Python OLS.f_test方法代码示例

# 需要导入模块: from statsmodels.regression.linear_model import OLS [as 别名]
# 或者: from statsmodels.regression.linear_model.OLS import f_test [as 别名]
 def setupClass(cls):
     data = longley.load()
     data.exog = add_constant(data.exog, prepend=False)
     res1 = OLS(data.endog, data.exog).fit()
     R2 = [[0,1,-1,0,0,0,0],[0, 0, 0, 0, 1, -1, 0]]
     cls.Ftest1 = res1.f_test(R2)
     hyp = 'x2 = x3, x5 = x6'
     cls.NewFtest1 = res1.f_test(hyp)

# 需要导入模块: from statsmodels.regression.linear_model import OLS [as 别名]
# 或者: from statsmodels.regression.linear_model.OLS import f_test [as 别名]
def test_f_score_with_covars_and_normalized_design_withcovar(random_state=0):
    """

    This test has a statsmodels dependance. There seems to be no simple,
    alternative way to perform a F-test on a linear model including
    covariates.

    """
    try:
        from statsmodels.regression.linear_model import OLS
    except:
        warnings.warn("Statsmodels is required to run this test")
        raise nose.SkipTest

    rng = check_random_state(random_state)

    ### Normalized data
    n_samples = 50
    # generate data
    var1 = np.ones((n_samples, 1)) / np.sqrt(n_samples)  # normalized
    var2 = rng.randn(n_samples, 1)
    var2 = var2 / np.sqrt(np.sum(var2 ** 2, 0))  # normalize
    covars = np.eye(n_samples, 3)  # covars is orthogonal
    covars[3] = -1  # covars is orthogonal to var1
    covars = orthonormalize_matrix(covars)
    # own f_score
    f_val_own = _f_score_with_covars_and_normalized_design(var1, var2, covars)[0]
    # statsmodels f_score
    test_matrix = np.array([[1.0, 0.0, 0.0, 0.0]])
    statsmodels_ols = OLS(var2, np.hstack((var1, covars))).fit()
    f_val_statsmodels = statsmodels_ols.f_test(test_matrix).fvalue[0]
    assert_array_almost_equal(f_val_own, f_val_statsmodels)

# 需要导入模块: from statsmodels.regression.linear_model import OLS [as 别名]
# 或者: from statsmodels.regression.linear_model.OLS import f_test [as 别名]
def reset_ramsey(res, degree=5):
    '''Ramsey's RESET specification test for linear models

    This is a general specification test, for additional non-linear effects
    in a model.


    Notes
    -----
    The test fits an auxiliary OLS regression where the design matrix, exog,
    is augmented by powers 2 to degree of the fitted values. Then it performs
    an F-test whether these additional terms are significant.

    If the p-value of the f-test is below a threshold, e.g. 0.1, then this
    indicates that there might be additional non-linear effects in the model
    and that the linear model is mis-specified.


    References
    ----------
    http://en.wikipedia.org/wiki/Ramsey_RESET_test

    '''
    order = degree + 1
    k_vars = res.model.exog.shape[1]
    #vander without constant and x:
    y_fitted_vander = np.vander(res.fittedvalues, order)[:, :-2] #drop constant
    exog = np.column_stack((res.model.exog, y_fitted_vander))
    res_aux = OLS(res.model.endog, exog).fit()
    #r_matrix = np.eye(degree, exog.shape[1], k_vars)
    r_matrix = np.eye(degree-1, exog.shape[1], k_vars)
    #df1 = degree - 1
    #df2 = exog.shape[0] - degree - res.df_model  (without constant)
    return res_aux.f_test(r_matrix) #, r_matrix, res_aux

# 需要导入模块: from statsmodels.regression.linear_model import OLS [as 别名]
# 或者: from statsmodels.regression.linear_model.OLS import f_test [as 别名]
def test_permuted_ols_intercept_statsmodels_withcovar(random_state=0):
    """

    This test has a statsmodels dependance. There seems to be no simple,
    alternative way to perform a F-test on a linear model including
    covariates.

