Source code for statsmodels.tsa.vector_ar.vecm

from collections import defaultdict

import numpy as np
from numpy import hstack, vstack
from numpy.linalg import inv, svd
import scipy
import scipy.stats

from statsmodels.iolib.summary import Summary
from statsmodels.iolib.table import SimpleTable
from statsmodels.tools.decorators import cache_readonly
from statsmodels.tools.sm_exceptions import HypothesisTestWarning
from statsmodels.tools.validation import string_like
import statsmodels.tsa.base.tsa_model as tsbase
from statsmodels.tsa.coint_tables import c_sja, c_sjt
from statsmodels.tsa.tsatools import duplication_matrix, lagmat, vec
from statsmodels.tsa.vector_ar.hypothesis_test_results import (
    CausalityTestResults,
    WhitenessTestResults,
)
import statsmodels.tsa.vector_ar.irf as irf
import statsmodels.tsa.vector_ar.plotting as plot
from statsmodels.tsa.vector_ar.util import get_index, seasonal_dummies
from statsmodels.tsa.vector_ar.var_model import (
    VAR,
    LagOrderResults,
    _compute_acov,
    forecast,
    forecast_interval,
    ma_rep,
    orth_ma_rep,
    test_normality,
)


[docs] def select_order( data, maxlags: int, deterministic: str = "n", seasons: int = 0, exog=None, exog_coint=None, ): """ Compute lag order selections based on each of the available information criteria. Parameters ---------- data : array_like (nobs_tot x neqs) The observed data. maxlags : int All orders until maxlag will be compared according to the information criteria listed in the Results-section of this docstring. deterministic : str {"n", "co", "ci", "lo", "li"} * ``"n"`` - no deterministic terms * ``"co"`` - constant outside the cointegration relation * ``"ci"`` - constant within the cointegration relation * ``"lo"`` - linear trend outside the cointegration relation * ``"li"`` - linear trend within the cointegration relation Combinations of these are possible (e.g. ``"cili"`` or ``"colo"`` for linear trend with intercept). See the docstring of the :class:`VECM`-class for more information. seasons : int, default: 0 Number of periods in a seasonal cycle. exog : ndarray (nobs_tot x neqs) or `None`, default: `None` Deterministic terms outside the cointegration relation. exog_coint : ndarray (nobs_tot x neqs) or `None`, default: `None` Deterministic terms inside the cointegration relation. Returns ------- selected_orders : :class:`statsmodels.tsa.vector_ar.var_model.LagOrderResults` """ ic = defaultdict(list) deterministic = string_like(deterministic, "deterministic") for p in range(1, maxlags + 2): # +2 because k_ar_VECM == k_ar_VAR - 1 exogs = [] if "co" in deterministic or "ci" in deterministic: exogs.append(np.ones(len(data)).reshape(-1, 1)) if "lo" in deterministic or "li" in deterministic: exogs.append(1 + np.arange(len(data)).reshape(-1, 1)) if exog_coint is not None: exogs.append(exog_coint) if seasons > 0: exogs.append( seasonal_dummies(seasons, len(data)).reshape(-1, seasons - 1) ) if exog is not None: exogs.append(exog) exogs = hstack(exogs) if exogs else None var_model = VAR(data, exogs) # exclude some periods ==> same amount of data used for each lag order var_result = var_model._estimate_var(lags=p, offset=maxlags + 1 - p) for k, v in var_result.info_criteria.items(): ic[k].append(v) # -1+1 in the following line is only here for clarification. # -1 because k_ar_VECM == k_ar_VAR - 1 # +1 because p == index +1 (we start with p=1, not p=0) selected_orders = { ic_name: np.array(ic_value).argmin() - 1 + 1 for ic_name, ic_value in ic.items() } return LagOrderResults(ic, selected_orders, True)
def _linear_trend(nobs, k_ar, coint=False): """ Construct an ndarray representing a linear trend in a VECM. Parameters ---------- nobs : int Number of observations excluding the presample. k_ar : int Number of lags in levels. coint : bool, default: False If True (False), the returned array represents a linear trend inside (outside) the cointegration relation. Returns ------- ret : ndarray (nobs) An ndarray representing a linear trend in a VECM Notes ----- The returned array's size is nobs and not nobs_tot so it cannot be used to construct the exog-argument of VECM's __init__ method. """ ret = np.arange(nobs) + k_ar if not coint: ret += 1 return ret def _num_det_vars(det_string, seasons=0): """Gives the number of deterministic variables specified by det_string and seasons. Parameters ---------- det_string : str {"n", "co", "ci", "lo", "li"} * "n" - no deterministic terms * "co" - constant outside the cointegration relation * "ci" - constant within the cointegration relation * "lo" - linear trend outside the cointegration relation * "li" - linear trend within the cointegration relation Combinations of these are possible (e.g. "cili" or "colo" for linear trend with intercept). See the docstring of the :class:`VECM`-class for more information. seasons : int Number of periods in a seasonal cycle. Returns ------- num : int Number of deterministic terms and number dummy variables for seasonal terms. """ num = 0 det_string = string_like(det_string, "det_string") if "ci" in det_string or "co" in det_string: num += 1 if "li" in det_string or "lo" in det_string: num += 1 if seasons > 0: num += seasons - 1 return num def _deterministic_to_exog( deterministic, seasons, nobs_tot, first_season=0, seasons_centered=False, exog=None, exog_coint=None, ): """ Translate all information about deterministic terms into a single array. These information is taken from `deterministic` and `seasons` as well as from the `exog` and `exog_coint` arrays. The resulting array form can then be used e.g. in VAR's __init__ method. Parameters ---------- deterministic : str A string specifying the deterministic terms in the model. See VECM's docstring for more information. seasons : int Number of periods in a seasonal cycle. nobs_tot : int Number of observations including the presample. first_season : int, default: 0 Season of the first observation. seasons_centered : bool, default: False If True, the seasonal dummy variables are demeaned such that they are orthogonal to an intercept term. exog : ndarray (nobs_tot x #det_terms) or None, default: None An ndarray representing deterministic terms outside the cointegration relation. exog_coint : ndarray (nobs_tot x #det_terms_coint) or None, default: None An ndarray representing deterministic terms inside the cointegration relation. Returns ------- exog : ndarray or None None, if the function's arguments do not contain deterministic terms. Otherwise, an ndarray representing these deterministic terms. """ exogs = [] deterministic = string_like(deterministic, "deterministic") if "co" in deterministic or "ci" in deterministic: exogs.append(np.ones(nobs_tot)) if exog_coint is not None: exogs.append(exog_coint) if "lo" in deterministic or "li" in deterministic: exogs.append(np.arange(nobs_tot)) if seasons > 0: exogs.append( seasonal_dummies( seasons, nobs_tot, first_period=first_season, centered=seasons_centered, ) ) if exog is not None: exogs.append(exog) return np.column_stack(exogs) if exogs else None def _mat_sqrt(_2darray): """Calculates the square root of a matrix. Parameters ---------- _2darray : ndarray A 2-dimensional ndarray representing a square matrix. Returns ------- result : ndarray Square root of the matrix given as function argument. """ u_, s_, v_ = svd(_2darray, full_matrices=False) s_ = np.sqrt(s_) return u_.dot(s_[:, None] * v_) def _endog_matrices( endog, exog, exog_coint, diff_lags, deterministic, seasons=0, first_season=0, ): """ Returns different matrices needed for parameter estimation. Compare p. 186 in [1]_. The returned matrices consist of elements of the data as well as elements representing deterministic terms. A tuple of consisting of these matrices is returned. Parameters ---------- endog : ndarray (neqs x nobs_tot) The whole sample including the presample. exog : ndarray (nobs_tot x neqs) or None Deterministic terms outside the cointegration relation. exog_coint : ndarray (nobs_tot x neqs) or None Deterministic terms inside the cointegration relation. diff_lags : int Number of lags in the VEC representation. deterministic : str {``"n"``, ``"co"``, ``"ci"``, ``"lo"``, ``"li"``} * ``"n"`` - no deterministic terms * ``"co"`` - constant outside the cointegration relation * ``"ci"`` - constant within the cointegration relation * ``"lo"`` - linear trend outside the cointegration relation * ``"li"`` - linear trend within the cointegration relation Combinations of these are possible (e.g. ``"cili"`` or ``"colo"`` for linear trend with intercept). See the docstring of the :class:`VECM`-class for more information. seasons : int, default: 0 Number of periods in a seasonal cycle. 