From 6b1cc78fbc8c34f0d7555aaf3168323f631cb4b3 Mon Sep 17 00:00:00 2001 From: Kevin Sheppard Date: Wed, 1 Apr 2020 17:34:39 +0100 Subject: [PATCH] More work and tests --- arch/conftest.py | 1 + arch/covariance/kernel.py | 29 + arch/covariance/var.py | 103 +- arch/tests/covariance/covariance_data.py | 45 + arch/tests/covariance/test_var.py | 116 +- arch/tests/unitroot/test_dynamic_ols.py | 24 +- arch/tests/unitroot/test_fmols_ccr.py | 12 +- arch/tests/unitroot/test_phillips_ouliaris.py | 63 + arch/unitroot/cointegration.py | 16 +- .../critical_values/phillips_ouliaris.py | 2326 ++++++++--------- doc/source/covariance/covariance.rst | 26 +- 11 files changed, 1507 insertions(+), 1254 deletions(-) create mode 100644 arch/tests/covariance/covariance_data.py diff --git a/arch/conftest.py b/arch/conftest.py index d5ebbad541..972b49c713 100644 --- a/arch/conftest.py +++ b/arch/conftest.py @@ -2,6 +2,7 @@ pytest_plugins = [ "arch.tests.unitroot.cointegration_data", + "arch.tests.covariance.covariance_data", ] diff --git a/arch/covariance/kernel.py b/arch/covariance/kernel.py index 43d37ba68b..9a517d7376 100644 --- a/arch/covariance/kernel.py +++ b/arch/covariance/kernel.py @@ -25,6 +25,7 @@ "Gallant", "NeweyWest", "normalize_kernel_name", + "ZeroLag", ] KERNELS = [ @@ -40,6 +41,7 @@ "Andrews", "Gallant", "NeweyWest", + "ZeroLag", ] @@ -189,6 +191,7 @@ class CovarianceEstimator(ABC): def __init__( self, x: ArrayLike, + *, bandwidth: Optional[float] = None, df_adjust: int = 0, center: bool = True, @@ -716,3 +719,29 @@ class NeweyWest(Bartlett): -------- Bartlett """ + + +zero_lag_name = "Zero-lag (No autocorrelation)" +zero_lag_formula = """\ +w= 1 & z=0\\\\ \ +\\ 0 & z>0 \ +\\end{cases} \ +""" + + +@Substitution(kernel_name=zero_lag_name, formula=zero_lag_formula) +class ZeroLag(CovarianceEstimator, metaclass=AbstractDocStringInheritor): + @property + def kernel_const(self) -> float: + return 1.0 + + @property + def bandwidth_scale(self) -> float: + return 0.0 + + @property + def rate(self) -> float: + return 0.0 + + def _weights(self) -> NDArray: + return np.ones(1) diff --git a/arch/covariance/var.py b/arch/covariance/var.py index 713c14d153..b898b8ceb4 100644 --- a/arch/covariance/var.py +++ b/arch/covariance/var.py @@ -1,9 +1,9 @@ from typing import Dict, NamedTuple, Optional, Tuple import numpy as np -from numpy import zeros from numpy.linalg import lstsq import pandas as pd +from pandas.util._decorators import Appender from statsmodels.tools import add_constant from statsmodels.tsa.tsatools import lagmat @@ -14,6 +14,9 @@ normalize_kernel_name, ) from arch.typing import ArrayLike, NDArray +from arch.vendor import cached_property + +__all__ = ["PreWhitenedRecolored"] class VARModel(NamedTuple): @@ -23,48 +26,98 @@ class VARModel(NamedTuple): intercept: bool -class PreWhitenRecoloredCovariance(CovarianceEstimator): +class PreWhitenedRecolored(CovarianceEstimator): """ + VAR-HAC and Pre-Whitened-Recolored Long-run covariance estimation. + + Andrews & Monahan [1]_ PWRC and DenHaan-Levin's VAR-HAC [2]_ covariance + estimators. + Parameters ---------- x : array_like + The data to use in covariance estimation. lags : int, default None + The number of lags to include in the VAR. If None, a specification + search is used to select the order. method : {"aic", "hqc", "bic"}, default "aic" + The information criteria to use in the model specification search. diagonal : bool, default True + Flag indicating to consider both diagonal parameter coefficient + matrices on lags. A diagonal coefficient matrix restricts all + off-diagonal coefficient to be zero. max_lag : int, default None + The maximum lag to use in the model specification search. If None, + then nobs**(1/3) is used. sample_autocov : bool, default False - kernel : str, default "bartlett" + Whether to the the same autocovariance or the theoretical + autocovariance implied by the estimated VAR when computing + the long-run covairance. + kernel : {str, None}, default "bartlett". + The name of the kernel to use. Can be any available kernel. Input + is normalised using lower casing and any underscores or hyphens + are removed, so that "QuadraticSpectral", "quadratic-spectral" and + "quadratic_spectral" are all the same. Use None to prevent recoloring. bandwidth : float, default None + The kernel's bandwidth. If None, optimal bandwidth is estimated. df_adjust : int, default 0 + Degrees of freedom to remove when adjusting the covariance. Uses the + number of observations in x minus df_adjust when dividing + inner-products. center : bool, default True + A flag indicating whether x should be demeaned before estimating the + covariance. weights : array_like, default None + An array of weights used to combine when estimating optimal bandwidth. + If not provided, a vector of 1s is used. Must have nvar elements. + force_int : bool, default False + Force bandwidth to be an integer. See Also -------- + arch.covariance.kernel + Kernel-based long-run covariance estimators Notes ----- + TODO: Add detailed notes Examples -------- + + References + ---------- + .. [1] Andrews, D. W., & Monahan, J. C. (1992). An improved + heteroskedasticity and autocorrelation consistent covariance matrix + estimator. Econometrica: Journal of the Econometric Society, 953-966. + .. [2] Haan, W. J. D., & Levin, A. T. (2000). Robust covariance matrix + estimation with data-dependent VAR prewhitening order (No. 255). + National Bureau of Economic Research. """ def __init__( self, x: ArrayLike, + *, lags: Optional[int] = None, method: str = "aic", diagonal: bool = True, max_lag: Optional[int] = None, sample_autocov: bool = False, - kernel: str = "bartlett", + kernel: Optional[str] = "bartlett", bandwidth: Optional[float] = None, df_adjust: int = 0, center: bool = True, weights: Optional[ArrayLike] = None, + force_int: bool = False, ) -> None: super().__init__( - x, bandwidth=bandwidth, df_adjust=df_adjust, center=center, weights=weights + x, + bandwidth=bandwidth, + df_adjust=df_adjust, + center=center, + weights=weights, + force_int=force_int, ) self._kernel_name = kernel self._lags = 0 @@ -75,7 +128,13 @@ def __init__( self._auto_lag_selection = True self._format_lags(lags) self._sample_autocov = sample_autocov - kernel = normalize_kernel_name(kernel) + if kernel is not None: + kernel = normalize_kernel_name(kernel) + else: + if self._bandwidth not in (0, None): + raise ValueError("bandwidth must be None when kernel is None") + self._bandwidth = None + kernel = "zerolag" if kernel not in KERNEL_ESTIMATORS: raise ValueError(KERNEL_ERR) @@ -155,7 +214,7 @@ def _ic_from_vars( ics: Dict[Tuple[int, int], float] = { (full_order, full_order): self._ic(sigma, nparam, nobs) } - if not self._diagonal: + if not self._diagonal or self._x.shape[1] == 1: return ics purged_indiv_lags = np.empty((nvar, nobs, max_lag - full_order)) @@ -206,7 +265,7 @@ def _select_lags(self) -> Tuple[int, int]: return models[ic.argmin()] def _estimate_var(self, full_order: int, diag_order: int) -> VARModel: - nobs, nvar = self._x.shape + nvar = self._x.shape[1] center = int(self._center) max_lag = max(full_order, diag_order) lhs, rhs, extra_lags = self._setup_model_data(max_lag) @@ -214,7 +273,7 @@ def _estimate_var(self, full_order: int, diag_order: int) -> VARModel: rhs = rhs[:, : c + full_order * nvar] extra_lags = extra_lags[:, :, full_order:diag_order] - params = zeros((nvar, nvar * max_lag + center)) + params = np.zeros((nvar, nvar * max_lag + center)) resids = np.empty_like(lhs) ncommon = rhs.shape[1] for i in range(nvar): @@ -246,8 +305,8 @@ def _estimate_sample_cov(self, nvar: int, nlag: int) -> NDArray: if self._center: x = x - x.mean(0) nobs = x.shape[0] - var_cov = zeros((nvar * nlag, nvar * nlag)) - gamma = zeros((nlag, nvar, nvar)) + var_cov = np.zeros((nvar * nlag, nvar * nlag)) + gamma = np.zeros((nlag, nvar, nvar)) for i in range(nlag): gamma[i] = (x[i:].T @ x[: (nobs - i)]) / nobs for r in range(nlag): @@ -258,10 +317,11 @@ def _estimate_sample_cov(self, nvar: int, nlag: int) -> NDArray: var_cov[r * nvar : (r + 1) * nvar, c * nvar : (c + 1) * nvar] = g return var_cov + @staticmethod def _estimate_model_cov( - self, nvar: int, nlag: int, coeffs: NDArray, short_run: NDArray + nvar: int, nlag: int, coeffs: NDArray, short_run: NDArray ) -> NDArray: - sigma = zeros((nvar * nlag, nvar * nlag)) + sigma = np.zeros((nvar * nlag, nvar * nlag)) sigma[:nvar, :nvar] = short_run multiplier = np.linalg.inv(np.eye(coeffs.size) - np.kron(coeffs, coeffs)) vec_sigma = sigma.ravel()[:, None] @@ -274,7 +334,7 @@ def _companion_form( ) -> Tuple[NDArray, NDArray]: nvar = var_model.resids.shape[1] nlag = var_model.var_order - coeffs = zeros((nvar * nlag, nvar * nlag)) + coeffs = np.zeros((nvar * nlag, nvar * nlag)) coeffs[:nvar] = var_model.params[:, var_model.intercept :] for i in range(nlag - 1): coeffs[(i + 1) * nvar : (i + 2) * nvar, i * nvar : (i + 1) * nvar] = np.eye( @@ -286,7 +346,8 @@ def _companion_form( var_cov = self._estimate_model_cov(nvar, nlag, coeffs, short_run) return coeffs, var_cov - @property + @cached_property + @Appender(CovarianceEstimator.cov.__doc__) def cov(self) -> CovarianceEstimate: common, individual = self._select_lags() self._order = (common, individual) @@ -294,7 +355,12 @@ def cov(self) -> CovarianceEstimate: resids = var_mod.resids nobs, nvar = resids.shape self._kernel_instance = self._kernel( - resids, self._bandwidth, 0, False, self._x_weights, self._force_int + resids, + bandwidth=self._bandwidth, + df_adjust=0, + center=False, + weights=self._x_weights, + force_int=self._force_int, ) kern_cov = self._kernel_instance.cov short_run = kern_cov.short_run @@ -316,7 +382,7 @@ def cov(self) -> CovarianceEstimate: have diagonal coefficient matrices. The maximum eigenvalue of the companion-form \ VAR(1) coefficient matrix is {max_eig}.""" ) - coeff_sum = zeros((nvar, nvar)) + coeff_sum = np.zeros((nvar, nvar)) params = var_mod.params[:, var_mod.intercept :] for i in range(var_mod.var_order): coeff_sum += params[:, i * nvar : (i + 1) * nvar] @@ -343,7 +409,8 @@ def cov(self) -> CovarianceEstimate: def _ensure_kernel_instantized(self) -> None: if self._kernel_instance is None: - self.cov + # Workaround to avoid linting noise + getattr(self, "cov") @property def bandwidth_scale(self) -> float: diff --git a/arch/tests/covariance/covariance_data.py b/arch/tests/covariance/covariance_data.py new file mode 100644 index 0000000000..ca98a64abc --- /dev/null +++ b/arch/tests/covariance/covariance_data.py @@ -0,0 +1,45 @@ +from itertools import product + +import numpy as np +import pandas as pd +import pytest + +DATA_PARAMS = list(product([1, 3], [True, False], [0, 1, 3])) +DATA_IDS = [f"dim: {d}, pandas: {p}, order: {o}" for d, p, o in DATA_PARAMS] + + +@pytest.fixture(scope="module", params=DATA_PARAMS, ids=DATA_IDS) +def covariance_data(request): + dim, pandas, order = request.param + rs = np.random.RandomState([839084, 3823810, 982103, 829108]) + burn = 100 + shape = (burn + 500,) + if dim > 1: + shape += (3,) + rvs = rs.standard_normal(shape) + phi = np.zeros((order, dim, dim)) + if order > 0: + phi[0] = np.eye(dim) * 0.4 + 0.1 + for i in range(1, order): + phi[i] = 0.3 / (i + 1) * np.eye(dim) + for i in range(order, burn + 500): + for j in range(order): + if dim == 1: + rvs[i] += np.squeeze(phi[j] * rvs[i - j - 1]) + else: + rvs[i] += phi[j] @ rvs[i - j - 1] + if order > 1: + p = np.eye(dim * order, dim * order, -dim) + for j in range(order): + p[:dim, j * dim : (j + 1) * dim] = phi[j] + v, _ = np.linalg.eig(p) + assert np.max(np.abs(v)) < 1 + rvs = rvs[burn:] + if pandas and dim == 1: + return pd.Series(rvs, name="x") + elif pandas: + df = pd.DataFrame(rvs, columns=[f"x{i}" for i in range(dim)]) + df.to_csv(f"cov-data-order-{order}.csv") + return df + + return rvs diff --git a/arch/tests/covariance/test_var.py b/arch/tests/covariance/test_var.py index 6fecae1738..45aedc03bb 100644 --- a/arch/tests/covariance/test_var.py +++ b/arch/tests/covariance/test_var.py @@ -1,4 +1,3 @@ -from itertools import product from typing import Optional, Tuple import numpy as np @@ -6,11 +5,10 @@ import pandas as pd import pytest -from arch.covariance.var import PreWhitenRecoloredCovariance +from arch.covariance.kernel import CovarianceEstimate +from arch.covariance.var import PreWhitenedRecolored from arch.typing import NDArray -DATA_PARAMS = list(product([1, 3], [True, False], [0])) # , 1, 3])) -DATA_IDS = [f"dim: {d}, pandas: {p}, order: {o}" for d, p, o in DATA_PARAMS] KERNELS = [ "Bartlett", "Parzen", @@ -32,40 +30,6 @@ def kernel(request): return request.param -@pytest.fixture(scope="module", params=DATA_PARAMS, ids=DATA_IDS) -def data(request): - dim, pandas, order = request.param - rs = np.random.RandomState([839084, 3823810, 982103, 829108]) - burn = 100 - shape = (burn + 500,) - if dim > 1: - shape += (3,) - rvs = rs.standard_normal(shape) - phi = np.zeros((order, dim, dim)) - if order > 0: - phi[0] = np.eye(dim) * 0.4 + 0.1 - for i in range(1, order): - phi[i] = 0.3 / (i + 1) * np.eye(dim) - for i in range(order, burn + 500): - for j in range(order): - if dim == 1: - rvs[i] += np.squeeze(phi[j] * rvs[i - j - 1]) - else: - rvs[i] += phi[j] @ rvs[i - j - 1] - if order > 1: - p = np.eye(dim * order, dim * order, -dim) - for j in range(order): - p[:dim, j * dim : (j + 1) * dim] = phi[j] - v, _ = np.linalg.eig(p) - assert np.max(np.abs(v)) < 1 - rvs = rvs[burn:] - if pandas and dim == 1: - return pd.Series(rvs, name="x") - elif pandas: - return pd.DataFrame(rvs, columns=[f"x{i}" for i in range(dim)]) - return rvs - - def direct_var( x, const: bool, full_order: int, diag_order: int, max_order: Optional[int] = None ) -> Tuple[NDArray, NDArray]: @@ -140,29 +104,38 @@ def direct_ic( @pytest.mark.parametrize("diag_order", [3, 5]) @pytest.mark.parametrize("max_order", [None, 10]) @pytest.mark.parametrize("ic", ["aic", "bic", "hqc"]) -def test_direct_var(data, const, full_order, diag_order, max_order, ic): - direct_ic(data, ic, const, full_order, diag_order, max_order) +def test_direct_var(covariance_data, const, full_order, diag_order, max_order, ic): + direct_ic(covariance_data, ic, const, full_order, diag_order, max_order) @pytest.mark.parametrize("center", [True, False]) @pytest.mark.parametrize("diagonal", [True, False]) @pytest.mark.parametrize("method", ["aic", "bic", "hqc"]) -def test_ic(data, center, diagonal, method): - pwrc = PreWhitenRecoloredCovariance( - data, center=center, diagonal=diagonal, method=method, bandwidth=0.0, +def test_ic(covariance_data, center, diagonal, method): + pwrc = PreWhitenedRecolored( + covariance_data, center=center, diagonal=diagonal, method=method, bandwidth=0.0, ) cov = pwrc.cov - expected_type = np.ndarray if isinstance(data, np.ndarray) else pd.DataFrame + expected_type = ( + np.ndarray if isinstance(covariance_data, np.ndarray) else pd.DataFrame + ) assert isinstance(cov.short_run, expected_type) - expected_max_lag = int(data.shape[0] ** (1 / 3)) + expected_max_lag = int(covariance_data.shape[0] ** (1 / 3)) assert pwrc._max_lag == expected_max_lag expected_ics = {} for full_order in range(expected_max_lag + 1): diag_limit = expected_max_lag + 1 if diagonal else full_order + 1 + if covariance_data.ndim == 1 or covariance_data.shape[1] == 1: + diag_limit = full_order + 1 for diag_order in range(full_order, diag_limit): key = (full_order, diag_order) expected_ics[key] = direct_ic( - data, method, center, full_order, diag_order, max_order=expected_max_lag + covariance_data, + method, + center, + full_order, + diag_order, + max_order=expected_max_lag, ) assert tuple(sorted(pwrc._ics.keys())) == tuple(sorted(expected_ics.keys())) for key in expected_ics: @@ -175,13 +148,18 @@ def test_ic(data, center, diagonal, method): @pytest.mark.parametrize("diagonal", [True, False]) @pytest.mark.parametrize("method", ["aic", "bic", "hqc"]) @pytest.mark.parametrize("lags", [0, 1, 3]) -def test_short_long_run(data, center, diagonal, method, lags): - pwrc = PreWhitenRecoloredCovariance( - data, center=center, diagonal=diagonal, method=method, lags=lags, bandwidth=0.0, +def test_short_long_run(covariance_data, center, diagonal, method, lags): + pwrc = PreWhitenedRecolored( + covariance_data, + center=center, + diagonal=diagonal, + method=method, + lags=lags, + bandwidth=0.0, ) cov = pwrc.cov full_order, diag_order = pwrc._order - params, resids = direct_var(data, center, full_order, diag_order) + params, resids = direct_var(covariance_data, center, full_order, diag_order) nobs, nvar = resids.shape expected_short_run = resids.T @ resids / nobs assert_allclose(cov.short_run, expected_short_run) @@ -195,25 +173,47 @@ def test_short_long_run(data, center, diagonal, method, lags): assert_allclose(cov.long_run, expected_long_run) +@pytest.mark.parametrize("force_int", [True, False]) +def test_pwrc_attributes(covariance_data, force_int): + pwrc = PreWhitenedRecolored(covariance_data, force_int=force_int) + assert isinstance(pwrc.bandwidth_scale, float) + assert isinstance(pwrc.kernel_const, float) + assert isinstance(pwrc.rate, float) + assert isinstance(pwrc._weights(), np.ndarray) + assert pwrc.force_int == force_int + expected_type = ( + np.ndarray if isinstance(covariance_data, np.ndarray) else pd.DataFrame + ) + assert isinstance(pwrc.cov.short_run, expected_type) + assert isinstance(pwrc.cov.long_run, expected_type) + assert isinstance(pwrc.cov.one_sided, expected_type) + assert isinstance(pwrc.cov.one_sided_strict, expected_type) + + @pytest.mark.parametrize("sample_autocov", [True, False]) -def test_data(data, sample_autocov): - pwrc = PreWhitenRecoloredCovariance( - data, sample_autocov=sample_autocov, bandwidth=0.0 +def test_data(covariance_data, sample_autocov, kernel): + pwrc = PreWhitenedRecolored( + covariance_data, sample_autocov=sample_autocov, kernel=kernel, bandwidth=0.0 ) - pwrc.cov + assert isinstance(pwrc.cov, CovarianceEstimate) def test_pwrc_errors(): x = np.random.standard_normal((500, 2)) with pytest.raises(ValueError, match="lags must be a"): - PreWhitenRecoloredCovariance(x, lags=-1) + PreWhitenedRecolored(x, lags=-1) with pytest.raises(ValueError, match="lags must be a"): - PreWhitenRecoloredCovariance(x, lags=np.array([2])) + PreWhitenedRecolored(x, lags=np.array([2])) with pytest.raises(ValueError, match="lags must be a"): - PreWhitenRecoloredCovariance(x, lags=3.5) + PreWhitenedRecolored(x, lags=3.5) def test_pwrc_warnings(): x = np.random.standard_normal((9, 5)) with pytest.warns(RuntimeWarning, match="The maximum number of lags is 0"): - PreWhitenRecoloredCovariance(x).cov + assert isinstance(PreWhitenedRecolored(x).cov, CovarianceEstimate) + + +def test_unknown_kernel(covariance_data): + with pytest.raises(ValueError, match=""): + PreWhitenedRecolored(covariance_data, kernel="unknown") diff --git a/arch/tests/unitroot/test_dynamic_ols.py b/arch/tests/unitroot/test_dynamic_ols.py index 12c3be76ea..aabfd86ea9 100644 --- a/arch/tests/unitroot/test_dynamic_ols.py +++ b/arch/tests/unitroot/test_dynamic_ols.py @@ -16,7 +16,17 @@ def test_smoke(data, trend, lags, leads, common, max_lag, method): y, x = data if common: leads = lags - mod = DynamicOLS(y, x, trend, lags, leads, common, max_lag, max_lag, method) + mod = DynamicOLS( + y, + x, + trend, + lags=lags, + leads=leads, + common=common, + max_lag=max_lag, + max_lead=max_lag, + method=method, + ) mod.fit() @@ -27,8 +37,14 @@ def test_smoke(data, trend, lags, leads, common, max_lag, method): @pytest.mark.parametrize("df_adjust", [True, False]) def test_smoke_fit(data, cov_type, kernel, bandwidth, force_int, df_adjust): y, x = data - mod = DynamicOLS(y, x, "ct", 3, 5, False) - res = mod.fit(cov_type, kernel, bandwidth, force_int, df_adjust) + mod = DynamicOLS(y, x, "ct", lags=3, leads=5, common=False) + res = mod.fit( + cov_type, + kernel=kernel, + bandwidth=bandwidth, + force_int=force_int, + df_adjust=df_adjust, + ) assert isinstance(res.leads, int) assert isinstance(res.lags, int) assert isinstance(res.bandwidth, (int, float)) @@ -44,7 +60,7 @@ def test_smoke_fit(data, cov_type, kernel, bandwidth, force_int, df_adjust): def test_mismatch_lead_lag(data): y, x = data with pytest.raises(ValueError, match="common is specified but leads"): - DynamicOLS(y, x, "c", 4, 5, True) + DynamicOLS(y, x, "c", lags=4, leads=5, common=True) with pytest.raises(ValueError, match="common is specified but max_lead"): DynamicOLS(y, x, max_lag=6, max_lead=7, common=True) diff --git a/arch/tests/unitroot/test_fmols_ccr.py b/arch/tests/unitroot/test_fmols_ccr.py index b22bd1c583..e9ed034383 100644 --- a/arch/tests/unitroot/test_fmols_ccr.py +++ b/arch/tests/unitroot/test_fmols_ccr.py @@ -20,8 +20,8 @@ def test_fmols_smoke( y, x = trivariate_data if x_trend is not None and len(x_trend) < len(trend): x_trend = trend - mod = FullyModifiedOLS(y, x, trend, x_trend) - res = mod.fit(kernel, bandwidth, force_int, diff) + mod = FullyModifiedOLS(y, x, trend, x_trend=x_trend) + res = mod.fit(kernel=kernel, bandwidth=bandwidth, force_int=force_int, diff=diff) assert isinstance(res.summary(), Summary) @@ -35,8 +35,8 @@ def test_ccr_smoke(trivariate_data, trend, x_trend, diff, kernel, bandwidth, for y, x = trivariate_data if x_trend is not None and len(x_trend) < len(trend): x_trend = trend - mod = CanonicalCointegratingReg(y, x, trend, x_trend) - res = mod.fit(kernel, bandwidth, force_int, diff) + mod = CanonicalCointegratingReg(y, x, trend, x_trend=x_trend) + res = mod.fit(kernel=kernel, bandwidth=bandwidth, force_int=force_int, diff=diff) assert isinstance(res.summary(), Summary) @@ -189,7 +189,7 @@ def test_fmols_eviews(trivariate_data, test_key): trend, x_trend, diff = test_key key = (trend, x_trend, diff) test_res = setup_test_values(FMOLS_RES[key]) - mod = FullyModifiedOLS(y, x, trend, x_trend) + mod = FullyModifiedOLS(y, x, trend, x_trend=x_trend) # BW is one less than what Eviews reports res = mod.fit(bandwidth=6, force_int=True, diff=diff, df_adjust=True) if trend != "ctt": @@ -322,7 +322,7 @@ def test_ccr_eviews(trivariate_data, test_key): trend, x_trend, diff = test_key key = (trend, x_trend, diff) test_res = setup_test_values(CCR_RES[key]) - mod = CanonicalCointegratingReg(y, x, trend, x_trend) + mod = CanonicalCointegratingReg(y, x, trend, x_trend=x_trend) # BW is one less than what Eviews reports res = mod.fit(bandwidth=6, force_int=True, diff=diff, df_adjust=True) if trend == "n": diff --git a/arch/tests/unitroot/test_phillips_ouliaris.py b/arch/tests/unitroot/test_phillips_ouliaris.py index b0323dc6af..588e4f21a5 100644 --- a/arch/tests/unitroot/test_phillips_ouliaris.py +++ b/arch/tests/unitroot/test_phillips_ouliaris.py @@ -2,6 +2,7 @@ import numpy as np from numpy.testing import assert_allclose +import pandas as pd import pytest from statsmodels.iolib.summary import Summary @@ -165,3 +166,65 @@ def test_auto_bandwidth(trivariate_data): assert int(res.bandwidth) != res.bandwidth res = phillips_ouliaris(y, x, force_int=True) assert int(res.bandwidth) == res.bandwidth + + +REFERENCE = ( + ( + "n", + [ + [-4.017712, 0.0062, -31.26709, 0.0079], + [-4.207778, 0.0033, -33.83294, 0.0046], + [-4.218211, 0.0031, -33.87041, 0.0045], + ], + ), + ( + "c", + [ + [-29.56978, 0.0000, -1210.576, 0.0001], + [-32.09233, 0.0000, -976.0578, 0.0001], + [-31.93710, 0.0000, -986.2292, 0.0001], + ], + ), + ( + "ct", + [ + [-27.55381, 0.0001, -1179.019, 0.0001], + [-32.22650, 0.0001, -967.9824, 0.0001], + [-31.75132, 0.0001, -999.3838, 0.0001], + ], + ), + ( + "ctt", + [ + [-27.61660, 0.0000, -1180.209, 0.0001], + [-32.50041, 0.0000, -950.8720, 0.0001], + [-31.94475, 0.0000, -983.4933, 0.0001], + ], + ), +) + + +@pytest.mark.parametrize("result", REFERENCE) +def test_against_ref(trivariate_data, result): + trend = result[0] + ref = result[1] + for i in range(3): + ref_row = ref[i] + x_idx = list(np.arange(3)) + x_idx.pop(i) + y_idx = [i] + if isinstance(trivariate_data[0], pd.DataFrame): + z = pd.concat(trivariate_data, axis=1) + x = z.iloc[:, x_idx] + y = z.iloc[:, y_idx] + else: + z = np.column_stack(trivariate_data) + x = z[:, x_idx] + y = z[:, y_idx] + zt = phillips_ouliaris(y, x, trend=trend, test_type="Zt", bandwidth=9) + za = phillips_ouliaris(y, x, trend=trend, test_type="Za", bandwidth=9) + scale = y.shape[0] / (y.shape[0] - 1) + assert_allclose(zt.stat, np.sqrt(scale) * ref_row[0], rtol=1e-4) + assert_allclose(zt.pvalue, ref_row[1], atol=1e-3) + assert_allclose(za.stat, scale * ref_row[2], rtol=1e-4) + assert_allclose(za.pvalue, ref_row[3], atol=1e-3) diff --git a/arch/unitroot/cointegration.py b/arch/unitroot/cointegration.py index c4ca827473..1939302b43 100644 --- a/arch/unitroot/cointegration.py +++ b/arch/unitroot/cointegration.py @@ -523,6 +523,7 @@ def __init__( y: ArrayLike1D, x: ArrayLike2D, trend: str = "c", + *, lags: Optional[int] = None, leads: Optional[int] = None, common: bool = False, @@ -671,6 +672,7 @@ def _leads_and_lags(self) -> Tuple[int, int]: def fit( self, cov_type: str = "unadjusted", + *, kernel: str = "bartlett", bandwidth: Optional[int] = None, force_int: bool = False, @@ -788,12 +790,16 @@ def _cov( kernel_est = KERNEL_ESTIMATORS[kernel] scale = nobs / (nobs - nx) if df_adjust else 1.0 if cov_type in ("unadjusted", "homoskedastic"): - est = kernel_est(eps, bandwidth, center=False, force_int=force_int) + est = kernel_est( + eps, bandwidth=bandwidth, center=False, force_int=force_int + ) sigma2 = np.squeeze(est.cov.long_run) cov = (scale * sigma2) * sigma_xx_inv / nobs elif cov_type in ("robust", "kernel"): scores = x * eps - est = kernel_est(scores, bandwidth, center=False, force_int=force_int) + est = kernel_est( + scores, bandwidth=bandwidth, center=False, force_int=force_int + ) s = est.cov.long_run cov = scale * sigma_xx_inv @ s @ sigma_xx_inv / nobs else: @@ -969,6 +975,7 @@ def __init__( y: ArrayLike1D, x: ArrayLike2D, trend: str = "c", + *, x_trend: Optional[str] = None, ) -> None: setup = _check_cointegrating_regression(y, x, trend) @@ -1025,6 +1032,7 @@ def _final_statistics(self, theta: pd.Series) -> Tuple[pd.Series, float, float]: def fit( self, + *, kernel: str = "bartlett", bandwidth: Optional[float] = None, force_int: bool = True, @@ -1122,13 +1130,15 @@ def __init__( y: ArrayLike1D, x: ArrayLike2D, trend: str = "c", + *, x_trend: Optional[str] = None, ) -> None: - super().