    """
    try:
        from statsmodels.regression.linear_model import OLS
    except:
        warnings.warn("Statsmodels is required to run this test")
        raise nose.SkipTest

    rng = check_random_state(random_state)
    # design parameters
    n_samples = 50
    # create design
    target_var = rng.randn(n_samples, 1)
    tested_var = np.ones((n_samples, 1))
    confounding_vars = rng.randn(n_samples, 2)
    # statsmodels OLS
    ols = OLS(target_var, np.hstack((tested_var, confounding_vars))).fit()
    fvals = ols.f_test([[1.0, 0.0, 0.0]]).fvalue
    # permuted OLS
    _, orig_scores, _ = permuted_ols(tested_var, target_var, confounding_vars, n_perm=0, random_state=random_state)
    # same thing but with model_intercept=True to check it has no effect
    _, orig_scores_addintercept, _ = permuted_ols(
        tested_var, target_var, confounding_vars, model_intercept=True, n_perm=0, random_state=random_state
    )
    assert_array_almost_equal(fvals, orig_scores, decimal=6)
    assert_array_almost_equal(orig_scores, orig_scores_addintercept, decimal=6)

# 需要导入模块: from statsmodels.regression.linear_model import OLS [as 别名]
# 或者: from statsmodels.regression.linear_model.OLS import f_test [as 别名]
def linear_lm(resid, exog, func=None):
    '''Lagrange multiplier test for linearity against functional alternative

    limitations: Assumes currently that the first column is integer.
    Currently it doesn't check whether the transformed variables contain NaNs,
    for example log of negative number.

    Parameters
    ----------
    resid : ndarray
        residuals of a regression
    exog : ndarray
        exogenous variables for which linearity is tested
    func : callable
        If func is None, then squares are used. func needs to take an array
        of exog and return an array of transformed variables.

    Returns
    -------
    lm : float
       Lagrange multiplier test statistic
    lm_pval : float
       p-value of Lagrange multiplier tes
    ftest : ContrastResult instance
       the results from the F test variant of this test

    Notes
    -----
    written to match Gretl's linearity test.
    The test runs an auxilliary regression of the residuals on the combined
    original and transformed regressors.
    The Null hypothesis is that the linear specification is correct.

    '''
    from scipy import stats

    if func is None:
        func = lambda x: np.power(x, 2)

    exog_aux = np.column_stack((exog, func(exog[:,1:])))

    nobs, k_vars = exog.shape
    ls = OLS(resid, exog_aux).fit()
    ftest = ls.f_test(np.eye(k_vars - 1, k_vars * 2 - 1, k_vars))
    lm = nobs * ls.rsquared
    lm_pval = stats.chi2.sf(lm, k_vars - 1)
    return lm, lm_pval, ftest

# 需要导入模块: from statsmodels.regression.linear_model import OLS [as 别名]
# 或者: from statsmodels.regression.linear_model.OLS import f_test [as 别名]
def grangercausalitytests(x, maxlag, addconst=True, verbose=True):
    """four tests for granger non causality of 2 timeseries

    all four tests give similar results
    `params_ftest` and `ssr_ftest` are equivalent based on F test which is
    identical to lmtest:grangertest in R

    Parameters
    ----------
    x : array, 2d, (nobs,2)
        data for test whether the time series in the second column Granger
        causes the time series in the first column
    maxlag : integer
        the Granger causality test results are calculated for all lags up to
        maxlag
    verbose : bool
        print results if true

    Returns
    -------
    results : dictionary
        all test results, dictionary keys are the number of lags. For each
        lag the values are a tuple, with the first element a dictionary with
        teststatistic, pvalues, degrees of freedom, the second element are
        the OLS estimation results for the restricted model, the unrestricted
        model and the restriction (contrast) matrix for the parameter f_test.

    Notes
    -----
    TODO: convert to class and attach results properly

    The Null hypothesis for grangercausalitytests is that the time series in
    the second column, x2, does NOT Granger cause the time series in the first
    column, x1. Grange causality means that past values of x2 have a
    statistically significant effect on the current value of x1, taking past
    values of x1 into account as regressors. We reject the null hypothesis
    that x2 does not Granger cause x1 if the pvalues are below a desired size
    of the test.

    The null hypothesis for all four test is that the coefficients
    corresponding to past values of the second time series are zero.