0 (default) means no seasons. first_season : int, default: 0 The season of the first observation. `0` means first season, `1` means second season, ..., `seasons-1` means the last season. Returns ------- y_1_T : ndarray (neqs x nobs) The (transposed) data without the presample. `.. math:: (y_1, \\ldots, y_T) delta_y_1_T : ndarray (neqs x nobs) The first differences of endog. `.. math:: (y_1, \\ldots, y_T) - (y_0, \\ldots, y_{T-1}) y_lag1 : ndarray (neqs x nobs) (dimensions assuming no deterministic terms are given) Endog of the previous period (lag 1). `.. math:: (y_0, \\ldots, y_{T-1}) delta_x : ndarray (k_ar_diff*neqs x nobs) (dimensions assuming no deterministic terms are given) Lagged differenced endog, used as regressor for the short term equation. References ---------- .. [1] Lütkepohl, H. 2005. *New Introduction to Multiple Time Series Analysis*. Springer. """ deterministic = string_like(deterministic, "deterministic") # p. 286: p = diff_lags + 1 y = endog K = y.shape[0] y_1_T = y[:, p:] T = y_1_T.shape[1] delta_y = np.diff(y) delta_y_1_T = delta_y[:, p - 1 :] y_lag1 = y[:, p - 1 : -1] if "co" in deterministic and "ci" in deterministic: raise ValueError( "Both 'co' and 'ci' as deterministic terms given. " + "Please choose one of the two." ) y_lag1_stack = [y_lag1] if "ci" in deterministic: # pp. 257, 299, 306, 307 y_lag1_stack.append(np.ones(T)) if "li" in deterministic: # p. 299 y_lag1_stack.append(_linear_trend(T, p, coint=True)) if exog_coint is not None: y_lag1_stack.append(exog_coint[-T - 1 : -1].T) y_lag1 = np.vstack(y_lag1_stack) # p. 286: delta_x = np.zeros((diff_lags * K, T)) if diff_lags > 0: for j in range(delta_x.shape[1]): delta_x[:, j] = delta_y[ :, j + p - 2 : None if j - 1 < 0 else j - 1 : -1 ].T.reshape(K * (p - 1)) delta_x_stack = [delta_x] # p. 299, p. 303: if "co" in deterministic: delta_x_stack.append(np.ones(T)) if seasons > 0: delta_x_stack.append( seasonal_dummies( seasons, delta_x.shape[1], first_period=first_season + diff_lags + 1, centered=True, ).T ) if "lo" in deterministic: delta_x_stack.append(_linear_trend(T, p)) if exog is not None: delta_x_stack.append(exog[-T:].T) delta_x = np.vstack(delta_x_stack) return y_1_T, delta_y_1_T, y_lag1, delta_x def _r_matrices(delta_y_1_T, y_lag1, delta_x): """Returns two ndarrays needed for parameter estimation as well as the calculation of standard errors. Parameters ---------- delta_y_1_T : ndarray (neqs x nobs) The first differences of endog. `.. math:: (y_1, \\ldots, y_T) - (y_0, \\ldots, y_{T-1}) y_lag1 : ndarray (neqs x nobs) (dimensions assuming no deterministic terms are given) Endog of the previous period (lag 1). `.. math:: (y_0, \\ldots, y_{T-1}) delta_x : ndarray (k_ar_diff*neqs x nobs) (dimensions assuming no deterministic terms are given) Lagged differenced endog, used as regressor for the short term equation. Returns ------- result : tuple A tuple of two ndarrays. (See p. 292 in [1]_ for the definition of R_0 and R_1.) References ---------- .. [1] Lütkepohl, H. 2005. *New Introduction to Multiple Time Series Analysis*. Springer. """ # todo: rewrite m such that a big (TxT) matrix is avoided nobs = y_lag1.shape[1] m = np.identity(nobs) - ( delta_x.T.dot(inv(delta_x.dot(delta_x.T))).dot(delta_x) ) # p. 291 r0 = delta_y_1_T.dot(m) # p. 292 r1 = y_lag1.dot(m) return r0, r1 def _sij(delta_x, delta_y_1_T, y_lag1): """Returns matrices and eigenvalues and -vectors used for parameter estimation and the calculation of a models loglikelihood. Parameters ---------- delta_x : ndarray (k_ar_diff*neqs x nobs) (dimensions assuming no deterministic terms are given) delta_y_1_T : ndarray (neqs x nobs) :math:`(y_1, \\ldots, y_T) - (y_0, \\ldots, y_{T-1})` y_lag1 : ndarray (neqs x nobs) (dimensions assuming no deterministic terms are given) :math:`(y_0, \\ldots, y_{T-1})` Returns ------- result : tuple A tuple of five ndarrays as well as eigenvalues and -vectors of a certain (matrix) product of some of the returned ndarrays. (See pp. 294-295 in [1]_ for more information on :math:`S_0, S_1, \\lambda_i, \\v_i` for :math:`i \\in \\{1, \\dots, K\\}`.) References ---------- .. [1] Lütkepohl, H. 2005. *New Introduction to Multiple Time Series Analysis*. Springer. """ nobs = y_lag1.shape[1] r0, r1 = _r_matrices(delta_y_1_T, y_lag1, delta_x) s00 = np.dot(r0, r0.T) / nobs s01 = np.dot(r0, r1.T) / nobs s10 = s01.T s11 = np.dot(r1, r1.T) / nobs s11_ = inv(_mat_sqrt(s11)) # p. 295: s01_s11_ = np.dot(s01, s11_) eig = np.linalg.eig(s01_s11_.T @ inv(s00) @ s01_s11_) lambd = eig[0] v = eig[1] # reorder eig_vals to make them decreasing (and order eig_vecs accordingly) lambd_order = np.argsort(lambd)[::-1] lambd = lambd[lambd_order] v = v[:, lambd_order] return s00, s01, s10, s11, s11_, lambd, v
[docs] class CointRankResults: """A class for holding the results from testing the cointegration rank. Parameters ---------- rank : int (0 <= `rank` <= `neqs`) The rank to choose according to the Johansen cointegration rank test. neqs : int Number of variables in the time series. test_stats : array_like (`rank` + 1 if `rank` < `neqs` else `rank`) A one-dimensional array-like object containing the test statistics of the conducted tests. crit_vals : array_like (`rank` +1 if `rank` < `neqs` else `rank`) A one-dimensional array-like object containing the critical values corresponding to the entries in the `test_stats` argument. method : str, {``"trace"``, ``"maxeig"``}, default: ``"trace"`` If ``"trace"``, the trace test statistic is used. If ``"maxeig"``, the maximum eigenvalue test statistic is used. signif : float, {0.1, 0.05, 0.01}, default: 0.05 The test's significance level. """ def __init__( self, rank, neqs, test_stats, crit_vals, method="trace", signif=0.05 ): self.rank = rank self.neqs = neqs self.r_1 = [ neqs if method == "trace" else i + 1 for i in range(min(rank + 1, neqs)) ] self.test_stats = test_stats self.crit_vals = crit_vals self.method = method self.signif = signif
[docs] def summary(self): headers = ["r_0", "r_1", "test statistic", "critical value"] title = ( "Johansen cointegration test using " + ("trace" if self.method == "trace" else "maximum eigenvalue") + f" test statistic with {self.signif:.0%}" + " significance level" ) num_tests = min(self.rank, self.neqs - 1) data = [ [i, self.r_1[i], self.test_stats[i], self.crit_vals[i]] for i in range(num_tests + 1) ] data_fmt = { "data_fmts": ["%s", "%s", "%#0.4g", "%#0.4g"], "data_aligns": "r", } html_data_fmt = dict(data_fmt) html_data_fmt["data_fmts"] = [ "<td>" + i + "</td>" for i in html_data_fmt["data_fmts"] ] return SimpleTable( data=data, headers=headers, title=title, txt_fmt=data_fmt, html_fmt=html_data_fmt, ltx_fmt=data_fmt, )
def __str__(self): return self.summary().as_text()
[docs] def select_coint_rank( endog, det_order, k_ar_diff, method="trace", signif=0.05 ): """Calculate the cointegration rank of a VECM. Parameters ---------- endog : array_like (nobs_tot x neqs) The data with presample. det_order : int * -1 - no deterministic terms * 0 - constant term * 1 - linear trend k_ar_diff : int, nonnegative Number of lagged differences in the model. method : str, {``"trace"``, ``"maxeig"``}, default: ``"trace"`` If ``"trace"``, the trace test statistic is used. If ``"maxeig"``, the maximum eigenvalue test statistic is used. signif : float, {0.1, 0.05, 0.01}, default: 0.05 The test's significance level. Returns ------- rank : :class:`CointRankResults` A :class:`CointRankResults` object containing the cointegration rank suggested by the test and allowing a summary to be printed. """ if method not in ["trace", "maxeig"]: raise ValueError( "The method argument has to be either 'trace' or" "'maximum eigenvalue'." ) if det_order not in [-1, 0, 1]: if type(det_order) is int and det_order > 1: raise ValueError( "A det_order greather than 1 is not supported." "Use a value of -1, 0, or 1." ) else: raise ValueError("det_order must be -1, 0, or 1.") possible_signif_values = [0.1, 0.05, 0.01] if signif not in possible_signif_values: raise ValueError( "Please choose a significance level from {0.1, 0.05," "0.01}" ) coint_result = coint_johansen(endog, det_order, k_ar_diff) test_stat = coint_result.lr1 if method == "trace" else coint_result.lr2 crit_vals = coint_result.cvt if method == "trace" else coint_result.cvm signif_index = possible_signif_values.index(signif) neqs = endog.shape[1] r_0 = 0 # rank in null hypothesis while r_0 < neqs: if test_stat[r_0] < crit_vals[r_0, signif_index]: break # we accept current rank else: r_0 += 1 # we reject current rank and test next possible rank return CointRankResults( r_0, neqs, test_stat[: r_0 + 1], crit_vals[: r_0 + 1, signif_index], method, signif, )
[docs] def coint_johansen(endog, det_order, k_ar_diff): """ Johansen cointegration test of the cointegration rank of a VECM Parameters ---------- endog : array_like (nobs_tot x neqs) Data to test det_order : int * -1 - no deterministic terms * 0 - constant term * 1 - linear trend k_ar_diff : int, nonnegative Number of lagged differences in the model. Returns ------- result : JohansenTestResult An object containing the test's results. The most important attributes of the result class are: * trace_stat and trace_stat_crit_vals * max_eig_stat and max_eig_stat_crit_vals Notes ----- The implementation might change to make more use of the existing VECM framework. See Also -------- statsmodels.tsa.vector_ar.vecm.select_coint_rank References ---------- .. [1] Lütkepohl, H. 2005. New Introduction to Multiple Time Series Analysis. Springer. """ import warnings if det_order not in [-1, 0, 1]: warnings.warn( "Critical values are only available for a det_order of " "-1, 0, or 1.", category=HypothesisTestWarning, stacklevel=2, ) if endog.shape[1] > 12: # todo: test with a time series of 13 variables warnings.warn( "Critical values are only available for time series " "with 12 variables at most.", category=HypothesisTestWarning, stacklevel=2, ) from statsmodels.regression.linear_model import OLS def detrend(y, order): if order == -1: return y return ( OLS(y, np.vander(np.linspace(-1, 1, len(y)), order + 1)) .fit() .resid ) def resid(y, x): if x.size == 0: return y r = y - np.dot(x, np.dot(np.linalg.pinv(x), y)) return r endog = np.asarray(endog) nobs, neqs = endog.shape # why this? f is detrend transformed series, det_order is detrend data if det_order > -1: f = 0 else: f = det_order endog = detrend(endog, det_order) dx = np.diff(endog, 1, axis=0) z = lagmat(dx, k_ar_diff) z = z[k_ar_diff:] z = detrend(z, f) dx = dx[k_ar_diff:] dx = detrend(dx, f) r0t = resid(dx, z) # GH 5731, [:-0] does not work, need [:t-0] lx = endog[: (endog.shape[0] - k_ar_diff)] lx = lx[1:] dx = detrend(lx, f) rkt = resid(dx, z) # level on lagged diffs # Level covariance after filtering k_ar_diff skk = np.dot(rkt.T, rkt) / rkt.shape[0] # Covariacne between filtered and unfiltered sk0 = np.dot(rkt.T, r0t) / rkt.shape[0] s00 = np.dot(r0t.T, r0t) / r0t.shape[0] sig = np.dot(sk0, np.dot(inv(s00), sk0.T)) tmp = inv(skk) au, du = np.linalg.eig(np.dot(tmp, sig)) # au is eval, du is evec temp = inv(np.linalg.cholesky(np.dot(du.T, np.dot(skk, du)))) dt = np.dot(du, temp) # JP: the next part can be done much easier auind = np.argsort(au) aind = np.flipud(auind) a = au[aind] d = dt[:, aind] # Normalize by first non-zero element of d, usually [0, 0] # GH 5517 non_zero_d = d.flat != 0 if np.any(non_zero_d): d *= np.sign(d.flat[non_zero_d][0]) # Compute the trace and max eigenvalue statistics lr1 = np.zeros(neqs) lr2 = np.zeros(neqs) cvm = np.zeros((neqs, 3)) cvt = np.zeros((neqs, 3)) iota = np.ones(neqs) t, junk = rkt.shape for i in range(0, neqs): tmp = np.log(iota - a)[i:] lr1[i] = -t * np.sum(tmp, 0) lr2[i] = -t * np.log(1 - a[i]) cvm[i, :] = c_sja(neqs - i, det_order) cvt[i, :] = c_sjt(neqs - i, det_order) aind[i] = i return JohansenTestResult(rkt, r0t, a, d, lr1, lr2, cvt, cvm, aind)
[docs] class JohansenTestResult: """ Results class for Johansen's cointegration test Notes ----- See p. 292 in [1]_ for r0t and rkt References ---------- .. [1] Lütkepohl, H. 2005. New Introduction to Multiple Time Series Analysis. Springer. """ def __init__(self, rkt, r0t, eig, evec, lr1, lr2, cvt, cvm, ind): self._meth = "johansen" self._rkt = rkt self._r0t = r0t self._eig = eig self._evec = evec self._lr1 = lr1 self._lr2 = lr2 self._cvt = cvt self._cvm = cvm self._ind = ind @property def rkt(self): """Residuals for :math:`Y_{-1}`""" return self._rkt @property def r0t(self): """Residuals for :math:`\\Delta Y`.""" return self._r0t @property def eig(self): """Eigenvalues of VECM coefficient matrix""" return self._eig @property def evec(self): """Eigenvectors of VECM coefficient matrix""" return self._evec @property def trace_stat(self): """Trace statistic""" return self._lr1 @property def lr1(self): """Trace statistic""" return self._lr1 @property def max_eig_stat(self): """Maximum eigenvalue statistic""" return self._lr2 @property def lr2(self): """Maximum eigenvalue statistic""" return self._lr2 @property def trace_stat_crit_vals(self): """Critical values (90%, 95%, 99%) of trace statistic""" return self._cvt @property def cvt(self): """Critical values (90%, 95%, 99%) of trace statistic""" return self._cvt @property def cvm(self): """Critical values (90%, 95%, 99%) of maximum eigenvalue statistic.""" return self._cvm @property def max_eig_stat_crit_vals(self): """Critical values (90%, 95%, 99%) of maximum eigenvalue statistic.""" return self._cvm @property def ind(self): """Order of eigenvalues""" return self._ind @property def meth(self): """Test method""" return self._meth
[docs] class VECM(tsbase.TimeSeriesModel): """ Class representing a Vector Error Correction Model (VECM). A VECM(:math:`k_{ar}-1`) has the following form .. math:: \\Delta y_t = \\Pi y_{t-1} + \\Gamma_1 \\Delta y_{t-1} + \\ldots + \\Gamma_{k_{ar}-1} \\Delta y_{t-k_{ar}+1} + u_t where .. math:: \\Pi = \\alpha \\beta' as described in chapter 7 of [1]_. Parameters ---------- endog : array_like (nobs_tot x neqs) 2-d endogenous response variable. exog : ndarray (nobs_tot x neqs) or None Deterministic terms outside the cointegration relation. exog_coint : ndarray (nobs_tot x neqs) or None Deterministic terms inside the cointegration relation. dates : array_like of datetime, optional See :class:`statsmodels.tsa.base.tsa_model.TimeSeriesModel` for more information. freq : str, optional See :class:`statsmodels.tsa.base.tsa_model.TimeSeriesModel` for more information. missing : str, optional See :class:`statsmodels.base.model.Model` for more information. k_ar_diff : int Number of lagged differences in the model. Equals :math:`k_{ar} - 1` in the formula above. coint_rank : int Cointegration rank, equals the rank of the matrix :math:`\\Pi` and the number of columns of :math:`\\alpha` and :math:`\\beta`. deterministic : str {``"n"``, ``"co"``, ``"ci"``, ``"lo"``, ``"li"``} * ``"n"`` - no deterministic terms * ``"co"`` - constant outside the cointegration relation * ``"ci"`` - constant within the cointegration relation * ``"lo"`` - linear trend outside the cointegration relation * ``"li"`` - linear trend within the cointegration relation Combinations of these are possible (e.g. ``"cili"`` or ``"colo"`` for linear trend with intercept). When using a constant term you have to choose whether you want to restrict it to the cointegration relation (i.e. ``"ci"``) or leave it unrestricted (i.e. ``"co"``). Do not use both ``"ci"`` and ``"co"``. The same applies for ``"li"`` and ``"lo"`` when using a linear term. See the Notes-section for more information. seasons : int, default: 0 Number of periods in a seasonal cycle. 0 means no seasons. first_season : int, default: 0 Season of the first observation. Notes ----- A VECM(:math:`k_{ar} - 1`) with deterministic terms has the form .. math:: \\Delta y_t = \\alpha \\begin{pmatrix}\\beta' & \\eta'\\end{pmatrix} \\begin{pmatrix}y_{t-1}\\\\D^{co}_{t-1}\\end{pmatrix} + \\Gamma_1 \\Delta y_{t-1} + \\dots + \\Gamma_{k_{ar}-1} \\Delta y_{t-k_{ar}+1} + C D_t + u_t. In :math:`D^{co}_{t-1}` we have the deterministic terms which are inside the cointegration relation (or restricted to the cointegration relation). :math:`\\eta` is the corresponding estimator. To pass a deterministic term inside the cointegration relation, we can use the `exog_coint` argument. For the two special cases of an intercept and a linear trend there exists a simpler way to declare these terms: we can pass ``"ci"`` and ``"li"`` respectively to the `deterministic` argument. So for an intercept inside the cointegration relation we can either pass ``"ci"`` as `deterministic` or `np.ones(len(data))` as `exog_coint` if `data` is passed as the `endog` argument. This ensures that :math:`D_{t-1}^{co} = 1` for all :math:`t`. We can also use deterministic terms outside the cointegration relation. These are defined in :math:`D_t` in the formula above with the corresponding estimators in the matrix :math:`C`. We specify such terms by passing them to the `exog` argument. For an intercept and/or linear trend we again have the possibility to use `deterministic` alternatively. For an intercept we pass ``"co"`` and for a linear trend we pass ``"lo"`` where the `o` stands for `outside`. The following table shows the five cases considered in [2]_. The last column indicates which string to pass to the `deterministic` argument for each of these cases. ==== =============================== =================================== ============= Case Intercept Slope of the linear trend `deterministic` ==== =============================== =================================== ============= I 0 0 ``"n"`` II :math:`- \\alpha \\beta^T \\mu` 0 ``"ci"`` III :math:`\\neq 0` 0 ``"co"`` IV :math:`\\neq 0` :math:`- \\alpha \\beta^T \\gamma` ``"coli"`` V :math:`\\neq 0` :math:`\\neq 0` ``"colo"`` ==== =============================== =================================== ============= References ---------- .. [1] Lütkepohl, H. 2005. *New Introduction to Multiple Time Series Analysis*. Springer. .. [2] Johansen, S. 1995. *Likelihood-Based Inference in Cointegrated * *Vector Autoregressive Models*. Oxford University Press. """ def __init__( self, endog, exog=None, exog_coint=None, dates=None, freq=None, missing="none", k_ar_diff=1, coint_rank=1, deterministic="n", seasons=0, first_season=0, ): super().__init__(endog, exog, dates, freq, missing=missing) if ( exog_coint is not None and not exog_coint.shape[0] == endog.shape[0] ): raise ValueError("exog_coint must have as many rows as enodg_tot!") if self.endog.ndim == 1: raise ValueError("Only gave one variable to VECM") self.y = self.endog.T self.exog_coint = exog_coint self.neqs = self.endog.shape[1] self.k_ar = k_ar_diff + 1 self.k_ar_diff = k_ar_diff self.coint_rank = coint_rank self.deterministic = deterministic self.seasons = seasons self.first_season = first_season self.load_coef_repr = "ec" # name for loading coef. (alpha) in summary
[docs] def fit(self, method="ml"): """ Estimates the parameters of a VECM. The estimation procedure is described on pp. 269-304 in [1]_. Parameters ---------- method : str {"ml"}, default: "ml" Estimation method to use. "ml" stands for Maximum Likelihood. Returns ------- est : :class:`VECMResults` References ---------- .. [1] Lütkepohl, H. 2005. *New Introduction to Multiple Time Series Analysis*. Springer. """ if method == "ml": return self._estimate_vecm_ml() else: raise ValueError( "{} not recognized, must be among {}".format(method, "ml") )