__init__(y, x, trend, x_trend) + super().__init__(y, x, trend, x_trend=x_trend) @Appender(FullyModifiedOLS.fit.__doc__) def fit( self, + *, kernel: str = "bartlett", bandwidth: Optional[float] = None, force_int: bool = True, diff --git a/arch/unitroot/critical_values/phillips_ouliaris.py b/arch/unitroot/critical_values/phillips_ouliaris.py index 4d44c50f97..475a8fd0e9 100644 --- a/arch/unitroot/critical_values/phillips_ouliaris.py +++ b/arch/unitroot/critical_values/phillips_ouliaris.py @@ -1,10 +1,10 @@ """ Critical values produced by phillips-ouliaris-simulation.py -Z-type statistics with trend n based on 6,000,000 simulations -Z-type statistics with trend c based on 3,750,000 simulations -Z-type statistics with trend ct based on 4,250,000 simulations -Z-type statistics with trend ctt based on 3,500,000 simulations +Z-type statistics with trend n based on 8,000,000 simulations +Z-type statistics with trend c based on 6,000,000 simulations +Z-type statistics with trend ct based on 5,750,000 simulations +Z-type statistics with trend ctt based on 5,500,000 simulations P-type statistics with trend n based on 2,000,000 simulations P-type statistics with trend c based on 3,500,000 simulations P-type statistics with trend ct based on 3,250,000 simulations @@ -15,524 +15,524 @@ CV_PARAMETERS = { ("Za", "n", 1): { - 1: [-13.70482, 52.42067, -284.70723, 0.0], - 5: [-8.0361, 16.92314, 51.87988, 0.0], - 10: [-5.71114, 8.78818, 47.43726, 0.0], + 1: [-13.70904, 53.19865, -337.49286, 0.0], + 5: [-8.0397, 17.80724, -11.00751, 0.0], + 10: [-5.7131, 9.22471, 19.80952, 0.0], }, ("Za", "n", 2): { - 1: [-22.93667, 141.9933, -699.2124, 0.0], - 5: [-15.83824, 68.92006, -185.61946, 0.0], - 10: [-12.67903, 44.14223, -25.62266, -1570.03724], + 1: [-22.94069, 141.91052, -682.00674, 0.0], + 5: [-15.84193, 69.35167, -213.02287, 0.0], + 10: [-12.68182, 44.6335, -61.75334, 0.0], }, ("Za", "n", 3): { - 1: [-30.42931, 258.67542, -1672.62155, 0.0], - 5: [-22.35996, 142.38696, -603.88212, 0.0], - 10: [-18.6628, 101.07617, -355.31261, 0.0], + 1: [-30.4307, 256.76446, -1514.79279, 0.0], + 5: [-22.36197, 142.4719, -611.89498, 0.0], + 10: [-18.6641, 100.93366, -346.20477, 0.0], }, ("Za", "n", 4): { - 1: [-37.19271, 393.37776, -3148.69974, 17640.84425], - 5: [-28.3423, 235.71135, -1475.91697, 5951.62983], - 10: [-24.22156, 176.62704, -1020.06703, 4159.05688], + 1: [-37.19602, 392.84679, -3061.18938, 15657.1044], + 5: [-28.3424, 235.15434, -1423.91482, 4846.93943], + 10: [-24.22155, 176.20303, -979.49903, 3244.72335], }, ("Za", "n", 5): { - 1: [-43.56625, 554.47721, -5690.47186, 45080.46404], - 5: [-34.03984, 350.65921, -3041.46749, 21998.06684], - 10: [-29.54667, 271.32899, -2245.8397, 17391.70411], + 1: [-43.57507, 554.17808, -5536.38217, 39996.44562], + 5: [-34.04031, 348.55903, -2822.18147, 16632.43697], + 10: [-29.54333, 268.71685, -2017.99804, 12163.68509], }, ("Za", "n", 6): { - 1: [-49.67603, 730.27299, -8166.29731, 57397.18795], - 5: [-39.54653, 480.1968, -4665.65448, 31537.41113], - 10: [-34.72429, 378.70644, -3407.59731, 22579.4062], + 1: [-49.67572, 727.02688, -7868.71603, 50660.97989], + 5: [-39.54379, 478.21359, -4511.78405, 28381.92263], + 10: [-34.72054, 377.4157, -3327.14514, 21129.2988], }, ("Za", "n", 7): { - 1: [-55.60943, 925.87643, -11485.37169, 82700.34989], - 5: [-44.90098, 624.09421, -6798.07673, 48712.16282], - 10: [-39.78175, 500.80009, -5017.18533, 33914.62527], + 1: [-55.60459, 924.01131, -11352.96958, 80455.75183], + 5: [-44.89863, 623.33363, -6742.84352, 47572.95905], + 10: [-39.7781, 499.53153, -4936.98109, 32449.83506], }, ("Za", "n", 8): { - 1: [-61.37002, 1139.22116, -16018.1578, 135915.01164], - 5: [-50.16833, 787.83482, -9599.89186, 72963.78884], - 10: [-44.76851, 643.58493, -7594.65945, 62685.34427], + 1: [-61.368, 1137.14195, -15789.11609, 130373.39386], + 5: [-50.1648, 786.54554, -9496.87541, 70581.92935], + 10: [-44.76705, 642.02244, -7432.36946, 58582.31373], }, ("Za", "n", 9): { - 1: [-67.01284, 1363.84567, -20467.62478, 173813.42677], - 5: [-55.34048, 966.05143, -13004.60427, 106348.46727], - 10: [-49.67538, 793.52795, -9855.76216, 75786.76545], + 1: [-67.01298, 1358.55009, -19859.99995, 158404.91586], + 5: [-55.34054, 963.92168, -12773.50452, 100761.17944], + 10: [-49.67571, 792.15735, -9718.3616, 72598.48096], }, ("Za", "n", 10): { - 1: [-72.56247, 1605.60212, -25716.3882, 225334.21935], - 5: [-60.44831, 1161.78015, -17371.74672, 160567.45898], - 10: [-54.54887, 967.22205, -13637.91976, 123587.04995], + 1: [-72.56094, 1601.46497, -25275.52031, 214512.40461], + 5: [-60.44298, 1158.29407, -17040.26438, 152834.14071], + 10: [-54.54576, 964.96449, -13430.34171, 118718.82234], }, ("Za", "n", 11): { - 1: [-78.0251, 1868.7293, -32488.42266, 310209.14374], - 5: [-65.48462, 1366.77815, -21596.2412, 198836.07405], - 10: [-59.36328, 1152.54256, -17703.65475, 170015.69954], + 1: [-78.02158, 1862.80986, -31864.26337, 294949.44098], + 5: [-65.47786, 1362.37273, -21219.90016, 190342.70286], + 10: [-59.35312, 1146.08211, -17060.30695, 152907.07482], }, ("Za", "n", 12): { - 1: [-83.41319, 2142.54666, -39548.65444, 391738.99358], - 5: [-70.47852, 1590.88836, -27029.2416, 262438.34119], - 10: [-64.13452, 1349.56818, -22143.70104, 219080.60439], + 1: [-83.41862, 2141.84487, -39479.07425, 391885.15306], + 5: [-70.47604, 1588.12497, -26794.7863, 257305.664], + 10: [-64.12747, 1344.86006, -21703.82737, 208153.32744], }, ("Za", "n", 13): { - 1: [-88.76925, 2440.72322, -48472.19276, 522213.00332], - 5: [-75.44495, 1837.52221, -34243.16696, 373631.75399], - 10: [-68.88502, 1563.28739, -27526.79616, 288616.15259], + 1: [-88.77788, 2442.74869, -48644.34078, 527630.31054], + 5: [-75.43885, 1833.14644, -33851.8239, 364307.81436], + 10: [-68.87693, 1559.32387, -27184.81071, 280482.66041], }, ("Zt", "n", 1): { - 1: [-2.56643, -4.59477, -23.59743, 0.0], - 5: [-1.94047, -2.408, 7.89136, 0.0], - 10: [-1.61613, -1.55581, 9.81803, -229.14518], + 1: [-2.56698, -4.4916, -29.70734, 0.0], + 5: [-1.9409, -2.29696, -0.24111, 0.0], + 10: [-1.6165, -1.46966, 4.22206, 0.0], }, ("Zt", "n", 2): { - 1: [-3.34327, -9.68891, -32.15415, 0.0], - 5: [-2.76153, -5.68837, -5.98211, 0.0], - 10: [-2.45883, -4.22553, 5.27138, -254.25311], + 1: [-3.34352, -9.72221, -29.59633, 0.0], + 5: [-2.76184, -5.64843, -8.76858, 0.0], + 10: [-2.45908, -4.17474, 1.169, -179.67984], }, ("Zt", "n", 3): { - 1: [-3.86093, -14.15095, -25.90184, 0.0], - 5: [-3.29661, -8.78277, -14.73097, 0.0], - 10: [-3.0023, -6.74809, -0.96273, -297.95905], + 1: [-3.86099, -14.30315, -14.02861, -750.0096], + 5: [-3.29671, -8.81093, -12.79761, 0.0], + 10: [-3.00238, -6.75655, -0.43088, -317.16863], }, ("Zt", "n", 4): { - 1: [-4.27553, -18.20344, -70.09689, 0.0], - 5: [-3.72094, -11.87168, -26.33939, 0.0], - 10: [-3.4319, -9.20618, -18.82377, 0.0], + 1: [-4.27544, -18.35177, -56.20368, 0.0], + 5: [-3.72094, -11.92863, -20.51031, 0.0], + 10: [-3.43185, -9.25221, -14.07021, 0.0], }, ("Zt", "n", 5): { - 1: [-4.63238, -22.05329, -118.01019, 0.0], - 5: [-4.08483, -14.64496, -67.45676, 629.04209], - 10: [-3.79872, -11.49199, -60.04745, 759.14168], + 1: [-4.63292, -22.06689, -107.00794, 0.0], + 5: [-4.08474, -14.8284, -49.26372, 0.0], + 10: [-3.79847, -11.70292, -40.68532, 0.0], }, ("Zt", "n", 6): { - 1: [-4.9508, -26.10666, -122.88654, 0.0], - 5: [-4.40785, -17.93854, -56.58672, 0.0], - 10: [-4.1243, -14.3282, -47.7713, 0.0], + 1: [-4.95064, -26.39411, -96.14768, 0.0], + 5: [-4.4077, -18.06313, -46.54069, 0.0], + 10: [-4.12411, -14.38558, -45.29103, 0.0], }, ("Zt", "n", 7): { - 1: [-5.24097, -30.32659, -123.34645, 0.0], - 5: [-4.70127, -21.13268, -78.35997, 0.0], - 10: [-4.41927, -17.25356, -46.74682, 0.0], + 1: [-5.24079, -30.37447, -122.86973, 0.0], + 5: [-4.70112, -21.18358, -74.83586, 0.0], + 10: [-4.41915, -17.28144, -47.24983, 0.0], }, ("Zt", "n", 8): { - 1: [-5.50868, -34.60235, -138.85993, 0.0], - 5: [-4.97275, -24.43647, -78.90028, 0.0], - 10: [-4.69243, -19.81799, -82.30358, 930.47761], + 1: [-5.50848, -34.77958, -117.70109, 0.0], + 5: [-4.97257, -24.51934, -72.32595, 0.0], + 10: [-4.69236, -19.92064, -71.25285, 0.0], }, ("Zt", "n", 9): { - 1: [-5.75869, -39.11677, -132.46264, 0.0], - 5: [-5.22597, -27.75857, -90.05561, 0.0], - 10: [-4.94632, -23.15707, -44.40921, 0.0], + 1: [-5.75864, -39.44617, -96.13458, -2000.49941], + 5: [-5.2259, -27.92201, -74.63095, 0.0], + 10: [-4.94636, -23.21282, -38.80316, 0.0], }, ("Zt", "n", 10): { - 1: [-5.99416, -43.88496, -89.98516, -3042.26158], - 5: [-5.46451, -31.17141, -112.0106, 0.0], - 10: [-5.18652, -25.8815, -82.35818, 0.0], + 1: [-5.9942, -44.04465, -74.19142, -3291.38838], + 5: [-5.4642, -31.39903, -91.24088, 0.0], + 10: [-5.1863, -26.02958, -69.21531, 0.0], }, ("Zt", "n", 11): { - 1: [-6.21798, -48.1261, -121.1721, 0.0], - 5: [-5.68993, -34.93614, -83.33201, 0.0], - 10: [-5.41337, -28.84742, -98.7165, 0.0], + 1: [-6.21763, -48.52065, -82.23026, -3556.0807], + 5: [-5.68953, -35.21398, -58.20655, 0.0], + 10: [-5.41283, -29.20409, -63.30005, 0.0], }, ("Zt", "n", 12): { - 1: [-6.43064, -52.87092, -102.9785, -4003.57421], - 5: [-5.90513, -38.53992, -85.86948, 0.0], - 10: [-5.62917, -32.05209, -97.24503, 0.0], + 1: [-6.43089, -52.85315, -113.86049, -3375.88852], + 5: [-5.90494, -38.71548, -73.0379, 0.0], + 10: [-5.62881, -32.32958, -71.16443, 0.0], }, ("Zt", "n", 13): { - 1: 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[-5.76439, -40.59061, -46.66217, -3633.31677], - 5: [-5.23253, -28.8847, -57.22562, 0.0], - 10: [-4.95366, -23.90856, -37.01147, 0.0], + 1: [-5.76438, -40.19784, -94.50981, -2210.67811], + 5: [-5.23249, -28.71092, -78.36145, 0.0], + 10: [-4.95368, -23.78842, -49.6721, 0.0], }, ("Zt", "ct", 8): { - 1: [-6.00047, -43.9167, -173.54871, 0.0], - 5: [-5.46961, -32.22212, -55.69968, 0.0], - 10: [-5.19197, -26.69021, -52.48769, 0.0], + 1: [-6.00042, -43.68618, -205.53131, 0.0], + 5: [-5.46944, -32.10815, -74.22853, 0.0], + 10: [-5.19176, -26.67857, -59.05444, 0.0], }, ("Zt", "ct", 9): { - 1: [-6.22326, -48.00494, -234.65649, 0.0], - 5: [-5.69442, -35.60286, -87.91688, 0.0], - 10: [-5.41756, -29.89625, -43.53162, 0.0], + 1: [-6.22279, -48.04583, -231.87577, 0.0], + 5: [-5.6944, -35.44369, -108.19986, 0.0], + 10: [-5.41741, -29.77015, -63.20579, 0.0], }, ("Zt", "ct", 10): { - 1: [-6.43627, -52.06292, -308.54197, 0.0], - 5: [-5.90912, -39.0665, -112.06121, 0.0], - 10: [-5.63301, -32.89454, -69.74781, 0.0], + 1: [-6.43573, -52.14475, -297.09788, 0.0], + 5: [-5.90902, -38.90732, -126.39016, 0.0], + 10: [-5.63277, -32.84408, -76.59977, 0.0], }, ("Zt", "ct", 11): { - 1: [-6.63997, -56.53413, -324.40962, 0.0], - 5: [-6.11415, -42.53159, -139.5908, 0.0], - 10: [-5.83845, -36.25958, -52.84675, 0.0], + 1: [-6.63929, -56.74691, -289.93552, 0.0], + 5: [-6.11395, -42.4758, -139.14675, 0.0], + 10: [-5.83834, -36.10308, -70.4358, 0.0], }, ("Zt", "ct", 12): { - 1: [-6.83504, -61.15834, -335.2151, 0.0], - 5: [-6.31185, -45.99484, -169.80411, 0.0], - 10: [-6.03652, -39.44398, -58.74477, 0.0], + 1: [-6.83544, -60.82675, -365.95884, 0.0], + 5: [-6.31182, -45.82641, -190.62971, 2196.32765], + 10: [-6.03664, -39.26022, -78.77069, 0.0], }, ("Zt", "ct", 13): { - 1: [-7.02416, -65.87903, -343.51071, 0.0], - 5: [-6.50196, -49.837, -152.20863, 0.0], - 10: [-6.22742, -42.55517, -90.7491, 0.0], + 1: [-7.02441, -65.543, -372.57595, 0.0], + 5: [-6.5016, -49.77062, -174.99149, 0.0], + 10: [-6.22718, -42.52823, -101.82518, 0.0], }, ("Za", "ctt", 1): { - 1: [-36.58585, 302.87844, -1445.58451, 0.0], - 5: [-28.10592, 168.84207, -497.72544, 0.0], - 10: [-24.17924, 118.95944, -231.51307, 0.0], + 1: [-36.59179, 