    'params_ftest', 'ssr_ftest' are based on F distribution

    'ssr_chi2test', 'lrtest' are based on chi-square distribution

    References
    ----------
    http://en.wikipedia.org/wiki/Granger_causality
    Greene: Econometric Analysis

    """
    from scipy import stats

    x = np.asarray(x)

    if x.shape[0] <= 3 * maxlag + int(addconst):
        raise ValueError("Insufficient observations. Maximum allowable "
                         "lag is {0}".format(int((x.shape[0] - int(addconst)) /
                                                 3) - 1))

    resli = {}

    for mlg in range(1, maxlag + 1):
        result = {}
        if verbose:
            print('\nGranger Causality')
            print('number of lags (no zero)', mlg)
        mxlg = mlg

        # create lagmat of both time series
        dta = lagmat2ds(x, mxlg, trim='both', dropex=1)

        #add constant
        if addconst:
            dtaown = add_constant(dta[:, 1:(mxlg + 1)], prepend=False)
            dtajoint = add_constant(dta[:, 1:], prepend=False)
        else:
            raise NotImplementedError('Not Implemented')
            #dtaown = dta[:, 1:mxlg]
            #dtajoint = dta[:, 1:]

        # Run ols on both models without and with lags of second variable
        res2down = OLS(dta[:, 0], dtaown).fit()
        res2djoint = OLS(dta[:, 0], dtajoint).fit()

        #print results
        #for ssr based tests see:
        #http://support.sas.com/rnd/app/examples/ets/granger/index.htm
        #the other tests are made-up

        # Granger Causality test using ssr (F statistic)
        fgc1 = ((res2down.ssr - res2djoint.ssr) /
                res2djoint.ssr / mxlg * res2djoint.df_resid)
        if verbose:
            print('ssr based F test:         F=%-8.4f, p=%-8.4f, df_denom=%d,'
                   ' df_num=%d' % (fgc1,
                                    stats.f.sf(fgc1, mxlg,
                                               res2djoint.df_resid),
                                    res2djoint.df_resid, mxlg))
#.........这里部分代码省略.........

# 需要导入模块: from statsmodels.regression.linear_model import OLS [as 别名]
# 或者: from statsmodels.regression.linear_model.OLS import f_test [as 别名]
def acorr_breusch_godfrey(results, nlags=None, store=False):
    '''Breusch Godfrey Lagrange Multiplier tests for residual autocorrelation

    Parameters
    ----------
    results : Result instance
        Estimation results for which the residuals are tested for serial
        correlation
    nlags : int
        Number of lags to include in the auxiliary regression. (nlags is
        highest lag)
    store : bool
        If store is true, then an additional class instance that contains
        intermediate results is returned.

    Returns
    -------
    lm : float
        Lagrange multiplier test statistic
    lmpval : float
        p-value for Lagrange multiplier test
    fval : float
        fstatistic for F test, alternative version of the same test based on
        F test for the parameter restriction
    fpval : float
        pvalue for F test
    resstore : instance (optional)
        a class instance that holds intermediate results. Only returned if
        store=True

    Notes
    -----
    BG adds lags of residual to exog in the design matrix for the auxiliary
    regression with residuals as endog,
    see Greene 12.7.1.

    References
    ----------
    Greene Econometrics, 5th edition

    '''

    x = np.asarray(results.resid)
    exog_old = results.model.exog
    nobs = x.shape[0]
    if nlags is None:
        #for adf from Greene referencing Schwert 1989
        nlags = np.trunc(12. * np.power(nobs/100., 1/4.))#nobs//4  #TODO: check default, or do AIC/BIC
        nlags = int(nlags)

    x = np.concatenate((np.zeros(nlags), x))

    #xdiff = np.diff(x)
    #
    xdall = lagmat(x[:,None], nlags, trim='both')
    nobs = xdall.shape[0]
    xdall = np.c_[np.ones((nobs,1)), xdall]
    xshort = x[-nobs:]
    exog = np.column_stack((exog_old, xdall))
    k_vars = exog.shape[1]

    if store: resstore = ResultsStore()

    resols = OLS(xshort, exog).fit()
    ft = resols.f_test(np.eye(nlags, k_vars, k_vars - nlags))
    fval = ft.fvalue
    fpval = ft.pvalue
    fval = np.squeeze(fval)[()]   #TODO: fix this in ContrastResults
    fpval = np.squeeze(fpval)[()]
    lm = nobs * resols.rsquared
    lmpval = stats.chi2.sf(lm, nlags)
    # Note: degrees of freedom for LM test is nvars minus constant = usedlags
    #return fval, fpval, lm, lmpval

    if store:
        resstore.resols = resols
        resstore.usedlag = nlags
        return lm, lmpval, fval, fpval, resstore
    else:
        return lm, lmpval, fval, fpval

# 需要导入模块: from statsmodels.regression.linear_model import OLS [as 别名]
# 或者: from statsmodels.regression.linear_model.OLS import f_test [as 别名]
def hacked_gct (x, maxlag, addconst=True, verbose=True):