def _estimate_vecm_ml(self): y_1_T, delta_y_1_T, y_lag1, delta_x = _endog_matrices( self.y, self.exog, self.exog_coint, self.k_ar_diff, self.deterministic, self.seasons, self.first_season, ) T = y_1_T.shape[1] s00, s01, s10, s11, s11_, _, v = _sij(delta_x, delta_y_1_T, y_lag1) beta_tilde = (v[:, : self.coint_rank].T.dot(s11_)).T beta_tilde = np.real_if_close(beta_tilde) # normalize beta tilde such that eye(r) forms the first r rows of it: beta_tilde = np.dot(beta_tilde, inv(beta_tilde[: self.coint_rank])) alpha_tilde = s01.dot(beta_tilde).dot( inv(beta_tilde.T.dot(s11).dot(beta_tilde)) ) gamma_tilde = ( (delta_y_1_T - alpha_tilde.dot(beta_tilde.T).dot(y_lag1)) .dot(delta_x.T) .dot(inv(np.dot(delta_x, delta_x.T))) ) temp = ( delta_y_1_T - alpha_tilde.dot(beta_tilde.T).dot(y_lag1) - gamma_tilde.dot(delta_x) ) sigma_u_tilde = temp.dot(temp.T) / T return VECMResults( self.y, self.exog, self.exog_coint, self.k_ar, self.coint_rank, alpha_tilde, beta_tilde, gamma_tilde, sigma_u_tilde, deterministic=self.deterministic, seasons=self.seasons, delta_y_1_T=delta_y_1_T, y_lag1=y_lag1, delta_x=delta_x, model=self, names=self.endog_names, dates=self.data.dates, first_season=self.first_season, ) @property def _lagged_param_names(self): """ Returns parameter names (for Gamma and deterministics) for the summary. Returns ------- param_names : list of str Returns a list of parameter names for the lagged endogenous parameters which are called :math:`\\Gamma` in [1]_ (see chapter 6). If present in the model, also names for deterministic terms outside the cointegration relation are returned. They name the elements of the matrix C in [1]_ (p. 299). References ---------- .. [1] Lütkepohl, H. 2005. *New Introduction to Multiple Time Series Analysis*. Springer. """ param_names = [] # 1. Deterministic terms outside cointegration relation if "co" in self.deterministic: param_names += ["const.%s" % n for n in self.endog_names] if self.seasons > 0: param_names += [ "season%d.%s" % (s, n) for s in range(1, self.seasons) for n in self.endog_names ] if "lo" in self.deterministic: param_names += ["lin_trend.%s" % n for n in self.endog_names] if self.exog is not None: param_names += [ "exog%d.%s" % (exog_no, n) for exog_no in range(1, self.exog.shape[1] + 1) for n in self.endog_names ] # 2. lagged endogenous terms param_names += [ "L%d.%s.%s" % (i + 1, n1, n2) for n2 in self.endog_names for i in range(self.k_ar_diff) for n1 in self.endog_names ] return param_names @property def _load_coef_param_names(self): """ Returns parameter names (for alpha) for the summary. Returns ------- param_names : list of str Returns a list of parameter names for the loading coefficients which are called :math:`\\alpha` in [1]_ (see chapter 6). References ---------- .. [1] Lütkepohl, H. 2005. *New Introduction to Multiple Time Series Analysis*. Springer. """ param_names = [] if self.coint_rank == 0: return None # loading coefficients (alpha) # called "ec" in JMulTi, "ECT" in tsDyn, param_names += [ # and "_ce" in Stata self.load_coef_repr + "%d.%s" % (i + 1, self.endog_names[j]) for j in range(self.neqs) for i in range(self.coint_rank) ] return param_names @property def _coint_param_names(self): """ Returns parameter names (for beta and deterministics) for the summary. Returns ------- param_names : list of str Returns a list of parameter names for the cointegration matrix as well as deterministic terms inside the cointegration relation (if present in the model). """ # 1. cointegration matrix/vector param_names = [] param_names += [ ("beta.%d." + self.load_coef_repr + "%d") % (j + 1, i + 1) for i in range(self.coint_rank) for j in range(self.neqs) ] # 2. deterministic terms inside cointegration relation if "ci" in self.deterministic: param_names += [ "const." + self.load_coef_repr + "%d" % (i + 1) for i in range(self.coint_rank) ] if "li" in self.deterministic: param_names += [ "lin_trend." + self.load_coef_repr + "%d" % (i + 1) for i in range(self.coint_rank) ] if self.exog_coint is not None: param_names += [ "exog_coint%d.%s" % (n + 1, exog_no) for exog_no in range(1, self.exog_coint.shape[1] + 1) for n in range(self.neqs) ] return param_names
[docs] class VECMResults: """Class for holding estimation related results of a vector error correction model (VECM). Parameters ---------- endog : ndarray (neqs x nobs_tot) Array of observations. exog : ndarray (nobs_tot x neqs) or `None` Deterministic terms outside the cointegration relation. exog_coint : ndarray (nobs_tot x neqs) or `None` Deterministic terms inside the cointegration relation. k_ar : int, >= 1 Lags in the VAR representation. This implies that the number of lags in the VEC representation (=lagged differences) equals :math:`k_{ar} - 1`. coint_rank : int, 0 <= `coint_rank` <= neqs Cointegration rank, equals the rank of the matrix :math:`\\Pi` and the number of columns of :math:`\\alpha` and :math:`\\beta`. alpha : ndarray (neqs x `coint_rank`) Estimate for the parameter :math:`\\alpha` of a VECM. beta : ndarray (neqs x `coint_rank`) Estimate for the parameter :math:`\\beta` of a VECM. gamma : ndarray (neqs x neqs*(k_ar-1)) Array containing the estimates of the :math:`k_{ar}-1` parameter matrices :math:`\\Gamma_1, \\dots, \\Gamma_{k_{ar}-1}` of a VECM(:math:`k_{ar}-1`). The submatrices are stacked horizontally from left to right. sigma_u : ndarray (neqs x neqs) Estimate of white noise process covariance matrix :math:`\\Sigma_u`. deterministic : str {``"n"``, ``"co"``, ``"ci"``, ``"lo"``, ``"li"``} * ``"n"`` - no deterministic terms * ``"co"`` - constant outside the cointegration relation * ``"ci"`` - constant within the cointegration relation * ``"lo"`` - linear trend outside the cointegration relation * ``"li"`` - linear trend within the cointegration relation Combinations of these are possible (e.g. ``"cili"`` or ``"colo"`` for linear trend with intercept). See the docstring of the :class:`VECM`-class for more information. seasons : int, default: 0 Number of periods in a seasonal cycle. 0 means no seasons. first_season : int, default: 0 Season of the first observation. delta_y_1_T : ndarray or `None`, default: `None` Auxiliary array for internal computations. It will be calculated if not given as parameter. y_lag1 : ndarray or `None`, default: `None` Auxiliary array for internal computations. It will be calculated if not given as parameter. delta_x : ndarray or `None`, default: `None` Auxiliary array for internal computations. It will be calculated if not given as parameter. model : :class:`VECM` An instance of the :class:`VECM`-class. names : list of str Each str in the list represents the name of a variable of the time series. dates : array_like For example a DatetimeIndex of length nobs_tot. Attributes ---------- nobs : int Number of observations (excluding the presample). model : see Parameters y_all : see `endog` in Parameters exog : see Parameters exog_coint : see Parameters names : see Parameters dates : see Parameters neqs : int Number of variables in the time series. k_ar : see Parameters deterministic : see Parameters seasons : see Parameters first_season : see Parameters alpha : see Parameters beta : see Parameters gamma : see Parameters sigma_u : see Parameters det_coef_coint : ndarray (#(determinist. terms inside the coint. rel.) x `coint_rank`) Estimated coefficients for the all deterministic terms inside the cointegration relation. const_coint : ndarray (1 x `coint_rank`) If there is a constant deterministic term inside the cointegration relation, then `const_coint` is the first row of `det_coef_coint`. Otherwise it's an ndarray of zeros. lin_trend_coint : ndarray (1 x `coint_rank`) If there is a linear deterministic term inside the cointegration relation, then `lin_trend_coint` contains the corresponding estimated coefficients. As such it represents the corresponding row of `det_coef_coint`. If there is no linear deterministic term inside the cointegration relation, then `lin_trend_coint` is an ndarray of zeros. exog_coint_coefs : ndarray (exog_coint.shape[1] x `coint_rank`) or `None` If deterministic terms inside the cointegration relation are passed via the `exog_coint` parameter, then `exog_coint_coefs` contains the corresponding estimated coefficients. As such `exog_coint_coefs` represents the last rows of `det_coef_coint`. If no deterministic terms were passed via the `exog_coint` parameter, this attribute is `None`. det_coef : ndarray (neqs x #(deterministic terms outside the coint. rel.)) Estimated coefficients for the all deterministic terms outside the cointegration relation. const : ndarray (neqs x 1) or (neqs x 0) If a constant deterministic term outside the cointegration is specified within the deterministic parameter, then `const` is the first column of `det_coef_coint`. Otherwise it's an ndarray of size zero. seasonal : ndarray (neqs x seasons) If the `seasons` parameter is > 0, then seasonal contains the estimated coefficients corresponding to the seasonal terms. Otherwise it's an ndarray of size zero. lin_trend : ndarray (neqs x 1) or (neqs x 0) If a linear deterministic term outside the cointegration is specified within the deterministic parameter, then `lin_trend` contains the corresponding estimated coefficients. As such it represents the corresponding column of `det_coef_coint`. If there is no linear deterministic term outside the cointegration relation, then `lin_trend` is an ndarray of size zero. exog_coefs : ndarray (neqs x exog_coefs.shape[1]) If deterministic terms outside the cointegration relation are passed via the `exog` parameter, then `exog_coefs` contains the corresponding estimated coefficients. As such `exog_coefs` represents