304.27342, -1541.52788, 5648.54023], + 5: [-28.10724, 169.6583, -529.47811, 0.0], + 10: [-24.17893, 119.02357, -216.47744, 0.0], }, ("Za", "ctt", 2): { - 1: [-42.60809, 448.37159, -3177.13928, 16894.56396], - 5: [-33.39221, 270.66465, -1410.96222, 0.0], - 10: [-29.06757, 201.18298, -834.25679, 0.0], + 1: [-42.60311, 449.1221, -3259.59872, 18290.98585], + 5: [-33.38777, 268.58758, -1216.63755, 0.0], + 10: [-29.06667, 200.48986, -747.86137, 0.0], }, ("Za", "ctt", 3): { - 1: [-48.50131, 611.50579, -5234.53385, 28085.54042], - 5: [-38.65363, 390.14909, -2667.93315, 11796.25002], - 10: [-33.97532, 298.48587, -1580.00784, 0.0], + 1: [-48.49172, 609.98055, -5113.50427, 25421.72801], + 5: [-38.64781, 388.7001, -2558.53166, 9534.59809], + 10: [-33.96904, 297.35266, -1501.70368, 0.0], }, ("Za", "ctt", 4): { - 1: [-54.31436, 805.49322, -9085.16686, 74886.3504], - 5: [-43.8681, 529.54742, -4774.80389, 32294.30508], - 10: [-38.87702, 418.05344, -3340.72659, 20098.45423], + 1: [-54.31258, 804.98992, -8954.8826, 70070.69137], + 5: [-43.86161, 526.08137, -4435.73227, 23987.39728], + 10: [-38.87315, 416.66356, -3222.67381, 17493.32723], }, ("Za", "ctt", 5): { - 1: [-60.01616, 1007.68145, -12523.51495, 101500.17096], - 5: [-49.03216, 681.70559, -6936.15664, 46018.732], - 10: [-43.75309, 546.95128, -4962.32089, 29065.3439], + 1: [-60.02503, 1010.92189, -12671.25151, 102549.79254], + 5: [-49.04032, 683.46858, -7023.01322, 47077.80027], + 10: [-43.754, 545.98524, -4831.88995, 25607.78215], }, ("Za", "ctt", 6): { - 1: [-65.62595, 1228.58804, -16702.5015, 135086.71206], - 5: [-54.15357, 854.01443, -10095.37997, 76758.66675], - 10: [-48.59957, 692.58677, -7193.21176, 45626.27327], + 1: [-65.63241, 1230.39228, -16829.37229, 137263.55789], + 5: [-54.1572, 854.35282, -10025.39141, 73711.51731], + 10: [-48.60357, 693.69202, -7234.32507, 45938.36101], }, ("Za", "ctt", 7): { - 1: [-71.17118, 1476.8437, -23180.21877, 226534.09929], - 5: [-59.23518, 1043.57989, -14085.53859, 123679.67317], - 10: [-53.42615, 856.23217, -10357.49796, 81399.31135], + 1: [-71.17565, 1477.54329, -23034.07552, 219376.05113], + 5: [-59.23622, 1043.86442, -14019.68311, 120599.263], + 10: [-53.42933, 857.60565, -10400.47955, 80988.47017], }, ("Za", "ctt", 8): { - 1: [-76.64287, 1733.55371, -29118.39338, 286802.29564], - 5: [-64.25702, 1245.35732, -18296.46663, 166218.86327], - 10: [-58.21922, 1036.93796, -14167.32099, 123020.52508], + 1: [-76.64191, 1733.43685, -29054.20993, 283915.90944], + 5: [-64.25869, 1247.39214, -18492.79914, 171089.93948], + 10: [-58.22164, 1038.36792, -14262.34855, 124760.18066], }, ("Za", "ctt", 9): { - 1: [-82.04923, 2008.64311, -36816.16598, 400865.54217], - 5: [-69.23724, 1465.88945, -23784.6579, 239867.29895], - 10: [-62.9757, 1228.13778, -18312.68156, 170259.9627], + 1: [-82.0438, 2007.25769, -36743.01642, 399303.65953], + 5: [-69.23606, 1463.66617, -23495.19152, 230699.87769], + 10: [-62.97749, 1228.85454, -18337.73194, 169865.69304], }, ("Za", "ctt", 10): { - 1: [-87.39625, 2303.08921, -45657.32706, 533176.81371], - 5: [-74.18869, 1704.6818, -30301.60743, 332087.76027], - 10: [-67.71708, 1442.5078, -24052.51087, 252614.20694], + 1: [-87.39547, 2298.65567, -45007.91468, 510776.19942], + 5: [-74.18549, 1701.8184, -29928.73324, 319803.47836], + 10: [-67.71461, 1441.49391, -23905.11298, 247248.48359], }, ("Za", "ctt", 11): { - 1: [-92.65388, 2598.57962, -53739.51801, 628800.12506], - 5: [-79.08383, 1945.20823, -36289.79906, 401586.3973], - 10: [-72.41685, 1661.84084, -29552.35514, 320520.67727], + 1: [-92.65357, 2597.82215, -53694.85093, 628265.50337], + 5: [-79.08856, 1945.98239, -36277.06378, 399112.63109], + 10: [-72.41542, 1660.23853, -29304.15766, 311596.27471], }, ("Za", "ctt", 12): { - 1: [-97.90472, 2927.46167, -65234.86736, 832888.50651], - 5: [-83.97008, 2214.95026, -44883.57944, 545782.38619], - 10: [-77.09885, 1899.31737, -36466.69151, 429236.70195], + 1: [-97.91819, 2931.588, -65541.6732, 839157.83012], + 5: [-83.97807, 2218.1228, -45120.47342, 549939.1435], + 10: [-77.10041, 1901.48565, -36630.90821, 431340.53599], }, ("Za", "ctt", 13): { - 1: [-103.09418, 3255.97285, -75470.08614, 974964.08109], - 5: [-88.81975, 2488.35479, -52799.72567, 652051.00243], - 10: [-81.7511, 2143.08555, -43128.96628, 515063.82882], + 1: [-103.12264, 3266.20748, -76535.51128, 1006410.02205], + 5: [-88.82441, 2490.86941, -53018.97359, 656871.49148], + 10: [-81.75675, 2145.68572, -43321.36361, 518251.43883], }, ("Zt", "ctt", 1): { - 1: [-4.37106, -21.24086, -108.93785, 0.0], - 5: [-3.83191, -15.00209, -47.54536, 0.0], - 10: [-3.55306, -12.21123, -44.15842, 0.0], + 1: [-4.37139, -21.16428, -114.81988, 0.0], + 5: [-3.8322, -14.90975, -51.66868, 0.0], + 10: [-3.55302, -12.1923, -43.96951, 0.0], }, ("Zt", "ctt", 2): { - 1: [-4.69167, -25.07736, -121.28409, 0.0], - 5: [-4.15339, -17.52541, -67.85646, 0.0], - 10: [-3.87327, -14.39535, -50.40119, 0.0], + 1: [-4.69151, -25.06728, -120.31027, 0.0], + 5: [-4.15312, -17.67216, -51.93873, 0.0], + 10: [-3.87318, -14.49128, -37.2894, 0.0], }, ("Zt", "ctt", 3): { - 1: [-4.98981, -28.8403, -135.66382, 0.0], - 5: [-4.45271, -20.40947, -63.22956, 0.0], - 10: [-4.1722, -16.98143, -29.19213, 0.0], + 1: [-4.98911, -28.96281, -118.90688, 0.0], + 5: [-4.4523, -20.52703, -53.47264, 0.0], + 10: [-4.17185, -17.04495, -23.40138, -473.84909], }, ("Zt", "ctt", 4): { - 1: [-5.26765, -32.30821, -200.0004, 1691.06608], - 5: [-4.73222, -23.02612, -110.33777, 979.93434], - 10: [-4.45244, -19.14615, -66.20659, 0.0], + 1: [-5.26761, -32.33934, -187.5258, 0.0], + 5: [-4.73189, -23.22156, -87.39721, 0.0], + 10: [-4.4521, -19.28652, -52.00697, 0.0], }, ("Zt", "ctt", 5): { - 1: [-5.52805, -36.39828, -196.28417, 0.0], - 5: [-4.99422, -26.39318, -87.35466, 0.0], - 10: [-4.71501, -22.02956, -49.34041, 0.0], + 1: [-5.52876, -36.00204, -222.4496, 1555.05725], + 5: [-4.99446, -26.34859, -87.03913, 0.0], + 10: [-4.71507, -22.08247, -42.40921, 0.0], }, ("Zt", "ctt", 6): { - 1: [-5.77382, -40.16221, -231.47596, 0.0], - 5: [-5.24219, -29.16756, -143.98966, 1447.46567], - 10: [-4.96334, -24.77385, -55.23288, 0.0], + 1: [-5.77434, -40.00174, -242.21483, 1517.00709], + 5: [-5.24217, -29.2475, -129.76291, 1015.53743], + 10: [-4.96356, -24.72313, -56.60721, 0.0], }, ("Zt", "ctt", 7): { - 1: [-6.00663, -44.253, -244.72035, 0.0], - 5: [-5.47657, -32.68812, -132.46662, 0.0], - 10: [-5.1991, -27.43819, -92.60485, 0.0], + 1: [-6.00715, -44.02489, -258.24341, 0.0], + 5: [-5.47678, -32.59462, -134.98262, 0.0], + 10: [-5.1993, -27.381, -90.13369, 0.0], }, ("Zt", "ctt", 8): { - 1: [-6.22849, -48.36105, -275.70105, 0.0], - 5: [-5.70047, -35.78259, -166.56814, 1671.72235], - 10: [-5.4234, -30.20216, -113.47584, 1191.10758], + 1: [-6.22844, -48.39631, -266.21665, 0.0], + 5: [-5.70035, -35.75812, -166.8955, 1661.61637], + 10: [-5.42353, -30.1321, -115.34749, 1179.66541], }, ("Zt", "ctt", 9): { - 1: [-6.44058, -52.56203, -318.82697, 0.0], - 5: [-5.91325, -39.26644, -177.46201, 0.0], - 10: [-5.63703, -33.43623, -100.98879, 0.0], + 1: [-6.4407, -52.41236, -346.64739, 3366.43066], + 5: [-5.91333, -39.32967, -166.79491, 0.0], + 10: [-5.63716, -33.4124, -96.07933, 0.0], }, ("Zt", "ctt", 10): { - 1: [-6.64347, -56.71046, -386.45638, 4311.36003], - 5: [-6.1183, -42.22814, -255.98237, 4022.17638], - 10: [-5.84276, -36.1008, -159.92599, 2537.73933], + 1: [-6.64331, -56.92493, -352.31656, 3008.73003], + 5: [-6.11826, -42.33544, -238.50294, 3339.94228], + 10: [-5.84261, -36.19683, -143.50068, 1895.0477], }, ("Zt", "ctt", 11): { - 1: [-6.83802, -61.25904, -379.04706, 0.0], - 5: [-6.31414, -46.17734, -246.06201, 3911.75777], - 10: [-6.03953, -39.45834, -153.05347, 2259.28864], + 1: [-6.83791, -61.36142, -366.02057, 0.0], + 5: [-6.31448, -46.1142, -242.01569, 3535.50878], + 10: [-6.03944, -39.53857, -139.58431, 1710.55776], }, ("Zt", "ctt", 12): { - 1: [-7.02679, -65.40823, -445.15901, 0.0], - 5: [-6.50387, -49.8115, -257.77019, 3979.43214], - 10: [-6.23018, -42.34733, -211.67736, 4127.12084], + 1: [-7.02718, -65.2755, -454.533, 4891.49951], + 5: [-6.50412, -49.71551, -259.44543, 3729.56016], + 10: [-6.23025, -42.30978, -205.67632, 3626.00347], }, ("Zt", "ctt", 13): { - 1: [-7.20818, -70.24217, -445.08676, 0.0], - 5: [-6.68733, -53.43332, -290.61484, 5008.40642], - 10: [-6.4134, -45.97736, -193.27868, 3561.06924], + 1: [-7.20912, -69.76276, -505.34142, 6588.53669], + 5: [-6.68739, -53.46935, -278.63252, 4383.80847], + 10: [-6.41363, -45.88867, -195.386, 3398.71522], }, ("Pu", "n", 1): { 1: [29.05894, -305.29302, 3398.44654, -22460.33423], @@ -1268,58 +1268,58 @@ } PVAL_LARGE_P = { - ("Pz", "ct", 1): [3.30155, 0.27713, 0.00592, 5e-05], - ("Pz", "ct", 2): [4.82879, 0.16613, 0.0016, 1e-05], - ("Pz", "ct", 3): [6.27041, 0.12266, 0.00069, 0.0], - ("Pz", "ct", 4): [7.77275, 0.10033, 0.00038, 0.0], - ("Pz", "ct", 5): [9.29828, 0.08613, 0.00024, 0.0], - ("Pz", "ct", 6): [10.67437, 0.07376, 0.00015, 0.0], - ("Pz", "ct", 7): [12.12459, 0.06532, 0.0001, 0.0], - ("Pz", "ct", 8): [13.6131, 0.05906, 8e-05, 0.0], - ("Pz", "ct", 9): [15.05283, 0.05359, 6e-05, 0.0], - ("Pz", "ct", 10): [16.4971, 0.04904, 4e-05, 0.0], - ("Pz", "ct", 11): [17.93937, 0.04534, 3e-05, 0.0], - ("Pz", "ct", 12): [19.28102, 0.04171, 3e-05, 0.0], - ("Pz", "ct", 13): [20.79457, 0.03921, 2e-05, 0.0], - ("Pz", "n", 1): [2.2525, 0.80865, 0.04884, 0.00084], - ("Pz", "n", 2): [3.32511, 0.30202, 0.00703, 6e-05], - ("Pz", "n", 3): [4.64192, 0.18952, 0.00216, 1e-05], - ("Pz", "n", 4): [6.11363, 0.14327, 0.00098, 0.0], - ("Pz", "n", 5): [7.42172, 0.11145, 0.00049, 0.0], - ("Pz", "n", 6): [8.68101, 0.09017, 0.00027, 0.0], - ("Pz", "n", 7): [10.08461, 0.07754, 0.00018, 0.0], - ("Pz", "n", 8): [11.55255, 0.06884, 0.00012, 0.0], - ("Pz", "n", 9): [12.96494, 0.06143, 9e-05, 0.0], - ("Pz", "n", 10): [14.60826, 0.05712, 7e-05, 0.0], - ("Pz", "n", 11): [15.91809, 0.05127, 5e-05, 0.0], - ("Pz", "n", 12): [17.32123, 0.047, 4e-05, 0.0], - ("Pz", "n", 13): [18.68509, 0.04326, 3e-05, 0.0], - ("Pz", "c", 1): [2.6477, 0.45084, 0.01728, 0.00022], - ("Pz", "c", 2): [3.92845, 0.23004, 0.00361, 2e-05], - ("Pz", "c", 3): [5.2781, 0.15799, 0.00134, 0.0], - ("Pz", "c", 4): [6.63454, 0.12106, 0.00064, 0.0], - ("Pz", "c", 5): [8.12364, 0.10148, 0.00038, 0.0], - ("Pz", "c", 6): [9.47191, 0.08495, 0.00023, 0.0], - ("Pz", "c", 7): [10.91098, 0.07412, 0.00015, 0.0], - ("Pz", "c", 8): [12.36115, 0.06592, 0.00011, 0.0], - ("Pz", "c", 9): [13.83247, 0.05953, 8e-05, 0.0], - ("Pz", "c", 10): [15.25473, 0.05401, 6e-05, 0.0], - ("Pz", "c", 11): [16.64305, 0.04921, 4e-05, 0.0], - ("Pz", "c", 12): [18.20898, 0.04607, 4e-05, 0.0], - ("Pz", "c", 13): [19.65749, 0.04285, 3e-05, 0.0], - ("Pz", "ctt", 1): [3.8921, 0.22752, 0.00358, 2e-05], - ("Pz", "ctt", 2): [5.53255, 0.13801, 0.00098, 0.0], - ("Pz", "ctt", 3): 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"n", 8): [4.10505, 0.20675, 0.00266, 2e-05], - ("Za", "n", 9): [4.38461, 0.19465, 0.00223, 1e-05], - ("Za", "n", 10): [4.63829, 0.18369, 0.00189, 1e-05], - ("Za", "n", 11): [4.88786, 0.17497, 0.00163, 1e-05], - ("Za", "n", 12): [5.12829, 0.16767, 0.00144, 1e-05], - ("Za", "n", 13): [5.35007, 0.16076, 0.00127, 0.0], - ("Za", "c", 1): [1.68846, 0.49131, 0.02633, 0.00056], - ("Za", "c", 2): [2.17814, 0.36625, 0.01301, 0.0002], - ("Za", "c", 3): [2.62372, 0.30453, 0.00797, 9e-05], - ("Za", "c", 4): [3.04958, 0.27123, 0.00574, 6e-05], - ("Za", "c", 5): [3.42404, 0.24637, 0.00436, 4e-05], - ("Za", "c", 6): [3.76759, 0.22783, 0.00347, 2e-05], - ("Za", "c", 7): [4.04436, 0.20917, 0.00273, 2e-05], - ("Za", "c", 8): [4.34393, 0.19828, 0.00233, 1e-05], - ("Za", "c", 9): [4.61094, 0.18739, 0.00197, 1e-05], - ("Za", "c", 10): [4.88643, 0.1799, 0.00174, 1e-05], - ("Za", "c", 11): [5.10377, 0.17063, 0.0015, 1e-05], - ("Za", "c", 12): [5.32214, 0.16302, 0.00131, 1e-05], - ("Za", "c", 13): [5.5395, 0.15675, 0.00117, 0.0], - ("Za", "ctt", 1): [3.21704, 0.34316, 0.00898, 0.0001], - ("Za", "ctt", 2): [3.46148, 0.29681, 0.00639, 6e-05], - ("Za", "ctt", 3): [3.69868, 0.26173, 0.00472, 4e-05], - ("Za", "ctt", 4): [3.92818, 0.23438, 0.00357, 3e-05], - ("Za", "ctt", 5): [4.1869, 0.21627, 0.00288, 2e-05], - ("Za", "ctt", 6): [4.42703, 0.20077, 0.00235, 1e-05], - ("Za", "ctt", 7): [4.66115, 0.18884, 0.00199, 1e-05], - ("Za", "ctt", 8): [4.89591, 0.17898, 0.00171, 1e-05], - ("Za", "ctt", 9): [5.11565, 0.17, 0.00148, 1e-05], - ("Za", "ctt", 10): [5.35214, 0.1637, 0.00132, 1e-05], - ("Za", "ctt", 11): [5.57976, 0.15822, 0.00119, 0.0], - ("Za", "ctt", 12): [5.75345, 0.15089, 0.00104, 0.0], - ("Za", "ctt", 13): [5.96944, 0.14668, 0.00096, 0.0], + ("Zt", "ct", 1): [2.68481, 