

def unconstrain_stationary_multivariate(constrained, error_variance):
    '\n    Transform constrained parameters used in likelihood evaluation\n    to unconstrained parameters used by the optimizer\n\n    Parameters\n    ----------\n    constrained : array or list\n        Constrained parameters of, e.g., an autoregressive or moving average\n        component, to be transformed to arbitrary parameters used by the\n        optimizer. If a list, should be a list of length `order`, where each\n        element is an array sized `k_endog` x `k_endog`. If an array, should be\n        the coefficient matrices horizontally concatenated and sized\n        `k_endog` x `k_endog * order`.\n    error_variance : array\n        The variance / covariance matrix of the error term. Should be sized\n        `k_endog` x `k_endog`. This is used as input in the algorithm even if\n        is not transformed by it (when `transform_variance` is False).\n\n    Returns\n    -------\n    unconstrained : array\n        Unconstrained parameters used by the optimizer, to be transformed to\n        stationary coefficients of, e.g., an autoregressive or moving average\n        component. Will match the type of the passed `constrained`\n        variable (so if a list was passed, a list will be returned).\n\n    Notes\n    -----\n    Uses the list representation internally, even if an array is passed.\n\n    References\n    ----------\n    .. [1] Ansley, Craig F., and Robert Kohn. 1986.\n       "A Note on Reparameterizing a Vector Autoregressive Moving Average Model\n       to Enforce Stationarity."\n       Journal of Statistical Computation and Simulation 24 (2): 99-106.\n\n    '
    from scipy import linalg
    use_list = (type(constrained) == list)
    if (not use_list):
        (k_endog, order) = constrained.shape
        order //= k_endog
        constrained = [constrained[:k_endog, (i * k_endog):((i + 1) * k_endog)] for i in range(order)]
    else:
        order = len(constrained)
        k_endog = constrained[0].shape[0]
    partial_autocorrelations = _compute_multivariate_pacf_from_coefficients(constrained, error_variance, order, k_endog)
    unconstrained = _unconstrain_sv_less_than_one(partial_autocorrelations, unconstrained, k_endog)
    if (not use_list):
        unconstrained = np.concatenate(unconstrained, axis=1)
    return (unconstrained, error_variance)

dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/tsa/statespace/tools.py unconstrain_stationary_multivariate/VarMisuse@129/803 `order`->`unconstrained`