the last columns of `det_coef`. If no deterministic terms were passed via the `exog` parameter, this attribute is an ndarray of size zero. _delta_y_1_T : see delta_y_1_T in Parameters _y_lag1 : see y_lag1 in Parameters _delta_x : see delta_x in Parameters coint_rank : int Cointegration rank, equals the rank of the matrix :math:`\\Pi` and the number of columns of :math:`\\alpha` and :math:`\\beta`. llf : float The model's log-likelihood. cov_params : ndarray (d x d) Covariance matrix of the parameters. The number of rows and columns, d (used in the dimension specification of this argument), is equal to neqs * (neqs+num_det_coef_coint + neqs*(k_ar-1)+number of deterministic dummy variables outside the cointegration relation). For the case with no deterministic terms this matrix is defined on p. 287 in [1]_ as :math:`\\Sigma_{co}` and its relationship to the ML-estimators can be seen in eq. (7.2.21) on p. 296 in [1]_. cov_params_wo_det : ndarray Covariance matrix of the parameters :math:`\\tilde{\\Pi}, \\tilde{\\Gamma}` where :math:`\\tilde{\\Pi} = \\tilde{\\alpha} \\tilde{\\beta'}`. Equals `cov_params` without the rows and columns related to deterministic terms. This matrix is defined as :math:`\\Sigma_{co}` on p. 287 in [1]_. stderr_params : ndarray (d) Array containing the standard errors of :math:`\\Pi`, :math:`\\Gamma`, and estimated parameters related to deterministic terms. stderr_coint : ndarray (neqs+num_det_coef_coint x `coint_rank`) Array containing the standard errors of :math:`\\beta` and estimated parameters related to deterministic terms inside the cointegration relation. stderr_alpha : ndarray (neqs x `coint_rank`) The standard errors of :math:`\\alpha`. stderr_beta : ndarray (neqs x `coint_rank`) The standard errors of :math:`\\beta`. stderr_det_coef_coint : ndarray (num_det_coef_coint x `coint_rank`) The standard errors of estimated the parameters related to deterministic terms inside the cointegration relation. stderr_gamma : ndarray (neqs x neqs*(k_ar-1)) The standard errors of :math:`\\Gamma_1, \\ldots, \\Gamma_{k_{ar}-1}`. stderr_det_coef : ndarray (neqs x det. terms outside the coint. relation) The standard errors of estimated the parameters related to deterministic terms outside the cointegration relation. tvalues_alpha : ndarray (neqs x `coint_rank`) tvalues_beta : ndarray (neqs x `coint_rank`) tvalues_det_coef_coint : ndarray (num_det_coef_coint x `coint_rank`) tvalues_gamma : ndarray (neqs x neqs*(k_ar-1)) tvalues_det_coef : ndarray (neqs x det. terms outside the coint. relation) pvalues_alpha : ndarray (neqs x `coint_rank`) pvalues_beta : ndarray (neqs x `coint_rank`) pvalues_det_coef_coint : ndarray (num_det_coef_coint x `coint_rank`) pvalues_gamma : ndarray (neqs x neqs*(k_ar-1)) pvalues_det_coef : ndarray (neqs x det. terms outside the coint. relation) var_rep : (k_ar x neqs x neqs) KxK parameter matrices :math:`A_i` of the corresponding VAR representation. If the return value is assigned to a variable ``A``, these matrices can be accessed via ``A[i]`` for :math:`i=0, \\ldots, k_{ar}-1`. cov_var_repr : ndarray (neqs**2 * k_ar x neqs**2 * k_ar) This matrix is called :math:`\\Sigma^{co}_{\\alpha}` on p. 289 in [1]_. It is needed e.g. for impulse-response-analysis. fittedvalues : ndarray (nobs x neqs) The predicted in-sample values of the models' endogenous variables. resid : ndarray (nobs x neqs) The residuals. References ---------- .. [1] Lütkepohl, H. 2005. *New Introduction to Multiple Time Series Analysis*. Springer. """ def __init__( self, endog, exog, exog_coint, k_ar, coint_rank, alpha, beta, gamma, sigma_u, deterministic="n", seasons=0, first_season=0, delta_y_1_T=None, y_lag1=None, delta_x=None, model=None, names=None, dates=None, ): self.model = model self.y_all = endog self.exog = exog self.exog_coint = exog_coint self.names = names self.dates = dates self.neqs = endog.shape[0] self.k_ar = k_ar deterministic = string_like(deterministic, "deterministic") self.deterministic = deterministic self.seasons = seasons self.first_season = first_season self.coint_rank = coint_rank if alpha.dtype == np.complex128 and np.all(np.imag(alpha) == 0): alpha = np.real_if_close(alpha) if beta.dtype == np.complex128 and np.all(np.imag(beta) == 0): beta = np.real_if_close(beta) if gamma.dtype == np.complex128 and np.all(np.imag(gamma) == 0): gamma = np.real_if_close(gamma) self.alpha = alpha self.beta, self.det_coef_coint = np.vsplit(beta, [self.neqs]) self.gamma, self.det_coef = np.hsplit( gamma, [self.neqs * (self.k_ar - 1)] ) if "ci" in deterministic: self.const_coint = self.det_coef_coint[:1, :] else: self.const_coint = np.zeros(coint_rank).reshape((1, -1)) if "li" in deterministic: start = 1 if "ci" in deterministic else 0 self.lin_trend_coint = self.det_coef_coint[start : start + 1, :] else: self.lin_trend_coint = np.zeros(coint_rank).reshape(1, -1) if self.exog_coint is not None: start = ("ci" in deterministic) + ("li" in deterministic) self.exog_coint_coefs = self.det_coef_coint[start:, :] else: self.exog_coint_coefs = None split_const_season = 1 if "co" in deterministic else 0 split_season_lin = split_const_season + ( (seasons - 1) if seasons else 0 ) if "lo" in deterministic: split_lin_exog = split_season_lin + 1 else: split_lin_exog = split_season_lin self.const, self.seasonal, self.lin_trend, self.exog_coefs = np.hsplit( self.det_coef, [split_const_season, split_season_lin, split_lin_exog], ) self.sigma_u = sigma_u if ( y_lag1 is not None and delta_x is not None and delta_y_1_T is not None ): self._delta_y_1_T = delta_y_1_T self._y_lag1 = y_lag1 self._delta_x = delta_x else: ( _y_1_T, self._delta_y_1_T, self._y_lag1, self._delta_x, ) = _endog_matrices(endog, self.exog, k_ar, deterministic, seasons) self.nobs = self._y_lag1.shape[1] @cache_readonly def llf(self): # Lutkepohl p. 295 (7.2.20) """ Compute the VECM's loglikelihood. """ K = self.neqs T = self.nobs r = self.coint_rank s00, _, _, _, _, lambd, _ = _sij( self._delta_x, self._delta_y_1_T, self._y_lag1 ) return ( -K * T * np.log(2 * np.pi) / 2 - T * (np.log(np.linalg.det(s00)) + sum(np.log(1 - lambd)[:r])) / 2 - K * T / 2 ) @cache_readonly def _cov_sigma(self): sigma_u = self.sigma_u d = duplication_matrix(self.neqs) d_K_plus = np.linalg.pinv(d) # compare p. 93, 297 Lutkepohl (2005) return 2 * (d_K_plus @ np.kron(sigma_u, sigma_u) @ d_K_plus.T) @cache_readonly def cov_params_default(self): # p.296 (7.2.21) # Sigma_co described on p. 287 beta = self.beta if self.det_coef_coint.size > 0: beta = vstack((beta, self.det_coef_coint)) dt = self.deterministic num_det = ("co" in dt) + ("lo" in dt) num_det += (self.seasons - 1) if self.seasons else 0 if self.exog is not None: num_det += self.exog.shape[1] b_id = scipy.linalg.block_diag( beta, np.identity(self.neqs * (self.k_ar - 1) + num_det) ) y_lag1 = self._y_lag1 b_y = beta.T.dot(y_lag1) omega11 = b_y.dot(b_y.T) omega12 = b_y.dot(self._delta_x.T) omega21 = omega12.T omega22 = self._delta_x.dot(self._delta_x.T) omega = np.bmat([[omega11, omega12], [omega21, omega22]]).A mat1 = b_id.dot(inv(omega)).dot(b_id.T) return np.kron(mat1, self.sigma_u) @cache_readonly def cov_params_wo_det(self): # rows & cols to be dropped (related to deterministic terms inside the # cointegration relation) start_i = self.neqs ** 2 # first elements belong to alpha @ beta.T end_i = start_i + self.neqs * self.det_coef_coint.shape[0] to_drop_i = np.arange(start_i, end_i) # rows & cols to be dropped (related to deterministic terms outside of # the cointegration relation) cov = self.cov_params_default cov_size = len(cov) to_drop_o = np.arange(cov_size - self.det_coef.size, cov_size) to_drop = np.union1d(to_drop_i, to_drop_o) mask = np.ones(cov.shape, dtype=bool) mask[to_drop] = False mask[:, to_drop] = False cov_size_new = mask.sum(axis=0)[0] return cov[mask].reshape((cov_size_new, cov_size_new)) # standard errors: @cache_readonly def stderr_params(self): return np.sqrt(np.diag(self.cov_params_default)) @cache_readonly def stderr_coint(self): """ Standard errors of beta and deterministic terms inside the cointegration relation. Notes ----- See p. 297 in [1]_. Using the rule .. math:: vec(B R) = (B' \\otimes I) vec(R) for two matrices B and R which are compatible for multiplication. This is rule (3) on p. 662 in [1]_. References ---------- .. [1] Lütkepohl, H. 2005. *New Introduction to Multiple Time Series Analysis*. Springer. """ r = self.coint_rank _, r1 = _r_matrices(self._delta_y_1_T, self._y_lag1, self._delta_x) r12 = r1[r:] if r12.size == 0: return np.zeros((r, r)) mat1 = inv(r12.dot(r12.T)) mat1 = np.kron(mat1.T, np.identity(r)) det = self.det_coef_coint.shape[0] mat2 = np.kron( np.identity(self.neqs - r + det), inv(self.alpha.T @ inv(self.sigma_u) @ self.alpha), ) first_rows = np.zeros((r, r)) last_rows_1d = np.sqrt(np.diag(mat1.dot(mat2))) last_rows = last_rows_1d.reshape((self.neqs - r + det, r), order="F") return vstack((first_rows, last_rows)) @cache_readonly def stderr_alpha(self): ret_1dim = self.stderr_params[: self.alpha.size] return ret_1dim.reshape(self.alpha.shape, order="F") @cache_readonly def stderr_beta(self): ret_1dim = self.stderr_coint[: self.beta.shape[0]] return ret_1dim.reshape(self.beta.shape, order="F") @cache_readonly def stderr_det_coef_coint(self): if self.det_coef_coint.size == 0: return self.det_coef_coint # 0-size array ret_1dim = self.stderr_coint[self.beta.shape[0] :] return ret_1dim.reshape(self.det_coef_coint.shape, order="F") @cache_readonly