0.92326, -0.2041, -0.02976], + ("Zt", "ct", 2): [3.03112, 0.84209, -0.20368, -0.02609], + ("Zt", "ct", 3): [3.38363, 0.79321, -0.19905, -0.02307], + ("Zt", "ct", 4): [3.78629, 0.81617, -0.17641, -0.01892], + ("Zt", "ct", 5): [4.20467, 0.86056, -0.15408, -0.01562], + ("Zt", "ct", 6): [4.58939, 0.88765, -0.14008, -0.01352], + ("Zt", "ct", 7): [5.01329, 0.94991, -0.1206, -0.01135], + ("Zt", "ct", 8): [5.50608, 1.06653, -0.09071, -0.00863], + ("Zt", "ct", 9): [5.92017, 1.12896, -0.07547, -0.00719], + ("Zt", "ct", 10): [6.32289, 1.19104, -0.0609, -0.0059], + ("Zt", "ct", 11): [6.74461, 1.26313, -0.04622, -0.00474], + ("Zt", "ct", 12): [7.19292, 1.35272, -0.02914, -0.00353], + ("Zt", "ct", 13): [7.52843, 1.37537, -0.02608, -0.00328], + ("Zt", "c", 1): [1.77481, 0.99261, -0.11947, -0.01715], + ("Zt", "c", 2): [2.29116, 0.82802, -0.19155, -0.02563], + ("Zt", "c", 3): [2.73811, 0.71657, -0.22531, -0.02754], + ("Zt", "c", 4): [3.16312, 0.67533, -0.2259, -0.02534], + ("Zt", "c", 5): [3.63591, 0.73356, -0.19564, -0.02055], + ("Zt", "c", 6): [4.12757, 0.82542, -0.16175, -0.01631], + ("Zt", "c", 7): [4.59201, 0.90378, -0.13593, -0.01327], + ("Zt", "c", 8): [5.02384, 0.96629, -0.11678, -0.01111], + ("Zt", "c", 9): [5.45429, 1.03969, -0.09621, -0.00903], + ("Zt", "c", 10): [5.92022, 1.13424, -0.07412, -0.00711], + ("Zt", "c", 11): [6.37008, 1.22419, -0.05413, -0.00547], + ("Zt", "c", 12): [6.75912, 1.27316, -0.04455, -0.00467], + ("Zt", "c", 13): [7.16747, 1.33941, -0.03188, -0.00373], + ("Zt", "n", 1): [0.46986, 0.98914, -0.0422, 0.00085], + ("Zt", "n", 2): [1.58026, 0.94734, -0.14069, -0.02173], + ("Zt", "n", 3): [2.27624, 0.83916, -0.20483, -0.02952], + ("Zt", "n", 4): [2.82762, 0.80502, -0.20614, -0.02655], + ("Zt", "n", 5): [3.32288, 0.82075, -0.18672, -0.02194], + ("Zt", "n", 6): [3.78339, 0.85138, -0.16671, -0.01824], + ("Zt", "n", 7): [4.27207, 0.93241, -0.13648, -0.01429], + ("Zt", "n", 8): [4.75441, 1.01961, -0.10909, -0.01118], + ("Zt", "n", 9): [5.23069, 1.10872, -0.08477, -0.00871], + ("Zt", "n", 10): [5.63083, 1.14886, -0.0742, -0.00755], + ("Zt", "n", 11): [6.05685, 1.21387, -0.05909, -0.00616], + ("Zt", "n", 12): [6.46141, 1.2709, -0.04654, -0.00506], + ("Zt", "n", 13): [6.83339, 1.31098, -0.03837, -0.00433], + ("Zt", "ctt", 1): [3.35537, 0.93041, -0.20414, -0.0276], + ("Zt", "ctt", 2): [3.70243, 0.93729, -0.17288, -0.02128], + ("Zt", "ctt", 3): [4.06017, 0.94824, -0.15162, -0.01729], + ("Zt", "ctt", 4): [4.44315, 0.98558, -0.12849, -0.01378], + ("Zt", "ctt", 5): [4.78487, 0.98809, -0.12042, -0.01219], + ("Zt", "ctt", 6): [5.15633, 1.01746, -0.10874, -0.01064], + ("Zt", "ctt", 7): [5.57742, 1.08445, -0.09044, -0.0088], + ("Zt", "ctt", 8): [5.95956, 1.13317, -0.07694, -0.00741], + ("Zt", "ctt", 9): [6.4049, 1.22268, -0.05678, -0.00574], + ("Zt", "ctt", 10): [6.8178, 1.29155, -0.0426, -0.00462], + ("Zt", "ctt", 11): [7.18459, 1.33757, -0.03324, -0.00385], + ("Zt", "ctt", 12): [7.61813, 1.42037, -0.01813, -0.00281], + ("Zt", "ctt", 13): [7.94777, 1.44722, -0.01383, -0.00246], + ("Pz", "ct", 1): [3.30155, 0.27713, 0.00592, 5e-05], + ("Pz", "ct", 2): [4.82879, 0.16613, 0.0016, 1e-05], + ("Pz", "ct", 3): [6.27041, 0.12266, 0.00069, 0.0], + ("Pz", "ct", 4): [7.77275, 0.10033, 0.00038, 0.0], + ("Pz", "ct", 5): [9.29828, 0.08613, 0.00024, 0.0], + ("Pz", "ct", 6): [10.67437, 0.07376, 0.00015, 0.0], + ("Pz", "ct", 7): [12.12459, 0.06532, 0.0001, 0.0], + ("Pz", "ct", 8): [13.6131, 0.05906, 8e-05, 0.0], + ("Pz", "ct", 9): [15.05283, 0.05359, 6e-05, 0.0], + ("Pz", "ct", 10): [16.4971, 0.04904, 4e-05, 0.0], + ("Pz", "ct", 11): [17.93937, 0.04534, 3e-05, 0.0], + ("Pz", "ct", 12): [19.28102, 0.04171, 3e-05, 0.0], + ("Pz", "ct", 13): [20.79457, 0.03921, 2e-05, 0.0], + ("Pz", "c", 1): [2.6477, 0.45084, 0.01728, 0.00022], + ("Pz", "c", 2): [3.92845, 0.23004, 0.00361, 2e-05], + ("Pz", "c", 3): [5.2781, 0.15799, 0.00134, 0.0], + ("Pz", "c", 4): [6.63454, 0.12106, 0.00064, 0.0], + ("Pz", "c", 5): [8.12364, 0.10148, 0.00038, 0.0], + ("Pz", "c", 6): [9.47191, 0.08495, 0.00023, 0.0], + ("Pz", "c", 7): [10.91098, 0.07412, 0.00015, 0.0], + ("Pz", "c", 8): [12.36115, 0.06592, 0.00011, 0.0], + ("Pz", "c", 9): [13.83247, 0.05953, 8e-05, 0.0], + ("Pz", "c", 10): [15.25473, 0.05401, 6e-05, 0.0], + ("Pz", "c", 11): [16.64305, 0.04921, 4e-05, 0.0], + ("Pz", "c", 12): [18.20898, 0.04607, 4e-05, 0.0], + ("Pz", "c", 13): [19.65749, 0.04285, 3e-05, 0.0], + ("Pz", "n", 1): [2.2525, 0.80865, 0.04884, 0.00084], + ("Pz", "n", 2): [3.32511, 0.30202, 0.00703, 6e-05], + ("Pz", "n", 3): [4.64192, 0.18952, 0.00216, 1e-05], + ("Pz", "n", 4): [6.11363, 0.14327, 0.00098, 0.0], + ("Pz", "n", 5): [7.42172, 0.11145, 0.00049, 0.0], + ("Pz", "n", 6): [8.68101, 0.09017, 0.00027, 0.0], + ("Pz", "n", 7): [10.08461, 0.07754, 0.00018, 0.0], + ("Pz", "n", 8): [11.55255, 0.06884, 0.00012, 0.0], + ("Pz", "n", 9): [12.96494, 0.06143, 9e-05, 0.0], + ("Pz", "n", 10): [14.60826, 0.05712, 7e-05, 0.0], + ("Pz", "n", 11): [15.91809, 0.05127, 5e-05, 0.0], + ("Pz", "n", 12): [17.32123, 0.047, 4e-05, 0.0], + ("Pz", "n", 13): [18.68509, 0.04326, 3e-05, 0.0], + ("Pz", "ctt", 1): [3.8921, 0.22752, 0.00358, 2e-05], + ("Pz", "ctt", 2): [5.53255, 0.13801, 0.00098, 0.0], + ("Pz", "ctt", 3): [7.14518, 0.10639, 0.00046, 0.0], + ("Pz", "ctt", 4): [8.68702, 0.08778, 0.00026, 0.0], + ("Pz", "ctt", 5): [10.23079, 0.07567, 0.00017, 0.0], + ("Pz", "ctt", 6): [11.71271, 0.06637, 0.00011, 0.0], + ("Pz", "ctt", 7): [13.17416, 0.05913, 8e-05, 0.0], + ("Pz", "ctt", 8): [14.66118, 0.05361, 6e-05, 0.0], + ("Pz", "ctt", 9): [16.26247, 0.04984, 5e-05, 0.0], + ("Pz", "ctt", 10): [17.86932, 0.04654, 4e-05, 0.0], + ("Pz", "ctt", 11): [19.42135, 0.04348, 3e-05, 0.0], + ("Pz", "ctt", 12): [20.856, 0.04038, 2e-05, 0.0], + ("Pz", "ctt", 13): [22.13287, 0.0372, 2e-05, 0.0], } PVAL_SMALL_P = { - ("Pz", "ct", 1): [1.10825, 0.07831, 0.00032], - ("Pz", "ct", 2): [2.4457, 0.06321, 0.00015], - ("Pz", "ct", 3): [3.64959, 0.05346, 8e-05], - ("Pz", "ct", 4): [4.85576, 0.04694, 5e-05], - ("Pz", "ct", 5): [6.09234, 0.04224, 4e-05], - ("Pz", "ct", 6): [7.12876, 0.03715, 3e-05], - ("Pz", "ct", 7): [8.34373, 0.03412, 2e-05], - ("Pz", "ct", 8): [9.54049, 0.03146, 1e-05], - ("Pz", "ct", 9): [10.59897, 0.02872, 1e-05], - ("Pz", "ct", 10): [12.04316, 0.02762, 1e-05], - ("Pz", "ct", 11): [12.96214, 0.02507, 1e-05], - ("Pz", "ct", 12): [13.97577, 0.02321, 1e-05], - ("Pz", "ct", 13): [15.20083, 0.02203, 0.0], - ("Pz", "n", 1): [-0.07982, 0.10301, 0.00086], - ("Pz", "n", 2): [1.08807, 0.08248, 0.00037], - ("Pz", "n", 3): [2.17245, 0.06651, 0.00018], - ("Pz", "n", 4): [3.26857, 0.05593, 0.0001], - ("Pz", "n", 5): [4.45316, 0.04945, 6e-05], - ("Pz", "n", 6): [5.58591, 0.0437, 4e-05], - ("Pz", "n", 7): [6.86709, 0.0402, 3e-05], - ("Pz", "n", 8): [8.04175, 0.03654, 2e-05], - ("Pz", "n", 9): [9.21748, 0.03344, 2e-05], - ("Pz", "n", 10): [10.16866, 0.0299, 1e-05], - ("Pz", "n", 11): [11.53691, 0.0285, 1e-05], - ("Pz", "n", 12): [13.1809, 0.02797, 1e-05], - ("Pz", "n", 13): [13.75407, 0.02462, 1e-05], - ("Pz", "c", 1): [0.44472, 0.09152, 0.00055], - ("Pz", "c", 2): [1.62448, 0.07365, 0.00025], - ("Pz", "c", 3): [2.74685, 0.06135, 0.00013], - ("Pz", "c", 4): [3.82537, 0.05198, 8e-05], - ("Pz", "c", 5): [4.99969, 0.04615, 5e-05], - ("Pz", "c", 6): [6.20557, 0.04178, 4e-05], - ("Pz", "c", 7): [7.32709, 0.0375, 3e-05], - ("Pz", "c", 8): [8.55055, 0.03455, 2e-05], - ("Pz", "c", 9): [9.73265, 0.03183, 1e-05], - ("Pz", "c", 10): [10.64803, 0.02852, 1e-05], - ("Pz", "c", 11): [12.06785, 0.0274, 1e-05], - ("Pz", "c", 12): [13.08064, 0.02516, 1e-05], - ("Pz", "c", 13): [14.17322, 0.02346, 1e-05], - ("Pz", "ctt", 1): [1.57074, 0.07104, 0.00023], - ("Pz", "ctt", 2): [3.04958, 0.05684, 0.00011], - ("Pz", "ctt", 3): [4.29533, 0.04757, 6e-05], - ("Pz", "ctt", 4): [5.61063, 0.04249, 4e-05], - ("Pz", "ctt", 5): [6.84772, 0.03814, 3e-05], - ("Pz", "ctt", 6): [8.08163, 0.03474, 2e-05], - ("Pz", "ctt", 7): [9.28456, 0.03185, 2e-05], - ("Pz", "ctt", 8): [10.52276, 0.02955, 1e-05], - ("Pz", "ctt", 9): [11.46108, 0.02659, 1e-05], - ("Pz", "ctt", 10): [12.83181, 0.02541, 1e-05], - ("Pz", "ctt", 11): [14.03409, 0.02389, 1e-05], - ("Pz", "ctt", 12): [15.24091, 0.02256, 0.0], - ("Pz", "ctt", 13): [16.65873, 0.02178, 0.0], + ("Za", "ct", 1): [1.11752, 0.15391, 0.00123], + ("Za", "ct", 2): [1.41231, 0.13526, 0.00085], + ("Za", "ct", 3): [1.73026, 0.12497, 0.00066], + ("Za", "ct", 4): [2.01787, 0.11678, 0.00054], + ("Za", "ct", 5): [2.28591, 0.11023, 0.00045], + ("Za", "ct", 6): [2.53357, 0.10482, 0.00038], + ("Za", "ct", 7): [2.7612, 0.10005, 0.00033], + ("Za", "ct", 8): [2.99547, 0.09655, 0.0003], + ("Za", "ct", 9): [3.18872, 0.09251, 0.00026], + ("Za", "ct", 10): [3.41738, 0.09026, 0.00024], + ("Za", "ct", 11): [3.61744, 0.0877, 0.00022], + ("Za", "ct", 12): [3.78533, 0.08476, 0.00019], + ("Za", "ct", 13): [3.96067, 0.08254, 0.00018], + ("Za", "c", 1): [0.4913, 0.18147, 0.00213], + ("Za", "c", 2): [0.96676, 0.15327, 0.00128], + ("Za", "c", 3): [1.34059, 0.13567, 0.00088], + ("Za", "c", 4): [1.67053, 0.1241, 0.00066], + ("Za", "c", 5): [1.98992, 0.11671, 0.00054], + ("Za", "c", 6): [2.25421, 0.10972, 0.00045], + ("Za", "c", 7): [2.52674, 0.10508, 0.00039], + ("Za", "c", 8): [2.77462, 0.10084, 0.00034], + ("Za", "c", 9): [3.00987, 0.09723, 0.0003], + ("Za", "c", 10): [3.19629, 0.09294, 0.00026], + ("Za", "c", 11): [3.41378, 0.09028, 0.00024], + ("Za", "c", 12): [3.59244, 0.08718, 0.00021], + ("Za", "c", 13): [3.81646, 0.08569, 0.0002], + ("Za", "n", 1): [-0.21823, 0.20578, 0.00364], + ("Za", "n", 2): [0.49355, 0.16004, 0.00159], + ("Za", "n", 3): [0.99241, 0.1414, 0.00104], + ("Za", "n", 4): [1.37672, 0.12848, 0.00076], + ("Za", "n", 5): [1.73102, 0.12054, 0.00062], + ("Za", "n", 6): [2.01475, 0.11253, 0.00049], + ("Za", "n", 7): [2.28639, 0.10667, 0.00042], + ("Za", "n", 8): [2.55373, 0.10245, 0.00036], + ("Za", "n", 9): [2.79763, 0.09848, 0.00032], + ("Za", "n", 10): [3.0312, 0.0952, 0.00029], + ("Za", "n", 11): [3.22351, 0.09144, 0.00025], + ("Za", "n", 12): [3.4069, 0.08811, 0.00022], + ("Za", "n", 13): [3.59651, 0.08548, 0.0002], + ("Za", "ctt", 1): [1.56722, 0.13949, 0.00089], + ("Za", "ctt", 2): [1.83105, 0.12722, 0.00068], + ("Za", "ctt", 3): [2.07674, 0.11766, 0.00054], + ("Za", "ctt", 4): [2.3134, 0.11013, 0.00045], + ("Za", "ctt", 5): [2.56584, 0.10521, 0.00039], + ("Za", "ctt", 6): [2.78229, 0.10026, 0.00033], + ("Za", "ctt", 7): [2.99932, 0.09634, 0.00029], + ("Za", "ctt", 8): [3.21018, 0.09297, 0.00026], + ("Za", "ctt", 9): [3.41628, 0.09009, 0.00024], + ("Za", "ctt", 10): [3.59956, 0.08713, 0.00021], + ("Za", "ctt", 11): [3.792, 0.08486, 0.0002], + ("Za", "ctt", 12): [3.98661, 0.08302, 0.00018], + ("Za", "ctt", 13): [4.15584, 0.08093, 0.00017], ("Pu", "ct", 1): [1.10825, 0.07831, 0.00032], ("Pu", "ct", 2): [1.3015, 0.07447, 0.00027], ("Pu", "ct", 3): [1.4747, 0.07079, 0.00023], @@ -1544,19 +1544,6 @@ ("Pu", "ct", 11): [2.57954, 0.0539, 0.0001], ("Pu", "ct", 12): [2.69661, 0.05265, 9e-05], ("Pu", "ct", 13): [2.80268, 0.0514, 9e-05], - ("Pu", "n", 1): [-0.07982, 0.10301, 0.00086], - ("Pu", "n", 2): [0.33922, 0.08968, 0.00054], - ("Pu", "n", 3): [0.63626, 0.0815, 0.0004], - ("Pu", "n", 4): [0.88355, 0.07573, 0.00031], - ("Pu", "n", 5): [1.10028, 0.07128, 0.00026], - ("Pu", "n", 6): [1.27202, 0.06695, 0.00021], - ("Pu", "n", 7): [1.47622, 0.06501, 0.00019], - ("Pu", "n", 8): [1.63721, 0.06219, 0.00016], - ("Pu", "n", 9): [1.77449, 0.05948, 0.00014], - ("Pu", "n", 10): [1.95281, 0.0584, 0.00013], - ("Pu", "n", 11): [2.07954, 0.05635, 0.00012], - ("Pu", "n", 12): [2.2371, 0.05539, 0.00011], - ("Pu", "n", 13): [2.40218, 0.0548, 0.00011], ("Pu", "c", 1): [0.44472, 0.09152, 0.00055], ("Pu", "c", 2): [0.69677, 0.08227, 0.0004], ("Pu", "c", 3): [0.9396, 0.07681, 0.00032], @@ -1570,6 +1557,19 @@ ("Pu", "c", 11): [2.25113, 0.05569, 0.00011], ("Pu", "c", 12): [2.40784, 0.05488, 0.00011], ("Pu", "c", 13): [2.53353, 0.05356, 0.0001], + ("Pu", "n", 1): [-0.07982, 0.10301, 0.00086], + ("Pu", "n", 2): [0.33922, 0.08968, 0.00054], + ("Pu", "n", 3): [0.63626, 0.0815, 0.0004], + ("Pu", "n", 4): [0.88355, 0.07573, 0.00031], + ("Pu", "n", 5): [1.10028, 0.07128, 0.00026], + ("Pu", "n", 6): [1.27202, 0.06695, 0.00021], + ("Pu", "n", 7): [1.47622, 0.06501, 0.00019], + ("Pu", "n", 8): [1.63721, 0.06219, 0.00016], + ("Pu", "n", 9): [1.77449, 0.05948, 0.00014], + ("Pu", "n", 10): [1.95281, 0.0584, 0.00013], + ("Pu", "n", 11): [2.07954, 0.05635, 0.00012], + ("Pu", "n", 12): [2.2371, 0.05539, 0.00011], + ("Pu", "n", 13): [2.40218, 0.0548, 0.00011], ("Pu", "ctt", 1): [1.57074, 0.07104, 0.00023], ("Pu", "ctt", 2): [1.70927, 0.06762, 0.0002], ("Pu", "ctt", 