    #from scipy import stats

    x = np.asarray(x)

    if x.shape[0] <= 3 * maxlag + int(addconst):
        raise ValueError("Insufficient observations. Maximum allowable "
                         "lag is {0}".format(int((x.shape[0] - int(addconst)) /
                                                 3) - 1))

    resli = {}

    for mlg in range(1, maxlag + 1):
        result = {}
        if verbose:
            print('\nGranger Causality')
            print('number of lags (no zero)', mlg)
        mxlg = mlg

        # create lagmat of both time series
        dta = lagmat2ds(x, mxlg, trim='both', dropex=1)

        #add constant
        if addconst:
            '''dtaown = add_constant(dta[:, 1:(mxlg + 1)], prepend=False)'''
            dtajoint = add_constant(dta[:, 1:], prepend=False)
        else:
            raise NotImplementedError('Not Implemented')
            #dtaown = dta[:, 1:mxlg]
            #dtajoint = dta[:, 1:]

        # Run ols on both models without and with lags of second variable
        '''res2down = OLS(dta[:, 0], dtaown).fit()'''
        res2down = 'skipped'
        res2djoint = OLS(dta[:, 0], dtajoint).fit()

        #print results
        #for ssr based tests see:
        #http://support.sas.com/rnd/app/examples/ets/granger/index.htm
        #the other tests are made-up
        '''
        # Granger Causality test using ssr (F statistic)
        fgc1 = ((res2down.ssr - res2djoint.ssr) /
                res2djoint.ssr / mxlg * res2djoint.df_resid)
        if verbose:
            print('ssr based F test:         F=%-8.4f, p=%-8.4f, df_denom=%d,'
                   ' df_num=%d' % (fgc1,
                                    stats.f.sf(fgc1, mxlg,
                                               res2djoint.df_resid),
                                    res2djoint.df_resid, mxlg))
        result['ssr_ftest'] = (fgc1,
                               stats.f.sf(fgc1, mxlg, res2djoint.df_resid),
                               res2djoint.df_resid, mxlg)

        # Granger Causality test using ssr (ch2 statistic)
        fgc2 = res2down.nobs * (res2down.ssr - res2djoint.ssr) / res2djoint.ssr
        if verbose:
            print('ssr based chi2 test:   chi2=%-8.4f, p=%-8.4f, '
                   'df=%d' % (fgc2, stats.chi2.sf(fgc2, mxlg), mxlg))
        result['ssr_chi2test'] = (fgc2, stats.chi2.sf(fgc2, mxlg), mxlg)

        #likelihood ratio test pvalue:
        lr = -2 * (res2down.llf - res2djoint.llf)
        if verbose:
            print('likelihood ratio test: chi2=%-8.4f, p=%-8.4f, df=%d' %
                   (lr, stats.chi2.sf(lr, mxlg), mxlg))
        result['lrtest'] = (lr, stats.chi2.sf(lr, mxlg), mxlg)
        '''
        # F test that all lag coefficients of exog are zero
        rconstr = np.column_stack((np.zeros((mxlg, mxlg)),
                                   np.eye(mxlg, mxlg),
                                   np.zeros((mxlg, 1))))
        ftres = res2djoint.f_test(rconstr)
        if verbose:
            print('parameter F test:         F=%-8.4f, p=%-8.4f, df_denom=%d,'
                   ' df_num=%d' % (ftres.fvalue, ftres.pvalue, ftres.df_denom,
                                    ftres.df_num))
        result['params_ftest'] = (np.squeeze(ftres.fvalue)[()],
                                  np.squeeze(ftres.pvalue)[()],
                                  ftres.df_denom, ftres.df_num)

        resli[mxlg] = (result, [res2down, res2djoint, rconstr])

    return resli

# 需要导入模块: from statsmodels.regression.linear_model import OLS [as 别名]
# 或者: from statsmodels.regression.linear_model.OLS import f_test [as 别名]
def grangercausalitytests(x, maxlag, addconst=True, verbose=True):
    '''four tests for granger causality of 2 timeseries

    all four tests give similar results
    `params_ftest` and `ssr_ftest` are equivalent based of F test which is
    identical to lmtest:grangertest in R

    Parameters
    ----------
    x : array, 2d, (nobs,2)
        data for test whether the time series in the second column Granger
        causes the time series in the first column
    maxlag : integer
        the Granger causality test results are calculated for all lags up to
        maxlag
    verbose : bool
        print results if true

    Returns
    -------
    results : dictionary
        all test results, dictionary keys are the number of lags. For each
        lag the values are a tuple, with the first element a dictionary with
        teststatistic, pvalues, degrees of freedom, the second element are
        the OLS estimation results for the restricted model, the unrestricted
        model and the restriction (contrast) matrix for the parameter f_test.