def stderr_gamma(self): start = self.alpha.shape[0] * ( self.beta.shape[0] + self.det_coef_coint.shape[0] ) ret_1dim = self.stderr_params[start : start + self.gamma.size] return ret_1dim.reshape(self.gamma.shape, order="F") @cache_readonly def stderr_det_coef(self): if self.det_coef.size == 0: return self.det_coef # 0-size array ret1_1dim = self.stderr_params[-self.det_coef.size :] return ret1_1dim.reshape(self.det_coef.shape, order="F") # t-values: @cache_readonly def tvalues_alpha(self): return self.alpha / self.stderr_alpha @cache_readonly def tvalues_beta(self): r = self.coint_rank first_rows = np.zeros((r, r)) last_rows = self.beta[r:] / self.stderr_beta[r:] return vstack((first_rows, last_rows)) @cache_readonly def tvalues_det_coef_coint(self): if self.det_coef_coint.size == 0: return self.det_coef_coint # 0-size array return self.det_coef_coint / self.stderr_det_coef_coint @cache_readonly def tvalues_gamma(self): return self.gamma / self.stderr_gamma @cache_readonly def tvalues_det_coef(self): if self.det_coef.size == 0: return self.det_coef # 0-size array return self.det_coef / self.stderr_det_coef # p-values: @cache_readonly def pvalues_alpha(self): return (1 - scipy.stats.norm.cdf(abs(self.tvalues_alpha))) * 2 @cache_readonly def pvalues_beta(self): first_rows = np.zeros((self.coint_rank, self.coint_rank)) tval_last = self.tvalues_beta[self.coint_rank :] last_rows = (1 - scipy.stats.norm.cdf(abs(tval_last))) * 2 # student-t return vstack((first_rows, last_rows)) @cache_readonly def pvalues_det_coef_coint(self): if self.det_coef_coint.size == 0: return self.det_coef_coint # 0-size array return (1 - scipy.stats.norm.cdf(abs(self.tvalues_det_coef_coint))) * 2 @cache_readonly def pvalues_gamma(self): return (1 - scipy.stats.norm.cdf(abs(self.tvalues_gamma))) * 2 @cache_readonly def pvalues_det_coef(self): if self.det_coef.size == 0: return self.det_coef # 0-size array return (1 - scipy.stats.norm.cdf(abs(self.tvalues_det_coef))) * 2 # confidence intervals def _make_conf_int(self, est, stderr, alpha): struct_arr = np.zeros( est.shape, dtype=[("lower", float), ("upper", float)] ) struct_arr["lower"] = ( est - scipy.stats.norm.ppf(1 - alpha / 2) * stderr ) struct_arr["upper"] = ( est + scipy.stats.norm.ppf(1 - alpha / 2) * stderr ) return struct_arr
[docs] def conf_int_alpha(self, alpha=0.05): return self._make_conf_int(self.alpha, self.stderr_alpha, alpha)
[docs] def conf_int_beta(self, alpha=0.05): return self._make_conf_int(self.beta, self.stderr_beta, alpha)
[docs] def conf_int_det_coef_coint(self, alpha=0.05): return self._make_conf_int( self.det_coef_coint, self.stderr_det_coef_coint, alpha )
[docs] def conf_int_gamma(self, alpha=0.05): return self._make_conf_int(self.gamma, self.stderr_gamma, alpha)
[docs] def conf_int_det_coef(self, alpha=0.05): return self._make_conf_int(self.det_coef, self.stderr_det_coef, alpha)
@cache_readonly def var_rep(self): pi = self.alpha.dot(self.beta.T) gamma = self.gamma K = self.neqs A = np.zeros((self.k_ar, K, K)) A[0] = pi + np.identity(K) if self.gamma.size > 0: A[0] += gamma[:, :K] A[self.k_ar - 1] = -gamma[:, K * (self.k_ar - 2) :] for i in range(1, self.k_ar - 1): A[i] = ( gamma[:, K * i : K * (i + 1)] - gamma[:, K * (i - 1) : K * i] ) return A @cache_readonly def cov_var_repr(self): """ Gives the covariance matrix of the corresponding VAR-representation. More precisely, the covariance matrix of the vector consisting of the columns of the corresponding VAR coefficient matrices (i.e. vec(self.var_rep)). Returns ------- cov : array (neqs**2 * k_ar x neqs**2 * k_ar) """ # This implementation is using the fact that for a random variable x # with covariance matrix Sigma_x the following holds: # B @ x with B being a suitably sized matrix has the covariance matrix # B @ Sigma_x @ B.T. The arrays called vecm_var_transformation and # self.cov_params_wo_det in the code play the roles of B and Sigma_x # respectively. The elements of the random variable x are the elements # of the estimated matrices Pi (alpha @ beta.T) and Gamma. # Alternatively the following code (commented out) would yield the same # result (following p. 289 in Lutkepohl): # K, p = self.neqs, self.k_ar # w = np.identity(K * p) # w[np.arange(K, len(w)), np.arange(K, len(w))] *= (-1) # w[np.arange(K, len(w)), np.arange(len(w)-K)] = 1 # # w_eye = np.kron(w, np.identity(K)) # # return w_eye.T @ self.cov_params_default @ w_eye if self.k_ar - 1 == 0: return self.cov_params_wo_det vecm_var_transformation = np.zeros( (self.neqs ** 2 * self.k_ar, self.neqs ** 2 * self.k_ar) ) eye = np.identity(self.neqs ** 2) # for A_1: vecm_var_transformation[ : self.neqs ** 2, : 2 * self.neqs ** 2 ] = hstack((eye, eye)) # for A_i, where i = 2, ..., k_ar-1 for i in range(2, self.k_ar): start_row = self.neqs ** 2 + (i - 2) * self.neqs ** 2 start_col = self.neqs ** 2 + (i - 2) * self.neqs ** 2 vecm_var_transformation[ start_row : start_row + self.neqs ** 2, start_col : start_col + 2 * self.neqs ** 2, ] = hstack((-eye, eye)) # for A_p: vecm_var_transformation[-self.neqs ** 2 :, -self.neqs ** 2 :] = -eye vvt = vecm_var_transformation return vvt @ self.cov_params_wo_det @ vvt.T
[docs] def ma_rep(self, maxn=10): return ma_rep(self.var_rep, maxn)
@cache_readonly def _chol_sigma_u(self): return np.linalg.cholesky(self.sigma_u)
[docs] def orth_ma_rep(self, maxn=10, P=None): """Compute orthogonalized MA coefficient matrices. For this purpose a matrix P is used which fulfills :math:`\\Sigma_u = PP^\\prime`. P defaults to the Cholesky decomposition of :math:`\\Sigma_u` Parameters ---------- maxn : int Number of coefficient matrices to compute P : ndarray (neqs x neqs), optional Matrix such that :math:`\\Sigma_u = PP'`. Defaults to Cholesky decomposition. Returns ------- coefs : ndarray (maxn x neqs x neqs) """ return orth_ma_rep(self, maxn, P)
[docs] def predict(self, steps=5, alpha=None, exog_fc=None, exog_coint_fc=None): """ Calculate future values of the time series. Parameters ---------- steps : int Prediction horizon. alpha : float, 0 < `alpha` < 1 or None If None, compute point forecast only. If float, compute confidence intervals too. In this case the argument stands for the confidence level. exog : ndarray (steps x self.exog.shape[1]) If self.exog is not None, then information about the future values of exog have to be passed via this parameter. The ndarray may be larger in it's first dimension. In this case only the first steps rows will be considered. Returns ------- forecast - ndarray (steps x neqs) or three ndarrays In case of a point forecast: each row of the returned ndarray represents the forecast of the neqs variables for a specific period. The first row (index [0]) is the forecast for the next period, the last row (index [steps-1]) is the steps-periods-ahead- forecast. """ if self.exog is not None and exog_fc is None: raise ValueError( "exog_fc is None: Please pass the future values " "of the VECM's exog terms via the exog_fc " "argument!" ) if self.exog is None and exog_fc is not None: raise ValueError( "This VECMResult-instance's exog attribute is " "None. Please do not pass a non-None value as the " "method's exog_fc-argument." ) if exog_fc is not None and exog_fc.shape[0] < steps: raise ValueError( "The argument exog_fc must have at least steps " "elements in its first dimension" ) if self.exog_coint is not None and exog_coint_fc is None: raise ValueError( "exog_coint_fc is None: Please pass the future " "values of the VECM's exog_coint terms via the " "exog_coint_fc argument!" ) if self.exog_coint is None and exog_coint_fc is not None: raise ValueError( "This VECMResult-instance's exog_coint attribute " "is None. Please do not pass a non-None value as " "the method's exog_coint_fc-argument." ) if exog_coint_fc is not None and exog_coint_fc.shape[0] < steps - 1: raise ValueError( "The argument exog_coint_fc must have at least " "steps elements in its first dimension" ) last_observations = self.y_all.T[-self.k_ar :] exog = [] trend_coefs = [] # adding deterministic terms outside cointegration relation exog_const = np.ones(steps) nobs_tot = self.nobs + self.k_ar if self.const.size > 0: exog.append(exog_const) trend_coefs.append(self.const.T) if self.seasons > 0: first_future_season = (self.first_season + nobs_tot) % self.seasons exog_seasonal = seasonal_dummies( self.seasons, steps, first_future_season, True ) exog.append(exog_seasonal) trend_coefs.append(self.seasonal.T) exog_lin_trend = _linear_trend(self.nobs, self.k_ar) exog_lin_trend = exog_lin_trend[-1] + 1 + np.arange(steps) if self.lin_trend.size > 0: exog.append(exog_lin_trend) trend_coefs.append(self.lin_trend.T) if exog_fc is not None: exog.append(exog_fc[:steps]) trend_coefs.append(self.exog_coefs.T) # adding deterministic terms inside cointegration relation if "ci" in self.deterministic: exog.append(exog_const) trend_coefs.append(self.alpha.dot(self.const_coint.T).T) exog_lin_trend_coint = _linear_trend(self.nobs, self.k_ar, coint=True) exog_lin_trend_coint = exog_lin_trend_coint[-1] + 1 + np.arange(steps) if "li" in self.deterministic: exog.append(exog_lin_trend_coint) trend_coefs.append(self.alpha.dot(self.lin_trend_coint.T).T) if exog_coint_fc is not None: if exog_coint_fc.ndim == 1: exog_coint_fc = exog_coint_fc[:, None] # make 2-D exog_coint_fc = np.vstack( (self.exog_coint[-1:], exog_coint_fc[: steps - 1]) ) exog.append(exog_coint_fc) trend_coefs.append(self.alpha.dot(self.exog_coint_coefs.T).T) # glueing all deterministics together exog = np.column_stack(exog) if exog != [] else None if trend_coefs != []: trend_coefs = np.vstack(trend_coefs) else: trend_coefs = None # call the forecasting function of the VAR-module if alpha is not None: return forecast_interval( last_observations, self.var_rep, trend_coefs, self.sigma_u, steps, alpha=alpha, exog=exog, ) else: return forecast( last_observations, self.var_rep, trend_coefs, steps, exog )