3): [1.84074, 0.06465, 0.00017], @@ -1583,165 +1583,165 @@ ("Pu", "ctt", 11): [2.82877, 0.0514, 9e-05], ("Pu", "ctt", 12): [2.96476, 0.05083, 8e-05], ("Pu", "ctt", 13): [3.06455, 0.04974, 8e-05], - ("Zt", "ct", 1): [3.18724, 1.56771, 0.045], - ("Zt", "ct", 2): [3.59045, 1.50363, 0.03215], - ("Zt", "ct", 3): [4.11572, 1.52657, 0.03174], - ("Zt", "ct", 4): [4.69115, 1.58426, 0.03526], - ("Zt", "ct", 5): [5.1054, 1.58076, 0.03227], - ("Zt", "ct", 6): [5.58155, 1.61407, 0.03347], - ("Zt", "ct", 7): [5.93045, 1.60608, 0.03095], - ("Zt", "ct", 8): [6.34189, 1.62636, 0.03109], - ("Zt", "ct", 9): [6.55242, 1.58076, 0.02552], - ("Zt", "ct", 10): [6.9459, 1.60535, 0.02637], - ("Zt", "ct", 11): [7.24021, 1.59972, 0.02471], - ("Zt", "ct", 12): [7.5141, 1.59158, 0.02302], - ("Zt", "ct", 13): [7.86472, 1.61449, 0.02415], - ("Zt", "n", 1): [0.62942, 1.23238, 0.03144], - ("Zt", "n", 2): [1.85249, 1.34407, 0.02857], - ("Zt", "n", 3): [2.72385, 1.42548, 0.03096], - ("Zt", "n", 4): [3.38215, 1.4584, 0.02952], - ("Zt", "n", 5): [4.0511, 1.53099, 0.03401], - ("Zt", "n", 6): [4.53129, 1.5341, 0.03074], - ("Zt", "n", 7): [5.01319, 1.55535, 0.03019], - ("Zt", "n", 8): [5.5283, 1.60115, 0.0325], - ("Zt", "n", 9): [5.97835, 1.62674, 0.03278], - ("Zt", "n", 10): [6.34659, 1.62976, 0.03128], - ("Zt", "n", 11): [6.65906, 1.62113, 0.02912], - ("Zt", "n", 12): [6.94677, 1.60889, 0.02677], - ("Zt", "n", 13): [7.23633, 1.60235, 0.02513], - ("Zt", "c", 1): [2.16792, 1.44569, 0.04001], - ("Zt", "c", 2): [2.89115, 1.48481, 0.038], - ("Zt", "c", 3): [3.49185, 1.50011, 0.03446], - ("Zt", "c", 4): [4.01541, 1.50495, 0.03067], - ("Zt", "c", 5): [4.5997, 1.55918, 0.03336], - ("Zt", "c", 6): [5.02296, 1.5574, 0.03056], - ("Zt", "c", 7): [5.55725, 1.61163, 0.03368], - ("Zt", "c", 8): [5.95982, 1.62071, 0.03245], - ("Zt", "c", 9): [6.2493, 1.59503, 0.02831], - ("Zt", "c", 10): [6.59621, 1.59814, 0.02711], - ("Zt", "c", 11): [7.04019, 1.63704, 0.02894], - ("Zt", "c", 12): [7.37087, 1.64437, 0.02843], - ("Zt", "c", 13): [7.7859, 1.68005, 0.03011], - ("Zt", "ctt", 1): [3.98192, 1.64795, 0.04755], - ("Zt", "ctt", 2): [4.34414, 1.59963, 0.03862], - ("Zt", "ctt", 3): [4.74761, 1.58797, 0.03491], - ("Zt", "ctt", 4): [5.14536, 1.58351, 0.03214], - ("Zt", "ctt", 5): [5.70292, 1.65177, 0.03688], - ("Zt", "ctt", 6): [6.02382, 1.63404, 0.03337], - ("Zt", "ctt", 7): [6.36813, 1.63284, 0.03172], - ("Zt", "ctt", 8): [6.70627, 1.6321, 0.03004], - ("Zt", "ctt", 9): [7.08593, 1.65079, 0.03022], - ("Zt", "ctt", 10): [7.40078, 1.65242, 0.02918], - ("Zt", "ctt", 11): [7.77104, 1.67367, 0.02966], - ("Zt", "ctt", 12): [8.09944, 1.68454, 0.02941], - ("Zt", "ctt", 13): [8.35146, 1.67696, 0.028], - ("Za", "ct", 1): [1.11899, 0.1541, 0.00123], - ("Za", "ct", 2): [1.41066, 0.13522, 0.00085], - ("Za", "ct", 3): [1.72978, 0.12494, 0.00066], - ("Za", "ct", 4): [2.02833, 0.11733, 0.00055], - ("Za", "ct", 5): [2.2933, 0.11057, 0.00045], - ("Za", "ct", 6): [2.54552, 0.10532, 0.00039], - ("Za", "ct", 7): [2.77104, 0.10046, 0.00034], - ("Za", "ct", 8): [3.01481, 0.09718, 0.0003], - ("Za", "ct", 9): [3.19074, 0.09257, 0.00026], - ("Za", "ct", 10): [3.41096, 0.09009, 0.00024], - ("Za", "ct", 11): [3.60625, 0.0874, 0.00021], - ("Za", "ct", 12): [3.79165, 0.08487, 0.0002], - ("Za", "ct", 13): [3.96037, 0.0825, 0.00018], - ("Za", "n", 1): [-0.21953, 0.20552, 0.00363], - ("Za", "n", 2): [0.49257, 0.15995, 0.00158], - ("Za", "n", 3): [0.99497, 0.14167, 0.00105], - ("Za", "n", 4): [1.37446, 0.12835, 0.00076], - ("Za", "n", 5): [1.72909, 0.12041, 0.00062], - ("Za", "n", 6): [2.01628, 0.11259, 0.0005], - ("Za", "n", 7): [2.28498, 0.10661, 0.00042], - ("Za", "n", 8): [2.55791, 0.10261, 0.00037], - ("Za", "n", 9): [2.7979, 0.09851, 0.00032], - ("Za", "n", 10): [3.03338, 0.09525, 0.00029], - ("Za", "n", 11): [3.22007, 0.09134, 0.00025], - ("Za", "n", 12): [3.40559, 0.08808, 0.00022], - ("Za", "n", 13): [3.5933, 0.08541, 0.0002], - ("Za", "c", 1): [0.48905, 0.18136, 0.00213], - ("Za", "c", 2): [0.96287, 0.15284, 0.00127], - ("Za", "c", 3): [1.33484, 0.13531, 0.00087], - ("Za", "c", 4): [1.6847, 0.12489, 0.00067], - ("Za", "c", 5): [1.99377, 0.11692, 0.00054], - ("Za", "c", 6): [2.25527, 0.1098, 0.00045], - ("Za", "c", 7): [2.52876, 0.10518, 0.00039], - ("Za", "c", 8): [2.77406, 0.10089, 0.00034], - ("Za", "c", 9): [2.99549, 0.09678, 0.0003], - ("Za", "c", 10): [3.19662, 0.09294, 0.00026], - ("Za", "c", 11): [3.41271, 0.09024, 0.00024], - ("Za", "c", 12): [3.61327, 0.08772, 0.00022], - ("Za", "c", 13): [3.82406, 0.08588, 0.0002], - ("Za", "ctt", 1): [1.57523, 0.14012, 0.0009], - ("Za", "ctt", 2): [1.83675, 0.12756, 0.00069], - ("Za", "ctt", 3): [2.08297, 0.11801, 0.00055], - ("Za", "ctt", 4): [2.32027, 0.11049, 0.00045], - ("Za", "ctt", 5): [2.57943, 0.10577, 0.00039], - ("Za", "ctt", 6): [2.79561, 0.10078, 0.00034], - ("Za", "ctt", 7): [3.00833, 0.09673, 0.0003], - ("Za", "ctt", 8): [3.22264, 0.09341, 0.00027], - ("Za", "ctt", 9): [3.43125, 0.09057, 0.00024], - ("Za", "ctt", 10): [3.60398, 0.0873, 0.00021], - ("Za", "ctt", 11): [3.80529, 0.08522, 0.0002], - ("Za", "ctt", 12): [3.99054, 0.08312, 0.00018], - ("Za", "ctt", 13): [4.14441, 0.08069, 0.00017], + ("Zt", "ct", 1): [3.19959, 1.57405, 0.04576], + ("Zt", "ct", 2): [3.61862, 1.5178, 0.03389], + ("Zt", "ct", 3): [4.10331, 1.52107, 0.03114], + ("Zt", "ct", 4): [4.64798, 1.5653, 0.03321], + ("Zt", "ct", 5): [5.07426, 1.56727, 0.03081], + ("Zt", "ct", 6): [5.52258, 1.58992, 0.03101], + ("Zt", "ct", 7): [5.87451, 1.58345, 0.02867], + ("Zt", "ct", 8): [6.29535, 1.60912, 0.02949], + ("Zt", "ct", 9): [6.60005, 1.59706, 0.0269], + ("Zt", "ct", 10): [7.01021, 1.6263, 0.02804], + ("Zt", "ct", 11): [7.33165, 1.62891, 0.027], + ("Zt", "ct", 12): [7.56691, 1.60911, 0.02444], + ("Zt", "ct", 13): [7.94648, 1.63976, 0.02607], + ("Zt", "c", 1): [2.16414, 1.44165, 0.03913], + ("Zt", "c", 2): [2.88981, 1.48452, 0.03802], + ("Zt", "c", 3): [3.52327, 1.51581, 0.03639], + ("Zt", "c", 4): [4.01892, 1.50737, 0.03104], + ("Zt", "c", 5): [4.57251, 1.54719, 0.03206], + ("Zt", "c", 6): [5.00302, 1.54918, 0.02974], + ("Zt", "c", 7): [5.5214, 1.59719, 0.03226], + ("Zt", "c", 8): [5.94637, 1.61453, 0.0318], + ("Zt", "c", 9): [6.3261, 1.62219, 0.03072], + ("Zt", "c", 10): [6.61042, 1.60343, 0.02763], + ("Zt", "c", 11): [7.03301, 1.63505, 0.02884], + ("Zt", "c", 12): [7.30148, 1.62286, 0.0268], + ("Zt", "c", 13): [7.7907, 1.68207, 0.03035], + ("Zt", "n", 1): [0.6288, 1.23198, 0.03149], + ("Zt", "n", 2): [1.8589, 1.34906, 0.02961], + ("Zt", "n", 3): [2.71812, 1.4213, 0.03026], + ("Zt", "n", 4): [3.38477, 1.45916, 0.02952], + ("Zt", "n", 5): [4.06128, 1.53611, 0.03464], + ("Zt", "n", 6): [4.52196, 1.52978, 0.03021], + ("Zt", "n", 7): [5.00332, 1.55089, 0.02967], + ("Zt", "n", 8): [5.51356, 1.59518, 0.03188], + ("Zt", "n", 9): [5.95837, 1.61882, 0.03199], + ("Zt", "n", 10): [6.3487, 1.63043, 0.03131], + ("Zt", "n", 11): [6.66712, 1.62358, 0.02928], + ("Zt", "n", 12): [6.94753, 1.60888, 0.02675], + ("Zt", "n", 13): [7.2512, 1.60671, 0.02543], + ("Zt", "ctt", 1): [3.96093, 1.63684, 0.04613], + ("Zt", "ctt", 2): [4.34612, 1.59993, 0.03859], + ("Zt", "ctt", 3): [4.72665, 1.57764, 0.03366], + ("Zt", "ctt", 4): [5.13788, 1.57901, 0.03151], + ("Zt", "ctt", 5): [5.61605, 1.6165, 0.03329], + ("Zt", "ctt", 6): [5.9396, 1.60171, 0.03028], + ("Zt", "ctt", 7): [6.29867, 1.60619, 0.02918], + ("Zt", "ctt", 8): [6.63736, 1.60712, 0.02778], + ("Zt", "ctt", 9): [7.00595, 1.62295, 0.02781], + ("Zt", "ctt", 10): [7.36275, 1.63915, 0.02802], + ("Zt", "ctt", 11): [7.69211, 1.64901, 0.02774], + ("Zt", "ctt", 12): [8.07196, 1.67631, 0.02881], + ("Zt", "ctt", 13): [8.35706, 1.6783, 0.02809], + ("Pz", "ct", 1): [1.10825, 0.07831, 0.00032], + ("Pz", "ct", 2): [2.4457, 0.06321, 0.00015], + ("Pz", "ct", 3): [3.64959, 0.05346, 8e-05], + ("Pz", "ct", 4): [4.85576, 0.04694, 5e-05], + ("Pz", "ct", 5): [6.09234, 0.04224, 4e-05], + ("Pz", "ct", 6): [7.12876, 0.03715, 3e-05], + ("Pz", "ct", 7): [8.34373, 0.03412, 2e-05], + ("Pz", "ct", 8): [9.54049, 0.03146, 1e-05], + ("Pz", "ct", 9): [10.59897, 0.02872, 1e-05], + ("Pz", "ct", 10): [12.04316, 0.02762, 1e-05], + ("Pz", "ct", 11): [12.96214, 0.02507, 1e-05], + ("Pz", "ct", 12): [13.97577, 0.02321, 1e-05], + ("Pz", "ct", 13): [15.20083, 0.02203, 0.0], + ("Pz", "c", 1): [0.44472, 0.09152, 0.00055], + ("Pz", "c", 2): [1.62448, 0.07365, 0.00025], + ("Pz", "c", 3): [2.74685, 0.06135, 0.00013], + ("Pz", "c", 4): [3.82537, 0.05198, 8e-05], + ("Pz", "c", 5): [4.99969, 0.04615, 5e-05], + ("Pz", "c", 6): [6.20557, 0.04178, 4e-05], + ("Pz", "c", 7): [7.32709, 0.0375, 3e-05], + ("Pz", "c", 8): [8.55055, 0.03455, 2e-05], + ("Pz", "c", 9): [9.73265, 0.03183, 1e-05], + ("Pz", "c", 10): [10.64803, 0.02852, 1e-05], + ("Pz", "c", 11): [12.06785, 0.0274, 1e-05], + ("Pz", "c", 12): [13.08064, 0.02516, 1e-05], + ("Pz", "c", 13): [14.17322, 0.02346, 1e-05], + ("Pz", "n", 1): [-0.07982, 0.10301, 0.00086], + ("Pz", "n", 2): [1.08807, 0.08248, 0.00037], + ("Pz", "n", 3): [2.17245, 0.06651, 0.00018], + ("Pz", "n", 4): [3.26857, 0.05593, 0.0001], + ("Pz", "n", 5): [4.45316, 0.04945, 6e-05], + ("Pz", "n", 6): [5.58591, 0.0437, 4e-05], + ("Pz", "n", 7): [6.86709, 0.0402, 3e-05], + ("Pz", "n", 8): [8.04175, 0.03654, 2e-05], + ("Pz", "n", 9): [9.21748, 0.03344, 2e-05], + ("Pz", "n", 10): [10.16866, 0.0299, 1e-05], + ("Pz", "n", 11): [11.53691, 0.0285, 1e-05], + ("Pz", "n", 12): [13.1809, 0.02797, 1e-05], + ("Pz", "n", 13): [13.75407, 0.02462, 1e-05], + ("Pz", "ctt", 1): [1.57074, 0.07104, 0.00023], + ("Pz", "ctt", 2): [3.04958, 0.05684, 0.00011], + ("Pz", "ctt", 3): [4.29533, 0.04757, 6e-05], + ("Pz", "ctt", 4): [5.61063, 0.04249, 4e-05], + ("Pz", "ctt", 5): [6.84772, 0.03814, 3e-05], + ("Pz", "ctt", 6): [8.08163, 0.03474, 2e-05], + ("Pz", "ctt", 7): [9.28456, 0.03185, 2e-05], + ("Pz", "ctt", 8): [10.52276, 0.02955, 1e-05], + ("Pz", "ctt", 9): [11.46108, 0.02659, 1e-05], + ("Pz", "ctt", 10): [12.83181, 0.02541, 1e-05], + ("Pz", "ctt", 11): [14.03409, 0.02389, 1e-05], + ("Pz", "ctt", 12): [15.24091, 0.02256, 0.0], + ("Pz", "ctt", 13): [16.65873, 0.02178, 0.0], } PVAL_TAU_MAX = { - ("Pz", "ct", 1): inf, - ("Pz", "ct", 2): inf, - ("Pz", "ct", 3): inf, - ("Pz", "ct", 4): inf, - ("Pz", "ct", 5): inf, - ("Pz", "ct", 6): inf, - ("Pz", "ct", 7): inf, - ("Pz", "ct", 8): inf, - ("Pz", "ct", 9): inf, - ("Pz", "ct", 10): inf, - ("Pz", "ct", 11): inf, - ("Pz", "ct", 12): inf, - ("Pz", "ct", 13): inf, - ("Pz", "n", 1): -11.96617, - ("Pz", "n", 2): inf, - ("Pz", "n", 3): inf, - ("Pz", "n", 4): inf, - ("Pz", "n", 5): inf, - ("Pz", "n", 6): inf, - ("Pz", "n", 7): inf, - ("Pz", "n", 8): inf, - ("Pz", "n", 9): inf, - ("Pz", "n", 10): inf, - ("Pz", "n", 11): inf, - ("Pz", "n", 12): inf, - ("Pz", "n", 13): inf, - ("Pz", "c", 1): -22.99996, - ("Pz", "c", 2): inf, - ("Pz", "c", 3): inf, - ("Pz", "c", 4): inf, - ("Pz", "c", 5): inf, - ("Pz", "c", 6): inf, - ("Pz", "c", 7): inf, - ("Pz", "c", 8): inf, - ("Pz", "c", 9): inf, - ("Pz", "c", 10): inf, - ("Pz", "c", 11): inf, - ("Pz", "c", 12): inf, - ("Pz", "c", 13): inf, - ("Pz", "ctt", 1): inf, - ("Pz", "ctt", 2): inf, - ("Pz", "ctt", 3): inf, - ("Pz", "ctt", 4): inf, - ("Pz", "ctt", 5): inf, - ("Pz", "ctt", 6): inf, - ("Pz", "ctt", 7): inf, - ("Pz", "ctt", 8): inf, - ("Pz", "ctt", 9): inf, - ("Pz", "ctt", 10): inf, - ("Pz", "ctt", 11): inf, - ("Pz", "ctt", 12): inf, - ("Pz", "ctt", 13): inf, + ("Za", "ct", 1): inf, + ("Za", "ct", 2): inf, + ("Za", "ct", 3): inf, + ("Za", "ct", 4): inf, + ("Za", "ct", 5): inf, + ("Za", "ct", 6): inf, + ("Za", "ct", 7): inf, + ("Za", "ct", 8): inf, + ("Za", "ct", 9): inf, + ("Za", "ct", 10): inf, + ("Za", "ct", 11): inf, + ("Za", "ct", 12): inf, + ("Za", "ct", 13): inf, + ("Za", "c", 1): inf, + ("Za", "c", 2): inf, + ("Za", "c", 3): inf, + ("Za", "c", 4): inf, + ("Za", "c", 5): inf, + ("Za", "c", 6): inf, + ("Za", "c", 7): inf, + ("Za", "c", 8): inf, + ("Za", "c", 9): inf, + ("Za", "c", 10): inf, + ("Za", "c", 11): inf, + ("Za", "c", 12): inf, + ("Za", "c", 13): inf, + ("Za", "n", 1): -8.55247, + ("Za", "n", 2): inf, + ("Za", "n", 3): inf, + ("Za", "n", 4): inf, + ("Za", "n", 5): inf, + ("Za", "n", 6): inf, + ("Za", "n", 7): inf, + ("Za", "n", 8): inf, + ("Za", "n", 9): inf, + ("Za", "n", 10): inf, + ("Za", "n", 11): inf, + ("Za", "n", 12): inf, + ("Za", "n", 13): inf, + ("Za", "ctt", 1): inf, + ("Za", "ctt", 2): inf, + ("Za", "ctt", 3): inf, + ("Za", "ctt", 4): inf, + ("Za", "ctt", 5): inf, + ("Za", "ctt", 6): inf, + ("Za", "ctt", 7): inf, + ("Za", "ctt", 8): inf, + ("Za", "ctt", 9): inf, + ("Za", "ctt", 10): inf, + ("Za", "ctt", 11): inf, + ("Za", "ctt", 12): inf, + ("Za", "ctt", 13): inf, ("Pu", "ct", 