , for references on Cochrane Orcutt model:
    See [[https://onlinecourses.science.psu.edu/stat501/node/357]]
    See : Applied Linear Statistical Models - Fifth Edition - Michael H. Kutner , page 492
    The method assumes the time series to have the following model
    
    Y_t = B.X_t + e_t
    e_t = rho*e_t-1+w_t
    e_t has autoregressive structure , where w_t is iid ~ N(0,&sigma 2)
    
    Outline of the method :
    1) OLS Regression for Y (timeseries) over regressors (X)
    2) Apply auto correlation test (Durbin-Watson test) over residuals , to test whether e_t still
       have auto-regressive structure
    3) if test fails stop , else update update coefficients (B's) accordingly and go back to step 1)
    
    Parameters
    ----------
    ts:
        Vector of size N for time series data to create the model for as a Numpy array
    regressors:
        Matrix N X K for the timed values for K regressors over N time points as a Numpy array
    maxIter:
        maximum number of iterations in iterative cochrane-orchutt estimation
    
    Returns instance of class [[RegressionARIMAModel]]