[docs] def plot_forecast( self, steps, alpha=0.05, plot_conf_int=True, n_last_obs=None ): """ Plot the forecast. Parameters ---------- steps : int Prediction horizon. alpha : float, 0 < `alpha` < 1 The confidence level. plot_conf_int : bool, default: True If True, plot bounds of confidence intervals. n_last_obs : int or None, default: None If int, restrict plotted history to n_last_obs observations. If None, include the whole history in the plot. """ mid, lower, upper = self.predict(steps, alpha=alpha) y = self.y_all.T y = y[self.k_ar :] if n_last_obs is None else y[-n_last_obs:] plot.plot_var_forc( y, mid, lower, upper, names=self.names, plot_stderr=plot_conf_int, legend_options={"loc": "lower left"}, )
[docs] def test_granger_causality(self, caused, causing=None, signif=0.05): r""" Test for Granger-causality. The concept of Granger-causality is described in chapter 7.6.3 of [1]_. Test |H0|: "The variables in `causing` do not Granger-cause those in `caused`" against |H1|: "`causing` is Granger-causal for `caused`". Parameters ---------- caused : int or str or sequence of int or str If int or str, test whether the variable specified via this index (int) or name (str) is Granger-caused by the variable(s) specified by `causing`. If a sequence of int or str, test whether the corresponding variables are Granger-caused by the variable(s) specified by `causing`. causing : int or str or sequence of int or str or `None`, default: `None` If int or str, test whether the variable specified via this index (int) or name (str) is Granger-causing the variable(s) specified by `caused`. If a sequence of int or str, test whether the corresponding variables are Granger-causing the variable(s) specified by `caused`. If `None`, `causing` is assumed to be the complement of `caused` (the remaining variables of the system). signif : float, 0 < `signif` < 1, default 5 % Significance level for computing critical values for test, defaulting to standard 0.05 level. Returns ------- results : :class:`statsmodels.tsa.vector_ar.hypothesis_test_results.CausalityTestResults` References ---------- .. [1] Lütkepohl, H. 2005. *New Introduction to Multiple Time Series Analysis*. Springer. .. |H0| replace:: H\ :sub:`0` .. |H1| replace:: H\ :sub:`1` """ if not (0 < signif < 1): raise ValueError("signif has to be between 0 and 1") allowed_types = (str, int) if isinstance(caused, allowed_types): caused = [caused] if not all(isinstance(c, allowed_types) for c in caused): raise TypeError( "caused has to be of type string or int (or a " "sequence of these types)." ) caused = [self.names[c] if type(c) is int else c for c in caused] caused_ind = [get_index(self.names, c) for c in caused] if causing is not None: if isinstance(causing, allowed_types): causing = [causing] if not all(isinstance(c, allowed_types) for c in causing): raise TypeError( "causing has to be of type string or int (or " "a sequence of these types) or None." ) causing = [self.names[c] if type(c) is int else c for c in causing] causing_ind = [get_index(self.names, c) for c in causing] if causing is None: causing_ind = [i for i in range(self.neqs) if i not in caused_ind] causing = [self.names[c] for c in causing_ind] y, k, t, p = self.y_all, self.neqs, self.nobs - 1, self.k_ar + 1 exog = _deterministic_to_exog( self.deterministic, self.seasons, nobs_tot=self.nobs + self.k_ar, first_season=self.first_season, seasons_centered=True, exog=self.exog, exog_coint=self.exog_coint, ) var_results = VAR(y.T, exog).fit(maxlags=p, trend="n") # num_restr is called N in Lutkepohl num_restr = len(causing) * len(caused) * (p - 1) num_det_terms = _num_det_vars(self.deterministic, self.seasons) if self.exog is not None: num_det_terms += self.exog.shape[1] if self.exog_coint is not None: num_det_terms += self.exog_coint.shape[1] # Make restriction matrix C = np.zeros( (num_restr, k * num_det_terms + k ** 2 * (p - 1)), dtype=float ) cols_det = k * num_det_terms row = 0 for j in range(p - 1): for ing_ind in causing_ind: for ed_ind in caused_ind: C[row, cols_det + ed_ind + k * ing_ind + k ** 2 * j] = 1 row += 1 Ca = np.dot(C, vec(var_results.params[:-k].T)) x_min_p_components = [] if exog is not None: x_min_p_components.append(exog[-t:].T) x_min_p = np.zeros((k * p, t)) for i in range(p - 1): # fll first k * k_ar rows of x_min_p x_min_p[i * k : (i + 1) * k, :] = ( y[:, p - 1 - i : -1 - i] - y[:, :-p] ) x_min_p[-k:, :] = y[:, :-p] # fill last rows of x_min_p x_min_p_components.append(x_min_p) x_min_p = np.vstack(x_min_p_components) x_x = np.dot(x_min_p, x_min_p.T) # k*k_ar x k*k_ar x_x_11 = inv(x_x)[ : k * (p - 1) + num_det_terms, : k * (p - 1) + num_det_terms ] # k*(k_ar-1) x k*(k_ar-1) # For VAR-models with parameter restrictions the denominator in the # calculation of sigma_u is nobs and not (nobs-k*k_ar-num_det_terms). # Testing for Granger-causality means testing for restricted # parameters, thus the former of the two denominators is used. As # Lutkepohl states, both variants of the estimated sigma_u are # possible. (see Lutkepohl, p.198) # The choice of the denominator T has also the advantage of getting the # same results as the reference software JMulTi. sigma_u = var_results.sigma_u * (t - k * p - num_det_terms) / t sig_alpha_min_p = t * np.kron(x_x_11, sigma_u) # k**2*(p-1)xk**2*(p-1) middle = inv(C @ sig_alpha_min_p @ C.T) wald_statistic = t * (Ca.T @ middle @ Ca) f_statistic = wald_statistic / num_restr df = (num_restr, k * var_results.df_resid) f_distribution = scipy.stats.f(*df) pvalue = f_distribution.sf(f_statistic) crit_value = f_distribution.ppf(1 - signif) return CausalityTestResults( causing, caused, f_statistic, crit_value, pvalue, df, signif, test="granger", method="f", )
[docs] def test_inst_causality(self, causing, signif=0.05): r""" Test for instantaneous causality. The concept of instantaneous causality is described in chapters 3.6.3 and 7.6.4 of [1]_. Test |H0|: "No instantaneous causality between the variables in `caused` and those in `causing`" against |H1|: "Instantaneous causality between `caused` and `causing` exists". Note that instantaneous causality is a symmetric relation (i.e. if `causing` is "instantaneously causing" `caused`, then also `caused` is "instantaneously causing" `causing`), thus the naming of the parameters (which is chosen to be in accordance with :meth:`test_granger_causality()`) may be misleading. Parameters ---------- causing : int or str or sequence of int or str If int or str, test whether the corresponding variable is causing the variable(s) specified in caused. If sequence of int or str, test whether the corresponding variables are causing the variable(s) specified in caused. signif : float, 0 < `signif` < 1, default 5 % Significance level for computing critical values for test, defaulting to standard 0.05 level. Returns ------- results : :class:`statsmodels.tsa.vector_ar.hypothesis_test_results.CausalityTestResults` Notes ----- This method is not returning the same result as `JMulTi`. This is because the test is based on a VAR(k_ar) model in `statsmodels` (in accordance to pp. 104, 320-321 in [1]_) whereas `JMulTi` seems to be using a VAR(k_ar+1) model. Reducing the lag order by one in `JMulTi` leads to equal results in `statsmodels` and `JMulTi`. References ---------- .. [1] Lütkepohl, H. 2005. *New Introduction to Multiple Time Series Analysis*. Springer. .. |H0| replace:: H\ :sub:`0` .. |H1| replace:: H\ :sub:`1` """ exog = _deterministic_to_exog( self.deterministic, self.seasons, nobs_tot=self.nobs + self.k_ar, first_season=self.first_season, seasons_centered=True, exog=self.exog, exog_coint=self.exog_coint, ) # Note: JMulTi seems to be using k_ar+1 instead of k_ar k, t, p = self.neqs, self.nobs, self.k_ar # fit with trend "n" because all trend information is already in exog var_results = VAR(self.y_all.T, exog).fit(maxlags=p, trend="n") var_results._results.names = self.names return var_results.test_inst_causality(causing=causing, signif=signif)
[docs] def irf(self, periods=10): return irf.IRAnalysis(self, periods=periods, vecm=True)
@cache_readonly def fittedvalues(self): """ Return the in-sample values of endog calculated by the model. Returns ------- fitted : array (nobs x neqs) The predicted in-sample values of the models' endogenous variables. """ beta = self.beta if self.det_coef_coint.size > 0: beta = vstack((beta, self.det_coef_coint)) pi = np.dot(self.alpha, beta.T) gamma = self.gamma if self.det_coef.size > 0: gamma = hstack((gamma, self.det_coef)) delta_y = np.dot(pi, self._y_lag1) + np.dot(gamma, self._delta_x) return (delta_y + self._y_lag1[: self.neqs]).T @cache_readonly def resid(self): """ Return the difference between observed and fitted values. Returns ------- resid : array (nobs x neqs) The residuals. """ return self.y_all.T[self.k_ar :] - self.fittedvalues