1): inf, ("Pu", "ct", 2): inf, ("Pu", "ct", 3): inf, @@ -1755,19 +1755,6 @@ ("Pu", "ct", 11): inf, ("Pu", "ct", 12): inf, ("Pu", "ct", 13): inf, - ("Pu", "n", 1): -11.96617, - ("Pu", "n", 2): -20.00694, - ("Pu", "n", 3): inf, - ("Pu", "n", 4): inf, - ("Pu", "n", 5): inf, - ("Pu", "n", 6): inf, - ("Pu", "n", 7): inf, - ("Pu", "n", 8): inf, - ("Pu", "n", 9): inf, - ("Pu", "n", 10): inf, - ("Pu", "n", 11): inf, - ("Pu", "n", 12): inf, - ("Pu", "n", 13): inf, ("Pu", "c", 1): -22.99996, ("Pu", "c", 2): inf, ("Pu", "c", 3): inf, @@ -1781,6 +1768,19 @@ ("Pu", "c", 11): inf, ("Pu", "c", 12): inf, ("Pu", "c", 13): inf, + ("Pu", "n", 1): -11.96617, + ("Pu", "n", 2): -20.00694, + ("Pu", "n", 3): inf, + ("Pu", "n", 4): inf, + ("Pu", "n", 5): inf, + ("Pu", "n", 6): inf, + ("Pu", "n", 7): inf, + ("Pu", "n", 8): inf, + ("Pu", "n", 9): inf, + ("Pu", "n", 10): inf, + ("Pu", "n", 11): inf, + ("Pu", "n", 12): inf, + ("Pu", "n", 13): inf, ("Pu", "ctt", 1): inf, ("Pu", "ctt", 2): inf, ("Pu", "ctt", 3): inf, @@ -1794,165 +1794,165 @@ ("Pu", "ctt", 11): inf, ("Pu", "ctt", 12): inf, ("Pu", "ctt", 13): inf, - ("Zt", "ct", 1): 1.67987, - ("Zt", "ct", 2): 1.59323, - ("Zt", "ct", 3): 1.57669, - ("Zt", "ct", 4): 1.80885, - ("Zt", "ct", 5): 2.12478, - ("Zt", "ct", 6): 2.3113, - ("Zt", "ct", 7): 2.84049, - ("Zt", "ct", 8): 3.82712, - ("Zt", "ct", 9): 4.54973, - ("Zt", "ct", 10): 5.55955, - ("Zt", "ct", 11): 6.54362, - ("Zt", "ct", 12): 9.72703, - ("Zt", "ct", 13): 9.91719, + ("Zt", "ct", 1): 1.65947, + ("Zt", "ct", 2): 1.58471, + ("Zt", "ct", 3): 1.56604, + ("Zt", "ct", 4): 1.79507, + ("Zt", "ct", 5): 2.11329, + ("Zt", "ct", 6): 2.36102, + ("Zt", "ct", 7): 2.81773, + ("Zt", "ct", 8): 3.80915, + ("Zt", "ct", 9): 4.53658, + ("Zt", "ct", 10): 5.45603, + ("Zt", "ct", 11): 6.71718, + ("Zt", "ct", 12): 8.88304, + ("Zt", "ct", 13): 9.47059, + ("Zt", "c", 1): 2.6462, + ("Zt", "c", 2): 1.6289, + ("Zt", "c", 3): 1.28661, + ("Zt", "c", 4): 1.23722, + ("Zt", "c", 5): 1.51369, + ("Zt", "c", 6): 1.96658, + ("Zt", "c", 7): 2.44712, + ("Zt", "c", 8): 2.9202, + ("Zt", "c", 9): 3.5894, + ("Zt", "c", 10): 4.60284, + ("Zt", "c", 11): 5.94713, + ("Zt", "c", 12): 6.86794, + ("Zt", "c", 13): 8.45955, ("Zt", "n", 1): inf, - ("Zt", "n", 2): 2.23243, - ("Zt", "n", 3): 1.52767, - ("Zt", "n", 4): 1.5095, - ("Zt", "n", 5): 1.64486, - ("Zt", "n", 6): 1.94615, - ("Zt", "n", 7): 2.46069, - ("Zt", "n", 8): 3.15425, - ("Zt", "n", 9): 4.06629, - ("Zt", "n", 10): 4.71672, - ("Zt", "n", 11): 5.66133, - ("Zt", "n", 12): 6.57937, - ("Zt", "n", 13): 7.3911, - ("Zt", "c", 1): 2.62677, - ("Zt", "c", 2): 1.63265, - ("Zt", "c", 3): 1.3, - ("Zt", "c", 4): 1.23265, - ("Zt", "c", 5): 1.45176, - ("Zt", "c", 6): 1.86961, - ("Zt", "c", 7): 2.43942, - ("Zt", "c", 8): 2.82202, - ("Zt", "c", 9): 3.55681, - ("Zt", "c", 10): 4.20356, - ("Zt", "c", 11): 5.77727, - ("Zt", "c", 12): 6.43462, - ("Zt", "c", 13): 8.37826, - ("Zt", "ctt", 1): 1.68607, - ("Zt", "ctt", 2): 1.94855, - ("Zt", "ctt", 3): 2.198, - ("Zt", "ctt", 4): 2.68687, - ("Zt", "ctt", 5): 2.97606, - ("Zt", "ctt", 6): 3.3135, - ("Zt", "ctt", 7): 3.9976, - ("Zt", "ctt", 8): 4.45351, - ("Zt", "ctt", 9): 5.53728, - ("Zt", "ctt", 10): 6.98297, - ("Zt", "ctt", 11): 7.92146, - ("Zt", "ctt", 12): 10.80158, - ("Zt", "ctt", 13): 11.53969, - ("Za", "ct", 1): inf, - ("Za", "ct", 2): inf, - ("Za", "ct", 3): inf, - ("Za", "ct", 4): inf, - ("Za", "ct", 5): inf, - ("Za", "ct", 6): inf, - ("Za", "ct", 7): inf, - ("Za", "ct", 8): inf, - ("Za", "ct", 9): inf, - ("Za", "ct", 10): inf, - ("Za", "ct", 11): inf, - ("Za", "ct", 12): inf, - ("Za", "ct", 13): inf, - ("Za", "n", 1): -8.55894, - ("Za", "n", 2): inf, - ("Za", "n", 3): inf, - ("Za", "n", 4): inf, - ("Za", "n", 5): inf, - ("Za", "n", 6): inf, - ("Za", "n", 7): inf, - ("Za", "n", 8): inf, - ("Za", "n", 9): inf, - ("Za", "n", 10): inf, - ("Za", "n", 11): inf, - ("Za", "n", 12): inf, - ("Za", "n", 13): inf, - ("Za", "c", 1): inf, - ("Za", "c", 2): inf, - ("Za", "c", 3): inf, - ("Za", "c", 4): inf, - ("Za", "c", 5): inf, - ("Za", "c", 6): inf, - ("Za", "c", 7): inf, - ("Za", "c", 8): inf, - ("Za", "c", 9): inf, - ("Za", "c", 10): inf, - ("Za", "c", 11): inf, - ("Za", "c", 12): inf, - ("Za", "c", 13): inf, - ("Za", "ctt", 1): inf, - ("Za", "ctt", 2): inf, - ("Za", "ctt", 3): inf, - ("Za", "ctt", 4): inf, - ("Za", "ctt", 5): inf, - ("Za", "ctt", 6): inf, - ("Za", "ctt", 7): inf, - ("Za", "ctt", 8): inf, - ("Za", "ctt", 9): inf, - ("Za", "ctt", 10): inf, - ("Za", "ctt", 11): inf, - ("Za", "ctt", 12): inf, - ("Za", "ctt", 13): inf, -} - -PVAL_TAU_STAR = { - ("Pz", "ct", 1): -3.08242, - ("Pz", "ct", 2): -13.63861, - ("Pz", "ct", 3): -32.51115, - ("Pz", "ct", 4): -59.24849, - ("Pz", "ct", 5): -93.92238, - ("Pz", "ct", 6): -136.40271, - ("Pz", "ct", 7): -186.49375, - ("Pz", "ct", 8): -244.68382, - ("Pz", "ct", 9): -310.15357, - ("Pz", "ct", 10): -383.26478, - ("Pz", "ct", 11): -464.03748, - ("Pz", "ct", 12): -552.20098, - ("Pz", "ct", 13): -648.45279, - ("Pz", "n", 1): -0.22199, - ("Pz", "n", 2): -2.85852, - ("Pz", "n", 3): -11.02763, - ("Pz", "n", 4): -26.34441, - ("Pz", "n", 5): -49.28784, - ("Pz", "n", 6): -80.04715, - ("Pz", "n", 7): -118.46026, - ("Pz", "n", 8): -164.84527, - ("Pz", "n", 9): -219.0016, - ("Pz", "n", 10): -280.90318, - ("Pz", "n", 11): -350.08994, - ("Pz", "n", 12): -427.20885, - ("Pz", "n", 13): -512.16408, - ("Pz", "c", 1): -0.85514, - ("Pz", "c", 2): -5.94104, - ("Pz", "c", 3): -17.62114, - ("Pz", "c", 4): -36.84006, - ("Pz", "c", 5): -63.61653, - ("Pz", "c", 6): -98.29471, - ("Pz", "c", 7): -140.59358, - ("Pz", "c", 8): -190.85425, - ("Pz", "c", 9): -248.94908, - ("Pz", "c", 10): -314.25398, - ("Pz", "c", 11): -387.49502, - ("Pz", "c", 12): -468.02964, - ("Pz", "c", 13): -556.77962, - ("Pz", "ctt", 1): -5.81055, - ("Pz", "ctt", 2): -22.13432, - ("Pz", "ctt", 3): -47.93851, - ("Pz", "ctt", 4): -82.12019, - ("Pz", "ctt", 5): -124.54157, - ("Pz", "ctt", 6): -174.67057, - ("Pz", "ctt", 7): -232.38028, - ("Pz", "ctt", 8): -298.06175, - ("Pz", "ctt", 9): -371.08836, - ("Pz", "ctt", 10): -452.31812, - ("Pz", "ctt", 11): -540.73765, - ("Pz", "ctt", 12): -636.67351, - ("Pz", "ctt", 13): -740.17437, + ("Zt", "n", 2): 2.22229, + ("Zt", "n", 3): 1.53746, + ("Zt", "n", 4): 1.51131, + ("Zt", "n", 5): 1.6928, + ("Zt", "n", 6): 1.93752, + ("Zt", "n", 7): 2.46299, + ("Zt", "n", 8): 3.14899, + ("Zt", "n", 9): 4.03229, + ("Zt", "n", 10): 4.56323, + ("Zt", "n", 11): 5.51495, + ("Zt", "n", 12): 6.58571, + ("Zt", "n", 13): 7.51566, + ("Zt", "ctt", 1): 1.69569, + ("Zt", "ctt", 2): 1.98399, + ("Zt", "ctt", 3): 2.25622, + ("Zt", "ctt", 4): 2.68008, + ("Zt", "ctt", 5): 2.86013, + ("Zt", "ctt", 6): 3.18698, + ("Zt", "ctt", 7): 3.84145, + ("Zt", "ctt", 8): 4.4727, + ("Zt", "ctt", 9): 5.75121, + ("Zt", "ctt", 10): 7.05734, + ("Zt", "ctt", 11): 8.2616, + ("Zt", "ctt", 12): 11.00912, + ("Zt", "ctt", 13): 12.24437, + ("Pz", "ct", 1): inf, + ("Pz", "ct", 2): inf, + ("Pz", "ct", 3): inf, + ("Pz", "ct", 4): inf, + ("Pz", "ct", 5): inf, + ("Pz", "ct", 6): inf, + ("Pz", "ct", 7): inf, + ("Pz", "ct", 8): inf, + ("Pz", "ct", 9): inf, + ("Pz", "ct", 10): inf, + ("Pz", "ct", 11): inf, + ("Pz", "ct", 12): inf, + ("Pz", "ct", 13): inf, + ("Pz", "c", 1): -22.99996, + ("Pz", "c", 2): inf, + ("Pz", "c", 3): inf, + ("Pz", "c", 4): inf, + ("Pz", "c", 5): inf, + ("Pz", "c", 6): inf, + ("Pz", "c", 7): inf, + ("Pz", "c", 8): inf, + ("Pz", "c", 9): inf, + ("Pz", "c", 10): inf, + ("Pz", "c", 11): inf, + ("Pz", "c", 12): inf, + ("Pz", "c", 13): inf, + ("Pz", "n", 1): -11.96617, + ("Pz", "n", 2): inf, + ("Pz", "n", 3): inf, + ("Pz", "n", 4): inf, + ("Pz", "n", 5): inf, + ("Pz", "n", 6): inf, + ("Pz", "n", 7): inf, + ("Pz", "n", 8): inf, + ("Pz", "n", 9): inf, + ("Pz", "n", 10): inf, + ("Pz", "n", 11): inf, + ("Pz", "n", 12): inf, + ("Pz", "n", 13): inf, + ("Pz", "ctt", 1): inf, + ("Pz", "ctt", 2): inf, + ("Pz", "ctt", 3): inf, + ("Pz", "ctt", 4): inf, + ("Pz", "ctt", 5): inf, + ("Pz", "ctt", 6): inf, + ("Pz", "ctt", 7): inf, + ("Pz", "ctt", 8): inf, + ("Pz", "ctt", 9): inf, + ("Pz", "ctt", 10): inf, + ("Pz", "ctt", 11): inf, + ("Pz", "ctt", 12): inf, + ("Pz", "ctt", 13): inf, +} + +PVAL_TAU_STAR = { + ("Za", "ct", 1): 0.95707, + ("Za", "ct", 2): 0.14113, + ("Za", "ct", 3): -0.94707, + ("Za", "ct", 4): -2.30996, + ("Za", "ct", 5): -3.89646, + ("Za", "ct", 6): -5.67704, + ("Za", "ct", 7): -7.51299, + ("Za", "ct", 8): -9.53536, + ("Za", "ct", 9): -11.64999, + ("Za", "ct", 10): -13.85722, + ("Za", "ct", 11): -16.15532, + ("Za", "ct", 12): -18.41612, + ("Za", "ct", 13): -20.83981, + ("Za", "c", 1): 2.41432, + ("Za", "c", 2): 1.89242, + ("Za", "c", 3): 1.00106, + ("Za", "c", 4): -0.23913, + ("Za", "c", 5): -1.80254, + ("Za", "c", 6): -3.60539, + ("Za", "c", 7): -5.5093, + ("Za", "c", 8): -7.45321, + ("Za", "c", 9): -9.4898, + ("Za", "c", 10): -11.63611, + ("Za", "c", 11): -13.8304, + ("Za", "c", 12): -16.10248, + ("Za", "c", 13): -18.41375, + ("Za", "n", 1): 3.10968, + ("Za", "n", 2): 2.86938, + ("Za", "n", 3): 2.09848, + ("Za", "n", 4): 0.9398, + ("Za", "n", 5): -0.53853, + ("Za", "n", 6): -2.18122, + ("Za", "n", 7): -3.97482, + ("Za", "n", 8): -5.87297, + ("Za", "n", 9): -7.84559, + ("Za", "n", 10): -9.87766, + ("Za", "n", 11): -12.01787, + ("Za", "n", 12): -14.27352, + ("Za", "n", 13): -16.55148, + ("Za", "ctt", 1): -0.77141, + ("Za", "ctt", 2): -1.79279, + ("Za", "ctt", 3): -3.02138, + ("Za", "ctt", 4): -4.44438, + ("Za", "ctt", 5): -6.09143, + ("Za", "ctt", 6): -7.88529, + ("Za", "ctt", 7): -9.8679, + ("Za", "ctt", 8): -11.88143, + ("Za", "ctt", 9): -14.05681, + ("Za", "ctt", 10): -16.26183, + ("Za", "ctt", 11): -18.53178, + ("Za", "ctt", 12): -20.89064, + ("Za", "ctt", 13): -23.3384, ("Pu", "ct", 1): -3.08242, ("Pu", "ct", 2): -3.59922, ("Pu", "ct", 3): -4.22731, @@ -1966,19 +1966,6 @@ ("Pu", "ct", 11): -13.96991, ("Pu", "ct", 12): -15.64286, ("Pu", "ct", 13): -17.36699, - ("Pu", "n", 1): -0.22199, - ("Pu", "n", 2): -0.32106, - ("Pu", "n", 3): -0.49989, - ("Pu", "n", 4): -0.81975, - ("Pu", "n", 5): -1.3517, - ("Pu", "n", 6): -2.13751, - ("Pu", "n", 7): -3.10198, - ("Pu", "n", 8): -4.21325, - ("Pu", "n", 9): -5.47656, - ("Pu", "n", 10): -6.78801, - ("Pu", "n", 11): -8.24013, - ("Pu", "n", 12): -9.79868, - ("Pu", "n", 13): -11.42269, ("Pu", "c", 1): -0.85514, ("Pu", "c", 2): -1.09353, ("Pu", "c", 3): -1.44418, @@ -1992,6 +1979,19 @@ ("Pu", "c", 11): -10.0253, ("Pu", "c", 12): -11.65946, ("Pu", "c", 13): -13.3168, + ("Pu", "n", 1): -0.22199, + ("Pu", "n", 2): -0.32106, + ("Pu", "n", 3): -0.49989, + ("Pu", "n", 4): -0.81975, + ("Pu", "n", 5): -1.3517, + ("Pu", "n", 6): -2.13751, + ("Pu", "n", 7): -3.10198, + ("Pu", "n", 8): -4.21325, + ("Pu", "n", 9): -5.47656, + ("Pu", "n", 10): -6.78801, + ("Pu", "n", 11): -8.24013, + ("Pu", "n", 12): -9.79868, + ("Pu", "n", 13): -11.42269, ("Pu", "ctt", 1): -5.81055, ("Pu", "ctt", 2): -6.52517, ("Pu", "ctt", 3): -7.36321, @@ -2005,165 +2005,165 @@ ("Pu", "ctt", 11): -18.12089, ("Pu", "ctt", 12): -19.85212, ("Pu", "ctt", 13): -21.66512, - ("Zt", "ct", 1): 0.38212, - ("Zt", "ct", 2): 0.04981, - ("Zt", "ct", 3): -0.31069, - ("Zt", "ct", 4): -0.66761, - ("Zt", "ct", 5): -1.01319, - ("Zt", "ct", 6): -1.34686, - ("Zt", "ct", 7): -1.63678, - ("Zt", "ct", 8): -1.92079, - ("Zt", "ct", 9): -2.19064, - ("Zt", "ct", 10): -2.43312, - ("Zt", "ct", 11): -3.05886, - ("Zt", "ct", 12): -3.14077, - ("Zt", "ct", 13): -3.34168, - ("Zt", "n", 1): 2.8191, - ("Zt", "n", 2): 1.9606, - ("Zt", "n", 3): 1.10352, - ("Zt", "n", 4): 0.37153, - ("Zt", "n", 5): -0.17661, - ("Zt", "n", 6): -0.6314, - ("Zt", "n", 7): -1.01429, - ("Zt", "n", 8): -1.35849, - ("Zt", "n", 9): -1.67029, - ("Zt", "n", 10): -1.94633, - ("Zt", "n", 11): -2.20802, - ("Zt", "n", 12): -2.45004, - ("Zt", "n", 13): -3.00464, - ("Zt", "c", 1): 1.366, - ("Zt", "c", 2): 0.92962, - ("Zt", "c", 3): 0.41408, - ("Zt", "c", 4): -0.0811, - ("Zt", "c", 5): -0.54887, - ("Zt", "c", 6): -0.95212, - ("Zt", "c", 7): -1.31399, - ("Zt", "c", 8): -1.62527, - ("Zt", "c", 9): -1.91437, - ("Zt", "c", 10): -2.18092, - ("Zt", "c", 11): -2.42324, - ("Zt", "c", 12): -2.65916, - ("Zt", "c", 13): -2.87467, - ("Zt", "ctt", 1): -0.24901, - ("Zt", "ctt", 2): -0.52876, - ("Zt", "ctt", 3): -0.82547, - ("Zt", "ctt", 4): -1.1183, - ("Zt", "ctt", 5): -1.41021, - ("Zt", "ctt", 6): -1.69575, - ("Zt", "ctt", 7): -1.96293, - ("Zt", "ctt", 8): -2.21438, - ("Zt", "ctt", 9): -2.4444, - ("Zt", "ctt", 10): -2.66945, - ("Zt", "ctt", 11): -2.88188, - ("Zt", "ctt", 12): -3.08981, - ("Zt", "ctt", 13): -3.28606, - ("Za", "ct", 1): 0.96459, - ("Za", "ct", 2): 0.14088, - ("Za", "ct", 3): -0.9636, - ("Za", "ct", 4): -2.31522, - ("Za", "ct", 5): -3.89243, - ("Za", "ct", 