    Notes
    -----
    TODO: convert to class and attach results properly

    The Null hypothesis for grangercausalitytests is that the time series in
    the second column, x2, Granger causes the time series in the first column,
    x1. This means that past values of x2 have a statistically significant
    effect on the current value of x1, taking also past values of x1 into
    account, as regressors. We reject the null hypothesis of x2 Granger
    causing x1 if the pvalues are below a desired size of the test.

    'params_ftest', 'ssr_ftest' are based on F test

    'ssr_chi2test', 'lrtest' are based on chi-square test

    '''
    from scipy import stats # lazy import

    resli = {}

    for mlg in range(1, maxlag+1):
        result = {}
        if verbose:
            print '\nGranger Causality'
            print 'number of lags (no zero)', mlg
        mxlg = mlg #+ 1 # Note number of lags starting at zero in lagmat

        # create lagmat of both time series
        dta = lagmat2ds(x, mxlg, trim='both', dropex=1)

        #add constant
        if addconst:
            dtaown = add_constant(dta[:,1:mxlg+1])
            dtajoint = add_constant(dta[:,1:])
        else:
            raise ValueError('Not Implemented')
            dtaown = dta[:,1:mxlg]
            dtajoint = dta[:,1:]

        #run ols on both models without and with lags of second variable
        res2down = OLS(dta[:,0], dtaown).fit()
        res2djoint = OLS(dta[:,0], dtajoint).fit()

        #print results
        #for ssr based tests see: http://support.sas.com/rnd/app/examples/ets/granger/index.htm
        #the other tests are made-up

        # Granger Causality test using ssr (F statistic)
        fgc1 = (res2down.ssr-res2djoint.ssr)/res2djoint.ssr/(mxlg)*res2djoint.df_resid
        if verbose:
            print 'ssr based F test:         F=%-8.4f, p=%-8.4f, df_denom=%d, df_num=%d' % \
              (fgc1, stats.f.sf(fgc1, mxlg, res2djoint.df_resid), res2djoint.df_resid, mxlg)
        result['ssr_ftest'] = (fgc1, stats.f.sf(fgc1, mxlg, res2djoint.df_resid), res2djoint.df_resid, mxlg)

        # Granger Causality test using ssr (ch2 statistic)
        fgc2 = res2down.nobs*(res2down.ssr-res2djoint.ssr)/res2djoint.ssr
        if verbose:
            print 'ssr based chi2 test:   chi2=%-8.4f, p=%-8.4f, df=%d' %  \
              (fgc2, stats.chi2.sf(fgc2, mxlg), mxlg)
        result['ssr_chi2test'] = (fgc2, stats.chi2.sf(fgc2, mxlg), mxlg)

        #likelihood ratio test pvalue:
        lr = -2*(res2down.llf-res2djoint.llf)
        if verbose:
            print 'likelihood ratio test: chi2=%-8.4f, p=%-8.4f, df=%d' %  \
              (lr, stats.chi2.sf(lr, mxlg), mxlg)
        result['lrtest'] = (lr, stats.chi2.sf(lr, mxlg), mxlg)

        # F test that all lag coefficients of exog are zero
        rconstr = np.column_stack((np.zeros((mxlg-1,mxlg-1)), np.eye(mxlg-1, mxlg-1),\
                                   np.zeros((mxlg-1, 1))))
        rconstr = np.column_stack((np.zeros((mxlg,mxlg)), np.eye(mxlg, mxlg),\
                                   np.zeros((mxlg, 1))))
#.........这里部分代码省略.........