[docs] def test_normality(self, signif=0.05): r""" Test assumption of normal-distributed errors using Jarque-Bera-style omnibus :math:`\\chi^2` test. Parameters ---------- signif : float The test's significance level. Returns ------- result : :class:`statsmodels.tsa.vector_ar.hypothesis_test_results.NormalityTestResults` Notes ----- |H0| : data are generated by a Gaussian-distributed process .. |H0| replace:: H\ :sub:`0` """ return test_normality(self, signif=signif)
[docs] def test_whiteness(self, nlags=10, signif=0.05, adjusted=False): """ Test the whiteness of the residuals using the Portmanteau test. This test is described in [1]_, chapter 8.4.1. Parameters ---------- nlags : int > 0 signif : float, 0 < `signif` < 1 adjusted : bool, default False Returns ------- result : :class:`statsmodels.tsa.vector_ar.hypothesis_test_results.WhitenessTestResults` References ---------- .. [1] Lütkepohl, H. 2005. *New Introduction to Multiple Time Series Analysis*. Springer. """ statistic = 0 u = np.asarray(self.resid) acov_list = _compute_acov(u, nlags) # self.sigma_u instead of cov(0) is necessary to get the same # result as JMulTi. The difference between the two is that sigma_u is # calculated with the usual residuals while in cov(0) the # residuals are demeaned. To me JMulTi's behaviour seems a bit strange # because it uses the usual residuals here but demeaned residuals in # the calculation of autocovariances with lag > 0. (used in the # argument of trace() four rows below this comment.) c0_inv = inv(self.sigma_u) # instead of inv(cov(0)) if c0_inv.dtype == np.complex128 and np.all(np.imag(c0_inv) == 0): c0_inv = np.real(c0_inv) for t in range(1, nlags + 1): ct = acov_list[t] to_add = np.trace(ct.T @ c0_inv @ ct @ c0_inv) if adjusted: to_add /= self.nobs - t statistic += to_add statistic *= self.nobs ** 2 if adjusted else self.nobs df = ( self.neqs ** 2 * (nlags - self.k_ar + 1) - self.neqs * self.coint_rank ) dist = scipy.stats.chi2(df) pvalue = dist.sf(statistic) crit_value = dist.ppf(1 - signif) return WhitenessTestResults( statistic, crit_value, pvalue, df, signif, nlags, adjusted )
[docs] def plot_data(self, with_presample=False): """ Plot the input time series. Parameters ---------- with_presample : bool, default: `False` If `False`, the pre-sample data (the first `k_ar` values) will not be plotted. """ y = self.y_all if with_presample else self.y_all[:, self.k_ar :] names = self.names dates = self.dates if with_presample else self.dates[self.k_ar :] plot.plot_mts(y.T, names=names, index=dates)
[docs] def summary(self, alpha=0.05): """ Return a summary of the estimation results. Parameters ---------- alpha : float 0 < `alpha` < 1, default 0.05 Significance level of the shown confidence intervals. Returns ------- summary : :class:`statsmodels.iolib.summary.Summary` A summary containing information about estimated parameters. """ from statsmodels.iolib.summary import summary_params summary = Summary() def make_table( self, params, std_err, t_values, p_values, conf_int, mask, names, title, strip_end=True, ): res = ( self, params[mask], std_err[mask], t_values[mask], p_values[mask], conf_int[mask], ) param_names = [ ".".join(name.split(".")[:-1]) if strip_end else name for name in np.array(names)[mask].tolist() ] return summary_params( res, yname=None, xname=param_names, alpha=alpha, use_t=False, title=title, ) # --------------------------------------------------------------------- # Add tables with gamma and det_coef for each endogenous variable: lagged_params_components = [] stderr_lagged_params_components = [] tvalues_lagged_params_components = [] pvalues_lagged_params_components = [] conf_int_lagged_params_components = [] if self.det_coef.size > 0: lagged_params_components.append(self.det_coef.flatten(order="F")) stderr_lagged_params_components.append( self.stderr_det_coef.flatten(order="F") ) tvalues_lagged_params_components.append( self.tvalues_det_coef.flatten(order="F") ) pvalues_lagged_params_components.append( self.pvalues_det_coef.flatten(order="F") ) conf_int = self.conf_int_det_coef(alpha=alpha) lower = conf_int["lower"].flatten(order="F") upper = conf_int["upper"].flatten(order="F") conf_int_lagged_params_components.append( np.column_stack((lower, upper)) ) if self.k_ar - 1 > 0: lagged_params_components.append(self.gamma.flatten()) stderr_lagged_params_components.append(self.stderr_gamma.flatten()) tvalues_lagged_params_components.append( self.tvalues_gamma.flatten() ) pvalues_lagged_params_components.append( self.pvalues_gamma.flatten() ) conf_int = self.conf_int_gamma(alpha=alpha) lower = conf_int["lower"].flatten() upper = conf_int["upper"].flatten() conf_int_lagged_params_components.append( np.column_stack((lower, upper)) ) # if gamma or det_coef exists, then make a summary-table for them: if len(lagged_params_components) != 0: lagged_params = hstack(lagged_params_components) stderr_lagged_params = hstack(stderr_lagged_params_components) tvalues_lagged_params = hstack(tvalues_lagged_params_components) pvalues_lagged_params = hstack(pvalues_lagged_params_components) conf_int_lagged_params = vstack(conf_int_lagged_params_components) for i in range(self.neqs): masks = [] offset = 0 # 1. Deterministic terms outside cointegration relation if "co" in self.deterministic: masks.append(offset + np.array(i, ndmin=1)) offset += self.neqs if self.seasons > 0: for _ in range(self.seasons - 1): masks.append(offset + np.array(i, ndmin=1)) offset += self.neqs if "lo" in self.deterministic: masks.append(offset + np.array(i, ndmin=1)) offset += self.neqs if self.exog is not None: for _ in range(self.exog.shape[1]): masks.append(offset + np.array(i, ndmin=1)) offset += self.neqs # 2. Lagged endogenous terms if self.k_ar - 1 > 0: start = i * self.neqs * (self.k_ar - 1) end = (i + 1) * self.neqs * (self.k_ar - 1) masks.append(offset + np.arange(start, end)) # offset += self.neqs**2 * (self.k_ar-1) # Create the table mask = np.concatenate(masks) eq_name = self.model.endog_names[i] title = ( "Det. terms outside the coint. relation " + "& lagged endog. parameters for equation %s" % eq_name ) table = make_table( self, lagged_params, stderr_lagged_params, tvalues_lagged_params, pvalues_lagged_params, conf_int_lagged_params, mask, self.model._lagged_param_names, title, ) summary.tables.append(table) # --------------------------------------------------------------------- # Loading coefficients (alpha): a = self.alpha.flatten() se_a = self.stderr_alpha.flatten() t_a = self.tvalues_alpha.flatten() p_a = self.pvalues_alpha.flatten() ci_a = self.conf_int_alpha(alpha=alpha) lower = ci_a["lower"].flatten() upper = ci_a["upper"].flatten() ci_a = np.column_stack((lower, upper)) a_names = self.model._load_coef_param_names alpha_masks = [] for i in range(self.neqs): if self.coint_rank > 0: start = i * self.coint_rank end = start + self.coint_rank mask = np.arange(start, end) # Create the table alpha_masks.append(mask) eq_name = self.model.endog_names[i] title = "Loading coefficients (alpha) for equation %s" % eq_name table = make_table( self, a, se_a, t_a, p_a, ci_a, mask, a_names, title ) summary.tables.append(table) # --------------------------------------------------------------------- # Cointegration matrix/vector (beta) and det. terms inside coint. rel.: coint_components = [] stderr_coint_components = [] tvalues_coint_components = [] pvalues_coint_components = [] conf_int_coint_components = [] if self.coint_rank > 0: coint_components.append(self.beta.T.flatten()) stderr_coint_components.append(self.stderr_beta.T.flatten()) tvalues_coint_components.append(self.tvalues_beta.T.flatten()) pvalues_coint_components.append(self.pvalues_beta.T.flatten()) conf_int = self.conf_int_beta(alpha=alpha) lower = conf_int["lower"].T.flatten() upper = conf_int["upper"].T.flatten() conf_int_coint_components.append(np.column_stack((lower, upper))) if self.det_coef_coint.size > 0: coint_components.append(self.det_coef_coint.flatten()) stderr_coint_components.append( self.stderr_det_coef_coint.flatten() ) tvalues_coint_components.append( self.tvalues_det_coef_coint.flatten() ) pvalues_coint_components.append( self.pvalues_det_coef_coint.flatten() ) conf_int = self.conf_int_det_coef_coint(alpha=alpha) lower = conf_int["lower"].flatten() upper = conf_int["upper"].flatten() conf_int_coint_components.append(np.column_stack((lower, upper))) coint = hstack(coint_components) stderr_coint = hstack(stderr_coint_components) tvalues_coint = hstack(tvalues_coint_components) pvalues_coint = hstack(pvalues_coint_components) conf_int_coint = vstack(conf_int_coint_components) coint_names = self.model._coint_param_names for i in range(self.coint_rank): masks = [] offset = 0 # 1. Cointegration matrix (beta) if self.coint_rank > 0: start = i * self.neqs end = start + self.neqs masks.append(offset + np.arange(start, end)) offset += self.neqs * self.coint_rank # 2. Deterministic terms inside cointegration relation if "ci" in self.deterministic: masks.append(offset + np.array(i, ndmin=1)) offset += self.coint_rank if "li" in self.deterministic: masks.append(offset + np.array(i, ndmin=1)) offset += self.coint_rank if self.exog_coint is not None: for _ in range(self.exog_coint.shape[1]): masks.append(offset + np.array(i, ndmin=1)) offset += self.coint_rank # Create the table mask = np.concatenate(masks) title = ( "Cointegration relations for " + "loading-coefficients-column %d" % (i + 1) ) table = make_table( self, coint, stderr_coint, tvalues_coint, pvalues_coint, conf_int_coint, mask, coint_names, title, ) summary.tables.append(table) return summary

Last update: May 14, 2024