6): -5.68813, - ("Za", "ct", 7): -7.52282, - ("Za", "ct", 8): -9.51759, - ("Za", "ct", 9): -11.68047, - ("Za", "ct", 10): -13.87176, - ("Za", "ct", 11): -16.16896, - ("Za", "ct", 12): -18.43728, - ("Za", "ct", 13): -20.83384, - ("Za", "n", 1): 3.11414, - ("Za", "n", 2): 2.87313, - ("Za", "n", 3): 2.10222, - ("Za", "n", 4): 0.94078, - ("Za", "n", 5): -0.53648, - ("Za", "n", 6): -2.18343, - ("Za", "n", 7): -3.97545, - ("Za", "n", 8): -5.86499, - ("Za", "n", 9): -7.84662, - ("Za", "n", 10): -9.89121, - ("Za", "n", 11): -12.03487, - ("Za", "n", 12): -14.26912, - ("Za", "n", 13): -16.54866, - ("Za", "c", 1): 2.41192, - ("Za", "c", 2): 1.9, - ("Za", "c", 3): 1.00153, - ("Za", "c", 4): -0.23112, - ("Za", "c", 5): -1.80304, - ("Za", "c", 6): -3.59613, - ("Za", "c", 7): -5.51334, - ("Za", "c", 8): -7.44103, - ("Za", "c", 9): -9.49598, - ("Za", "c", 10): -11.64825, - ("Za", "c", 11): -13.82919, - ("Za", "c", 12): -16.09896, - ("Za", "c", 13): -18.40355, - ("Za", "ctt", 1): -0.77089, - ("Za", "ctt", 2): -1.78133, - ("Za", "ctt", 3): -3.04043, - ("Za", "ctt", 4): -4.43733, - ("Za", "ctt", 5): -6.09263, - ("Za", "ctt", 6): -7.89124, - ("Za", "ctt", 7): -9.85666, - ("Za", "ctt", 8): -11.86937, - ("Za", "ctt", 9): -14.03075, - ("Za", "ctt", 10): -16.24756, - ("Za", "ctt", 11): -18.54319, - ("Za", "ctt", 12): -20.86603, - ("Za", "ctt", 13): -23.30151, + ("Zt", "ct", 1): 0.3805, + ("Zt", "ct", 2): 0.05024, + ("Zt", "ct", 3): -0.30551, + ("Zt", "ct", 4): -0.66559, + ("Zt", "ct", 5): -1.01425, + ("Zt", "ct", 6): -1.34402, + ("Zt", "ct", 7): -1.63601, + ("Zt", "ct", 8): -1.92171, + ("Zt", "ct", 9): -2.18579, + ("Zt", "ct", 10): -2.43157, + ("Zt", "ct", 11): -2.66131, + ("Zt", "ct", 12): -3.21293, + ("Zt", "ct", 13): -3.46851, + ("Zt", "c", 1): 1.36869, + ("Zt", "c", 2): 0.92707, + ("Zt", "c", 3): 0.4158, + ("Zt", "c", 4): -0.08379, + ("Zt", "c", 5): -0.54741, + ("Zt", "c", 6): -0.95534, + ("Zt", "c", 7): -1.31534, + ("Zt", "c", 8): -1.62843, + ("Zt", "c", 9): -1.91339, + ("Zt", "c", 10): -2.18113, + ("Zt", "c", 11): -2.42587, + ("Zt", "c", 12): -2.66081, + ("Zt", "c", 13): -2.87455, + ("Zt", "n", 1): 2.82005, + ("Zt", "n", 2): 1.96013, + ("Zt", "n", 3): 1.1036, + ("Zt", "n", 4): 0.37276, + ("Zt", "n", 5): -0.1774, + ("Zt", "n", 6): -0.63025, + ("Zt", "n", 7): -1.01581, + ("Zt", "n", 8): -1.35957, + ("Zt", "n", 9): -1.66974, + ("Zt", "n", 10): -1.94422, + ("Zt", "n", 11): -2.20531, + ("Zt", "n", 12): -2.44906, + ("Zt", "n", 13): -3.06244, + ("Zt", "ctt", 1): -0.24829, + ("Zt", "ctt", 2): -0.53141, + ("Zt", "ctt", 3): -0.82365, + ("Zt", "ctt", 4): -1.12021, + ("Zt", "ctt", 5): -1.40713, + ("Zt", "ctt", 6): -1.69359, + ("Zt", "ctt", 7): -1.96149, + ("Zt", "ctt", 8): -2.21232, + ("Zt", "ctt", 9): -2.44739, + ("Zt", "ctt", 10): -2.67278, + ("Zt", "ctt", 11): -2.88516, + ("Zt", "ctt", 12): -3.09104, + ("Zt", "ctt", 13): -3.28843, + ("Pz", "ct", 1): -3.08242, + ("Pz", "ct", 2): -13.63861, + ("Pz", "ct", 3): -32.51115, + ("Pz", "ct", 4): -59.24849, + ("Pz", "ct", 5): -93.92238, + ("Pz", "ct", 6): -136.40271, + ("Pz", "ct", 7): -186.49375, + ("Pz", "ct", 8): -244.68382, + ("Pz", "ct", 9): -310.15357, + ("Pz", "ct", 10): -383.26478, + ("Pz", "ct", 11): -464.03748, + ("Pz", "ct", 12): -552.20098, + ("Pz", "ct", 13): -648.45279, + ("Pz", "c", 1): -0.85514, + ("Pz", "c", 2): -5.94104, + ("Pz", "c", 3): -17.62114, + ("Pz", "c", 4): -36.84006, + ("Pz", "c", 5): -63.61653, + ("Pz", "c", 6): -98.29471, + ("Pz", "c", 7): -140.59358, + ("Pz", "c", 8): -190.85425, + ("Pz", "c", 9): -248.94908, + ("Pz", "c", 10): -314.25398, + ("Pz", "c", 11): -387.49502, + ("Pz", "c", 12): -468.02964, + ("Pz", "c", 13): -556.77962, + ("Pz", "n", 1): -0.22199, + ("Pz", "n", 2): -2.85852, + ("Pz", "n", 3): -11.02763, + ("Pz", "n", 4): -26.34441, + ("Pz", "n", 5): -49.28784, + ("Pz", "n", 6): -80.04715, + ("Pz", "n", 7): -118.46026, + ("Pz", "n", 8): -164.84527, + ("Pz", "n", 9): -219.0016, + ("Pz", "n", 10): -280.90318, + ("Pz", "n", 11): -350.08994, + ("Pz", "n", 12): -427.20885, + ("Pz", "n", 13): -512.16408, + ("Pz", "ctt", 1): -5.81055, + ("Pz", "ctt", 2): -22.13432, + ("Pz", "ctt", 3): -47.93851, + ("Pz", "ctt", 4): -82.12019, + ("Pz", "ctt", 5): -124.54157, + ("Pz", "ctt", 6): -174.67057, + ("Pz", "ctt", 7): -232.38028, + ("Pz", "ctt", 8): -298.06175, + ("Pz", "ctt", 9): -371.08836, + ("Pz", "ctt", 10): -452.31812, + ("Pz", "ctt", 11): -540.73765, + ("Pz", "ctt", 12): -636.67351, + ("Pz", "ctt", 13): -740.17437, } PVAL_TAU_MIN = { - ("Pz", "ct", 1): -123.44756, - ("Pz", "ct", 2): -212.32738, - ("Pz", "ct", 3): -317.23947, - ("Pz", "ct", 4): -433.89154, - ("Pz", "ct", 5): -561.88559, - ("Pz", "ct", 6): -736.94601, - ("Pz", "ct", 7): -902.13571, - ("Pz", "ct", 8): -1088.66009, - ("Pz", "ct", 9): -1317.90752, - ("Pz", "ct", 10): -1478.86835, - ("Pz", "ct", 11): -1793.72312, - ("Pz", "ct", 12): -2100.83741, - ("Pz", "ct", 13): -2368.86433, - ("Pz", "n", 1): -60.12803, - ("Pz", "n", 2): -112.39177, - ("Pz", "n", 3): -189.44888, - ("Pz", "n", 4): -286.62199, - ("Pz", "n", 5): -388.21033, - ("Pz", "n", 6): -516.73274, - ("Pz", "n", 7): -640.91225, - ("Pz", "n", 8): -795.56478, - ("Pz", "n", 9): -972.72477, - ("Pz", "n", 10): -1220.83567, - ("Pz", "n", 11): -1384.99515, - ("Pz", "n", 12): -1513.00844, - ("Pz", "n", 13): -1888.49487, - ("Pz", "c", 1): -83.54853, - ("Pz", "c", 2): -148.87824, - ("Pz", "c", 3): -233.18419, - ("Pz", "c", 4): -341.96835, - ("Pz", "c", 5): -455.91451, - ("Pz", "c", 6): -578.86991, - ("Pz", "c", 7): -736.94168, - ("Pz", "c", 8): -895.32531, - ("Pz", "c", 9): -1077.84361, - ("Pz", "c", 10): -1341.04305, - ("Pz", "c", 11): -1501.85741, - ("Pz", "c", 12): -1792.56461, - ("Pz", "c", 13): -2083.44133, - ("Pz", "ctt", 1): -155.00783, - ("Pz", "ctt", 2): -268.74314, - ("Pz", "ctt", 3): -404.66937, - ("Pz", "ctt", 4): -536.6706, - ("Pz", "ctt", 5): -691.88602, - ("Pz", "ctt", 6): -860.98243, - ("Pz", "ctt", 7): -1047.42188, - ("Pz", "ctt", 8): -1248.6763, - ("Pz", "ctt", 9): -1545.40597, - ("Pz", "ctt", 10): -1739.12781, - ("Pz", "ctt", 11): -1996.09064, - ("Pz", "ctt", 12): -2271.82207, - ("Pz", "ctt", 13): -2495.7556, + ("Za", "ct", 1): -62.6535, + ("Za", "ct", 2): -79.88866, + ("Za", "ct", 3): -93.97958, + ("Za", "ct", 4): -108.42965, + ("Za", "ct", 5): -122.82841, + ("Za", "ct", 6): -136.66774, + ("Za", "ct", 7): -150.80227, + ("Za", "ct", 8): -163.21685, + ("Za", "ct", 9): -178.97577, + ("Za", "ct", 10): -189.89894, + ("Za", "ct", 11): -202.58756, + ("Za", "ct", 12): -217.72444, + ("Za", "ct", 13): -230.00825, + ("Za", "c", 1): -42.59827, + ("Za", "c", 2): -59.96767, + ("Za", "c", 3): -77.16465, + ("Za", "c", 4): -93.42199, + ("Za", "c", 5): -107.56049, + ("Za", "c", 6): -122.34527, + ("Za", "c", 7): -135.07975, + ("Za", "c", 8): -148.28863, + ("Za", "c", 9): -160.93076, + ("Za", "c", 10): -176.86866, + ("Za", "c", 11): -189.17117, + ("Za", "c", 12): -203.66329, + ("Za", "c", 13): -212.8362, + ("Za", "n", 1): -28.29012, + ("Za", "n", 2): -50.47808, + ("Za", "n", 3): -67.79886, + ("Za", "n", 4): -84.10963, + ("Za", "n", 5): -97.54531, + ("Za", "n", 6): -113.71286, + ("Za", "n", 7): -128.33885, + ("Za", "n", 8): -140.86076, + ("Za", "n", 9): -154.18389, + ("Za", "n", 10): -166.66314, + ("Za", "n", 11): -181.49456, + ("Za", "n", 12): -196.43684, + ("Za", "n", 13): -210.06558, + ("Za", "ctt", 1): -78.23034, + ("Za", "ctt", 2): -92.94324, + ("Za", "ctt", 3): -107.96644, + ("Za", "ctt", 4): -123.6402, + ("Za", "ctt", 5): -136.31409, + ("Za", "ctt", 6): -150.37908, + ("Za", "ctt", 7): -163.72419, + ("Za", "ctt", 8): -177.06591, + ("Za", "ctt", 9): -189.9828, + ("Za", "ctt", 10): -204.13523, + ("Za", "ctt", 11): -216.74749, + ("Za", "ctt", 12): -227.96101, + ("Za", "ctt", 13): -240.72029, ("Pu", "ct", 1): -123.44756, ("Pu", "ct", 2): -136.25608, ("Pu", "ct", 3): -150.94655, @@ -2177,19 +2177,6 @@ ("Pu", "ct", 11): -269.62056, ("Pu", "ct", 12): -284.41257, ("Pu", "ct", 13): -299.31789, - ("Pu", "n", 1): -60.12803, - ("Pu", "n", 2): -83.71551, - ("Pu", "n", 3): -102.72644, - ("Pu", "n", 4): -121.33763, - ("Pu", "n", 5): -139.17001, - ("Pu", "n", 6): -158.76926, - ("Pu", "n", 7): -170.28014, - ("Pu", "n", 8): -189.0383, - ("Pu", "n", 9): -208.60477, - ("Pu", "n", 10): -219.16726, - ("Pu", "n", 11): -236.91681, - ("Pu", "n", 12): -247.3861, - ("Pu", "n", 13): -255.28054, ("Pu", "c", 1): -83.54853, ("Pu", "c", 2): -103.73063, ("Pu", "c", 3): -120.78525, @@ -2203,6 +2190,19 @@ ("Pu", "c", 11): -244.43899, ("Pu", "c", 12): -254.54936, ("Pu", "c", 13): -269.30478, + ("Pu", "n", 1): -60.12803, + ("Pu", "n", 2): -83.71551, + ("Pu", "n", 3): -102.72644, + ("Pu", "n", 4): -121.33763, + ("Pu", "n", 5): -139.17001, + ("Pu", "n", 6): -158.76926, + ("Pu", "n", 7): -170.28014, + ("Pu", "n", 8): -189.0383, + ("Pu", "n", 9): -208.60477, + ("Pu", "n", 10): -219.16726, + ("Pu", "n", 11): -236.91681, + ("Pu", "n", 12): -247.3861, + ("Pu", "n", 13): -255.28054, ("Pu", "ctt", 1): -155.00783, ("Pu", "ctt", 2): -170.20901, ("Pu", "ctt", 3): -185.43547, @@ -2216,108 +2216,108 @@ ("Pu", "ctt", 11): -301.91631, ("Pu", "ctt", 12): -311.03002, ("Pu", "ctt", 13): -325.86556, - ("Zt", "ct", 1): -17.42066, - ("Zt", "ct", 2): -23.38694, - ("Zt", "ct", 3): -24.04689, - ("Zt", "ct", 4): -22.46555, - ("Zt", "ct", 5): -24.49527, - ("Zt", "ct", 6): -24.11444, - ("Zt", "ct", 7): -25.94933, - ("Zt", "ct", 8): -26.15965, - ("Zt", "ct", 9): -30.9686, - ("Zt", "ct", 10): -30.43935, - ("Zt", "ct", 11): -32.37123, - ("Zt", "ct", 12): -34.56792, - ("Zt", "ct", 13): -33.42983, - ("Zt", "n", 1): -19.59978, - ("Zt", "n", 2): -23.52144, - ("Zt", "n", 3): -23.02487, - ("Zt", "n", 4): -24.70602, - ("Zt", "n", 5): -22.50774, - ("Zt", "n", 6): -24.94925, - ("Zt", "n", 7): -25.75939, - ("Zt", "n", 8): -24.63677, - ("Zt", "n", 9): -24.80934, - ("Zt", "n", 10): -26.05312, - ("Zt", "n", 11): -27.83462, - ("Zt", "n", 12): -30.04588, - ("Zt", "n", 13): -31.88166, - ("Zt", "c", 1): -18.06595, - ("Zt", "c", 2): -19.53682, - ("Zt", "c", 3): -21.76302, - ("Zt", "c", 4): -24.5337, - ("Zt", "c", 5): -23.37231, - ("Zt", "c", 6): -25.47813, - ("Zt", "c", 7): -23.92402, - ("Zt", "c", 8): -24.97095, - ("Zt", "c", 9): -28.1683, - ("Zt", "c", 10): -29.4733, - ("Zt", "c", 11): -28.28346, - ("Zt", "c", 12): -28.9203, - ("Zt", "c", 13): -27.89672, - ("Zt", "ctt", 1): -17.328, - ("Zt", "ctt", 2): -20.71227, - ("Zt", "ctt", 3): -22.74588, - ("Zt", "ctt", 4): -24.63761, - ("Zt", "ctt", 5): -22.39321, - ("Zt", "ctt", 6): -24.48061, - ("Zt", "ctt", 7): -25.73785, - ("Zt", "ctt", 8): -27.16109, - ("Zt", "ctt", 9): -27.30957, - ("Zt", "ctt", 10): -28.31294, - ("Zt", "ctt", 11): -28.21463, - ("Zt", "ctt", 12): -28.63475, - ("Zt", "ctt", 13): -29.94268, - ("Za", "ct", 1): -62.40117, - ("Za", "ct", 2): -79.77926, - ("Za", "ct", 3): -94.01222, - ("Za", "ct", 4): -107.60035, - ("Za", "ct", 5): -122.15157, - ("Za", "ct", 6): -135.53835, - ("Za", "ct", 7): -149.49831, - ("Za", "ct", 8): -161.50527, - ("Za", "ct", 9): -178.79025, - ("Za", "ct", 10): -190.27862, - ("Za", "ct", 11): -203.56151, - ("Za", "ct", 12): -217.45273, - ("Za", "ct", 13): -230.24351, - ("Za", "n", 1): -28.34591, - ("Za", "n", 2): -50.57511, - ("Za", "n", 3): -67.56132, - ("Za", "n", 4): -84.21607, - ("Za", "n", 5): -97.76205, - ("Za", "n", 6): -113.5372, - ("Za", "n", 7): -128.3743, - ("Za", "n", 8): -140.46717, - ("Za", "n", 9): 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-1545.40597, + ("Pz", "ctt", 10): -1739.12781, + ("Pz", "ctt", 11): -1996.09064, + ("Pz", "ctt", 12): -2271.82207, + ("Pz", "ctt", 13): -2495.7556, } diff --git a/doc/source/covariance/covariance.rst b/doc/source/covariance/covariance.rst index 95c393a316..f940577158 100644 --- a/doc/source/covariance/covariance.rst +++ b/doc/source/covariance/covariance.rst @@ -1,10 +1,12 @@ Long-run Covariance Estimation ============================== - Long-run Covariance Estimators ------------------------------ +Kernel-based Estimators +~~~~~~~~~~~~~~~~~~~~~~~ + .. module:: arch.covariance.kernel :synopsis: Kernel-based long-run covariance estimation @@ -25,11 +27,31 @@ Long-run Covariance Estimators TukeyHamming TukeyHanning TukeyParzen + ZeroLag + + +Vector AR-based Estimators +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. module:: arch.covariance.var + :synopsis: Vector-AR-based long-run covariance estimation + +.. currentmodule:: arch.covariance.var + +.. autosummary:: + :toctree: generated/ + + PreWhitenedRecolored Results ------- +All long-run covariance estimators return their results using the same type +of object. + +.. currentmodule:: arch.covariance + .. autosummary:: :toctree: generated/ - CovarianceEstimate + ~kernel.CovarianceEstimate