# 需要导入模块: from statsmodels.regression.linear_model import OLS [as 别名]
# 或者: from statsmodels.regression.linear_model.OLS import f_test [as 别名]
def grangercausalitytests_mod(x, maxlag, addconst=True, verbose=True):
    
    import numpy as np
    from scipy import stats
    from statsmodels.tsa.tsatools import lagmat2ds
    from statsmodels.tools.tools import add_constant
    from statsmodels.regression.linear_model import OLS
    from warnings import warn

    x = np.asarray(x)

    if x.shape[0] <= 3 * maxlag + int(addconst):
        warn("Insufficient observations. Maximum allowable lag is {0}."
             "The maximum lag will be set to "
             "this number".format(int((x.shape[0] - int(addconst)) / 3) - 1))
        maxlag = int((x.shape[0] - int(addconst)) /  3) - 1
#    print(x.shape[0])
#    print(int((x.shape[0] - int(addconst)) /  3) - 1)
#    print(maxlag)

    resli = {}

    for mlg in range(1, maxlag + 1):
        
        result = {}
        if verbose:
            print('\nGranger Causality')
            print('number of lags (no zero)', mlg)
        mxlg = mlg

        # create lagmat of both time series
        dta = lagmat2ds(x, mxlg, trim='both')
        dta = np.delete(dta, -1, axis = 1) # removal of the not lagged xs

        #add constant
        if addconst:
            dtaown = add_constant(dta[:, 1:(mxlg + 1)], prepend=False)
            dtajoint = add_constant(dta[:, 1:], prepend=False)
        else:
            raise NotImplementedError('Not Implemented')
            #dtaown = dta[:, 1:mxlg]
            #dtajoint = dta[:, 1:]

        # Run ols on both models without and with lags of second variable
        res2down = OLS(dta[:, 0], dtaown).fit()
        res2djoint = OLS(dta[:, 0], dtajoint).fit()

        #print results
        #for ssr based tests see:
        #http://support.sas.com/rnd/app/examples/ets/granger/index.htm
        #the other tests are made-up

        # Granger Causality test using ssr (F statistic)
        fgc1 = ((res2down.ssr - res2djoint.ssr) /
                res2djoint.ssr / mxlg * res2djoint.df_resid)
        if verbose:
            print('ssr based F test:         F=%-8.4f, p=%-8.4f, df_denom=%d,'
                   ' df_num=%d' % (fgc1,
                                    stats.f.sf(fgc1, mxlg,
                                               res2djoint.df_resid),
                                    res2djoint.df_resid, mxlg))
        result['ssr_ftest'] = (fgc1,
                               stats.f.sf(fgc1, mxlg, res2djoint.df_resid),
                               res2djoint.df_resid, mxlg)

        # Granger Causality test using ssr (ch2 statistic)
        fgc2 = res2down.nobs * (res2down.ssr - res2djoint.ssr) / res2djoint.ssr
        if verbose:
            print('ssr based chi2 test:   chi2=%-8.4f, p=%-8.4f, '
                   'df=%d' % (fgc2, stats.chi2.sf(fgc2, mxlg), mxlg))
        result['ssr_chi2test'] = (fgc2, stats.chi2.sf(fgc2, mxlg), mxlg)

        #likelihood ratio test pvalue:
        lr = -2 * (res2down.llf - res2djoint.llf)
        if verbose:
            print('likelihood ratio test: chi2=%-8.4f, p=%-8.4f, df=%d' %
                   (lr, stats.chi2.sf(lr, mxlg), mxlg))
        result['lrtest'] = (lr, stats.chi2.sf(lr, mxlg), mxlg)

        # F test that all lag coefficients of exog are zero
        rconstr = np.column_stack((np.zeros((mxlg, mxlg)),
                                   np.eye(mxlg, mxlg),
                                   np.zeros((mxlg, 1))))
        ftres = res2djoint.f_test(rconstr)
        if verbose:
            print('parameter F test:         F=%-8.4f, p=%-8.4f, df_denom=%d,'
                   ' df_num=%d' % (ftres.fvalue, ftres.pvalue, ftres.df_denom,
                                    ftres.df_num))
        result['params_ftest'] = (np.squeeze(ftres.fvalue)[()],
                                  np.squeeze(ftres.pvalue)[()],
                                  ftres.df_denom, ftres.df_num)

        resli[mxlg] = (result, [res2down, res2djoint, rconstr])
    return resli

# 需要导入模块: from statsmodels.regression.linear_model import OLS [as 别名]
# 或者: from statsmodels.regression.linear_model.OLS import f_test [as 别名]
 def setupClass(cls):
     data = longley.load()
     data.exog = add_constant(data.exog)
     res1 = OLS(data.endog, data.exog).fit()
     R2 = [[0,1,-1,0,0,0,0],[0, 0, 0, 0, 1, -1, 0]]
     cls.Ftest1 = res1.f_test(R2)