import numpy as np from numpy.testing import ( assert_, assert_allclose, assert_array_equal, assert_raises, assert_raises_regex, ) import pytest import statsmodels.datasets.interest_inflation.data as e6 from statsmodels.tools.testing import assert_equal from statsmodels.tsa.vector_ar.tests.JMulTi_results.parse_jmulti_vecm_output import ( dt_s_tup_to_string, load_results_jmulti, sublists, ) from statsmodels.tsa.vector_ar.util import seasonal_dummies from statsmodels.tsa.vector_ar.var_model import VARProcess from statsmodels.tsa.vector_ar.vecm import ( VECM, select_coint_rank, select_order, ) pytestmark = pytest.mark.filterwarnings("ignore:in the future np.array_split") class DataSet: """ A class for representing the data in a data module. Parameters ---------- data_module : module A module contained in the statsmodels/datasets directory. n_seasons : list A list of integers. Each int represents a number of seasons to test the model with. first_season : list A list of integers. Each int corresponds to the int with the same index in the n_seasons list and declares to which season the first data entry belongs. variable_names : list A list of strings. Each string names a variable. """ def __init__(self, data_module, n_seasons, first_season, variable_names): self.data_module = data_module self.seasons = n_seasons self.first_season = first_season self.dt_s_list = [ (det, s, self.first_season[i]) for det in deterministic_terms_list for i, s in enumerate(self.seasons) ] self.variable_names = variable_names def __str__(self): return self.data_module.__str__() atol = 0.0005 # absolute tolerance rtol = 0 # relative tolerance datasets = [] data = {} results_ref = {} results_sm = {} results_sm_exog = {} results_sm_exog_coint = {} coint_rank = 1 debug_mode = False dont_test_se_t_p = False deterministic_terms_list = ["nc", "co", "colo", "ci", "cili"] all_tests = [ "Gamma", "alpha", "beta", "C", "det_coint", "Sigma_u", "VAR repr. A", "VAR to VEC representation", "log_like", "fc", "granger", "inst. causality", "impulse-response", "lag order", "test_norm", "whiteness", "summary", "exceptions", "select_coint_rank", ] to_test = all_tests # ["beta"] def load_data(dataset, data_dict): dtset = dataset.data_module.load_pandas() variables = dataset.variable_names loaded = dtset.data[variables].astype(float).values data_dict[dataset] = loaded.reshape((-1, len(variables))) def load_results_statsmodels(dataset): results_per_deterministic_terms = dict.fromkeys(dataset.dt_s_list) for dt_s_tup in dataset.dt_s_list: model = VECM( data[dataset], k_ar_diff=3, coint_rank=coint_rank, deterministic=dt_s_tup[0], seasons=dt_s_tup[1], first_season=dt_s_tup[2], ) results_per_deterministic_terms[dt_s_tup] = model.fit(method="ml") return results_per_deterministic_terms def load_results_statsmodels_exog(dataset): """ Load data with seasonal terms in `exog`. Same as load_results_statsmodels() except that the seasonal term is provided to :class:`VECM`'s `__init__()` method via the `eoxg` parameter. This is to check whether the same results are produced no matter whether `exog` or `seasons` is being used. Parameters ---------- dataset : DataSet """ results_per_deterministic_terms = dict.fromkeys(dataset.dt_s_list) endog = data[dataset] for dt_s_tup in dataset.dt_s_list: det_string = dt_s_tup[0] seasons = dt_s_tup[1] first_season = dt_s_tup[2] if seasons == 0: exog = None else: exog = seasonal_dummies( seasons, len(data[dataset]), first_season, centered=True ) if "lo" in dt_s_tup[0]: exog = np.hstack( (exog, 1 + np.arange(len(endog)).reshape(-1, 1)) ) # remove "lo" since it's now already in exog. det_string = det_string[:-2] model = VECM( endog, exog, k_ar_diff=3, coint_rank=coint_rank, deterministic=det_string, ) results_per_deterministic_terms[dt_s_tup] = model.fit(method="ml") return results_per_deterministic_terms def load_results_statsmodels_exog_coint(dataset): """ Load data with deterministic terms in `exog_coint`. Same as load_results_statsmodels() except that deterministic terms inside the cointegration relation are provided to :class:`VECM`'s `__init__()` method via the `eoxg_coint` parameter. This is to check whether the same results are produced no matter whether `exog_coint` or the `deterministic` argument is being used. Parameters ---------- dataset : DataSet """ results_per_deterministic_terms = dict.fromkeys(dataset.dt_s_list) endog = data[dataset] for dt_s_tup in dataset.dt_s_list: det_string = dt_s_tup[0] if "ci" not in det_string and "li" not in det_string: exog_coint = None else: exog_coint = [] if "li" in det_string: exog_coint.append(1 + np.arange(len(endog)).reshape(-1, 1)) det_string = det_string[:-2] if "ci" in det_string: exog_coint.append(np.ones(len(endog)).reshape(-1, 1)) det_string = det_string[:-2] # reversing (such that constant is first and linear is second) exog_coint = exog_coint[::-1] exog_coint = np.hstack(exog_coint) model = VECM( endog, exog=None, exog_coint=exog_coint, k_ar_diff=3, coint_rank=coint_rank, deterministic=det_string, seasons=dt_s_tup[1], first_season=dt_s_tup[2], ) results_per_deterministic_terms[dt_s_tup] = model.fit(method="ml") return results_per_deterministic_terms def build_err_msg(ds, dt_s, parameter_str): dt = dt_s_tup_to_string(dt_s) seasons = dt_s[1] err_msg = "Error in " + parameter_str + " for:\n" err_msg += "- Dataset: " + ds.__str__() + "\n" err_msg += "- Deterministic terms: " err_msg += dt_s[0] if dt != "n" else "no det. terms" if seasons > 0: err_msg += ", seasons: " + str(seasons) return err_msg def setup(): datasets.append( DataSet(e6, [0, 4], [0, 1], ["Dp", "R"]), # DataSet(...) TODO: append more data sets for more test cases. ) for ds in datasets: load_data(ds, data) results_ref[ds] = load_results_jmulti(ds) results_sm[ds] = load_results_statsmodels(ds) results_sm_exog[ds] = load_results_statsmodels_exog(ds) results_sm_exog_coint[ds] = load_results_statsmodels_exog_coint(ds) setup() def test_ml_gamma(): if debug_mode: if "Gamma" not in to_test: # pragma: no cover return print("\n\nGAMMA", end="") for ds in datasets: for dt in ds.dt_s_list: if debug_mode: print("\n" + dt_s_tup_to_string(dt) + ": ", end="") exog = results_sm_exog[ds][dt].exog is not None exog_coint = results_sm_exog_coint[ds][dt].exog_coint is not None # estimated parameter vector err_msg = build_err_msg(ds, dt, "Gamma") obtained = results_sm[ds][dt].gamma obtained_exog = results_sm_exog[ds][dt].gamma obtained_exog_coint = results_sm_exog_coint[ds][dt].gamma desired = results_ref[ds][dt]["est"]["Gamma"] assert_allclose(obtained, desired, rtol, atol, False, err_msg) if exog: assert_equal(obtained_exog, obtained, "WITH EXOG: " + err_msg) if exog_coint: assert_equal( obtained_exog_coint, obtained, "WITH EXOG_COINT: " + err_msg, ) if debug_mode and dont_test_se_t_p: # pragma: no cover continue # standard errors obt = results_sm[ds][dt].stderr_gamma obt_exog = results_sm_exog[ds][dt].stderr_gamma obt_exog_coint = results_sm_exog_coint[ds][dt].stderr_gamma des = results_ref[ds][dt]["se"]["Gamma"] assert_allclose( obt, des, rtol, atol, False, "STANDARD ERRORS\n" + err_msg ) if exog: assert_equal(obt_exog, obt, "WITH EXOG: " + err_msg) if exog_coint: assert_equal( obt_exog_coint, obt, "WITH EXOG_COINT: " + err_msg ) # t-values obt = results_sm[ds][dt].tvalues_gamma obt_exog = results_sm_exog[ds][dt].tvalues_gamma obt_exog_coint = results_sm_exog_coint[ds][dt].tvalues_gamma des = results_ref[ds][dt]["t"]["Gamma"] assert_allclose( obt, des, rtol, atol, False, "t-VALUES\n" + err_msg ) if exog: assert_equal(obt_exog, obt, "WITH EXOG: " + err_msg) if exog_coint: assert_equal( obt_exog_coint, obt, "WITH EXOG_COINT: " + err_msg ) # p-values obt = results_sm[ds][dt].pvalues_gamma obt_exog = results_sm_exog[ds][dt].pvalues_gamma obt_exog_coint = results_sm_exog_coint[ds][dt].pvalues_gamma des = results_ref[ds][dt]["p"]["Gamma"] assert_allclose( obt, des, rtol, atol, False, "p-VALUES\n" + err_msg ) if exog: assert_equal(obt_exog, obt, "WITH EXOG: " + err_msg) if exog_coint: assert_equal( obt_exog_coint, obt, "WITH EXOG_COINT: " + err_msg ) def test_ml_alpha(): if debug_mode: if "alpha" not in to_test: # pragma: no cover return print("\n\nALPHA", end="") for ds in datasets: for dt in ds.dt_s_list: if debug_mode: print("\n" + dt_s_tup_to_string(dt) + ": ", end="") exog = results_sm_exog[ds][dt].exog is not None exog_coint = results_sm_exog_coint[ds][dt].exog_coint is not None err_msg = build_err_msg(ds, dt, "alpha") obtained = results_sm[ds][dt].alpha obtained_exog = results_sm_exog[ds][dt].alpha obtained_exog_coint = results_sm_exog_coint[ds][dt].alpha desired = results_ref[ds][dt]["est"]["alpha"] assert_allclose(obtained, desired, rtol, atol, False, err_msg) if exog: assert_equal(obtained_exog, obtained, "WITH EXOG: " + err_msg) if exog_coint: assert_equal( obtained_exog_coint, obtained, "WITH EXOG_COINT: " + err_msg, ) if debug_mode and dont_test_se_t_p: # pragma: no cover continue # standard errors obt = results_sm[ds][dt].stderr_alpha obt_exog = results_sm_exog[ds][dt].stderr_alpha obt_exog_coint = results_sm_exog_coint[ds][dt].stderr_alpha des = results_ref[ds][dt]["se"]["alpha"] assert_allclose( obt, des, rtol, atol, False, "STANDARD ERRORS\n" + err_msg ) if exog: assert_equal(obt_exog, obt, "WITH EXOG: " + err_msg) if exog_coint: assert_equal( obt_exog_coint, obt, "WITH EXOG_COINT: " + err_msg ) # t-values obt = results_sm[ds][dt].tvalues_alpha obt_exog = results_sm_exog[ds][dt].tvalues_alpha obt_exog_coint = results_sm_exog_coint[ds][dt].tvalues_alpha des = results_ref[ds][dt]["t"]["alpha"] assert_allclose( obt, des, rtol, atol, False, "t-VALUES\n" + err_msg ) if exog: assert_equal(obt_exog, obt, "WITH EXOG: " + err_msg) if exog_coint: assert_equal( obt_exog_coint, obt, "WITH EXOG_COINT: " + err_msg ) # p-values obt = results_sm[ds][dt].pvalues_alpha obt_exog = results_sm_exog[ds][dt].pvalues_alpha obt_exog_coint = results_sm_exog_coint[ds][dt].pvalues_alpha des = results_ref[ds][dt]["p"]["alpha"] assert_allclose( obt, des, rtol, atol, False, "p-VALUES\n" + err_msg ) if exog: assert_equal(obt_exog, obt, "WITH EXOG: " + err_msg) if exog_coint: assert_equal( obt_exog_coint, obt, "WITH EXOG_COINT: " + err_msg ) def test_ml_beta(): if debug_mode: if "beta" not in to_test: # pragma: no cover return print("\n\nBETA", end="") for ds in datasets: for dt in ds.dt_s_list: if debug_mode: print("\n" + dt_s_tup_to_string(dt) + ": ", end="") exog = results_sm_exog[ds][dt].exog is not None exog_coint = results_sm_exog_coint[ds][dt].exog_coint is not None err_msg = build_err_msg(ds, dt, "beta") desired = results_ref[ds][dt]["est"]["beta"] rows = desired.shape[0] # - first coint_rank rows in JMulTi output have se=t_val=p_val=0 # - beta includes deterministic terms in cointegration relation in # sm, so we compare only the elements belonging to beta. obtained = results_sm[ds][dt].beta[coint_rank:rows] obtained_exog = results_sm_exog[ds][dt].beta[coint_rank:rows] obtained_exog_coint = results_sm_exog_coint[ds][dt].beta[ coint_rank:rows ] desired = desired[coint_rank:] assert_allclose(obtained, desired, rtol, atol, False, err_msg) if exog: assert_equal(obtained_exog, obtained, "WITH EXOG: " + err_msg) if exog_coint: assert_equal( obtained_exog_coint, obtained, "WITH EXOG_COINT: " + err_msg, ) if debug_mode and dont_test_se_t_p: # pragma: no cover continue # standard errors obt = results_sm[ds][dt].stderr_beta[coint_rank:rows] obt_exog = results_sm_exog[ds][dt].stderr_beta[coint_rank:rows] obt_exog_coint = results_sm_exog_coint[ds][dt].stderr_beta[ coint_rank:rows ] des = results_ref[ds][dt]["se"]["beta"][coint_rank:] assert_allclose( obt, des, rtol, atol, False, "STANDARD ERRORS\n" + err_msg ) if exog: assert_equal(obt_exog, obt, "WITH EXOG: " + err_msg) if exog_coint: assert_equal( obt_exog_coint, obt, "WITH EXOG_COINT: " + err_msg ) # t-values obt = results_sm[ds][dt].tvalues_beta[coint_rank:rows] obt_exog = results_sm_exog[ds][dt].tvalues_beta[coint_rank:rows] obt_exog_coint = results_sm_exog_coint[ds][dt].tvalues_beta[ coint_rank:rows ] des = results_ref[ds][dt]["t"]["beta"][coint_rank:] assert_allclose( obt, des, rtol, atol, False, "t-VALUES\n" + err_msg ) if exog: assert_equal(obt_exog, obt, "WITH EXOG: " + err_msg) if exog_coint: assert_equal( obt_exog_coint, obt, "WITH EXOG_COINT: " + err_msg ) # p-values obt = results_sm[ds][dt].pvalues_beta[coint_rank:rows] obt_exog = results_sm_exog[ds][dt].pvalues_beta[coint_rank:rows] obt_exog_coint = results_sm_exog_coint[ds][dt].pvalues_beta[ coint_rank:rows ] des = results_ref[ds][dt]["p"]["beta"][coint_rank:] assert_allclose( obt, des, rtol, atol, False, "p-VALUES\n" + err_msg ) if exog: assert_equal(obt_exog, obt, "WITH EXOG: " + err_msg) if exog_coint: assert_equal( obt_exog_coint, obt, "WITH EXOG_COINT: " + err_msg ) def test_ml_c(): # test deterministic terms outside coint relation if debug_mode: if "C" not in to_test: # pragma: no cover return print("\n\nDET_COEF", end="") for ds in datasets: for dt in ds.dt_s_list: if debug_mode: print("\n" + dt_s_tup_to_string(dt) + ": ", end="") exog = results_sm_exog[ds][dt].exog is not None exog_coint = results_sm_exog_coint[ds][dt].exog_coint is not None C_obt = results_sm[ds][dt].det_coef C_obt_exog = results_sm_exog[ds][dt].det_coef C_obt_exog_coint = results_sm_exog_coint[ds][dt].det_coef se_C_obt = results_sm[ds][dt].stderr_det_coef se_C_obt_exog = results_sm_exog[ds][dt].stderr_det_coef se_C_obt_exog_coint = results_sm_exog_coint[ds][dt].stderr_det_coef t_C_obt = results_sm[ds][dt].tvalues_det_coef t_C_obt_exog = results_sm_exog[ds][dt].tvalues_det_coef t_C_obt_exog_coint = results_sm_exog_coint[ds][dt].tvalues_det_coef p_C_obt = results_sm[ds][dt].pvalues_det_coef p_C_obt_exog = results_sm_exog[ds][dt].pvalues_det_coef p_C_obt_exog_coint = results_sm_exog_coint[ds][dt].pvalues_det_coef if "C" not in results_ref[ds][dt]["est"].keys(): # case: there are no deterministic terms if ( C_obt.size == 0 and se_C_obt.size == 0 and t_C_obt.size == 0 and p_C_obt.size == 0 ): assert_(True) continue desired = results_ref[ds][dt]["est"]["C"] dt_string = dt_s_tup_to_string(dt) if "co" in dt_string: err_msg = build_err_msg(ds, dt, "CONST") const_obt = C_obt[:, :1] const_obt_exog = C_obt_exog[:, :1] const_obt_exog_coint = C_obt_exog_coint[:, :1] const_des = desired[:, :1] C_obt = C_obt[:, 1:] C_obt_exog = C_obt_exog[:, 1:] C_obt_exog_coint = C_obt_exog_coint[:, 1:] desired = desired[:, 1:] assert_allclose( const_obt, const_des, rtol, atol, False, err_msg ) if exog: assert_equal( const_obt_exog, const_obt, "WITH EXOG: " + err_msg ) if exog_coint: assert_equal( const_obt_exog_coint, const_obt, "WITH EXOG_COINT: " + err_msg, ) if "s" in dt_string: err_msg = build_err_msg(ds, dt, "SEASONAL") if "lo" in dt_string: seas_obt = C_obt[:, :-1] seas_obt_exog = C_obt_exog[:, :-1] seas_obt_exog_coint = C_obt_exog_coint[:, :-1] seas_des = desired[:, :-1] else: seas_obt = C_obt seas_obt_exog = C_obt_exog seas_obt_exog_coint = C_obt_exog_coint seas_des = desired assert_allclose(seas_obt, seas_des, rtol, atol, False, err_msg) if exog: assert_equal( seas_obt_exog, seas_obt, "WITH EXOG: " + err_msg ) if exog_coint: assert_equal( seas_obt_exog_coint, seas_obt, "WITH EXOG_COINT: " + err_msg, ) if "lo" in dt_string: err_msg = build_err_msg(ds, dt, "LINEAR TREND") lt_obt = C_obt[:, -1:] lt_obt_exog = C_obt_exog[:, -1:] lt_obt_exog_coint = C_obt_exog_coint[:, -1:] lt_des = desired[:, -1:] assert_allclose(lt_obt, lt_des, rtol, atol, False, err_msg) if exog: assert_equal(lt_obt_exog, lt_obt, "WITH EXOG: " + err_msg) if exog_coint: assert_equal( lt_obt_exog_coint, lt_obt, "WITH EXOG_COINT: " + err_msg, ) if debug_mode and dont_test_se_t_p: # pragma: no cover continue # standard errors se_desired = results_ref[ds][dt]["se"]["C"] if "co" in dt_string: err_msg = build_err_msg(ds, dt, "SE CONST") se_const_obt = se_C_obt[:, 0][:, None] se_C_obt = se_C_obt[:, 1:] se_const_obt_exog = se_C_obt_exog[:, 0][:, None] se_C_obt_exog = se_C_obt_exog[:, 1:] se_const_obt_exog_coint = se_C_obt_exog_coint[:, 0][:, None] se_C_obt_exog_coint = se_C_obt_exog_coint[:, 1:] se_const_des = se_desired[:, 0][:, None] se_desired = se_desired[:, 1:] assert_allclose( se_const_obt, se_const_des, rtol, atol, False, err_msg ) if exog: assert_equal( se_const_obt_exog, se_const_obt, "WITH EXOG: " + err_msg, ) if exog_coint: assert_equal( se_const_obt_exog_coint, se_const_obt, "WITH EXOG_COINT: " + err_msg, ) if "s" in dt_string: err_msg = build_err_msg(ds, dt, "SE SEASONAL") if "lo" in dt_string: se_seas_obt = se_C_obt[:, :-1] se_seas_obt_exog = se_C_obt_exog[:, :-1] se_seas_obt_exog_coint = se_C_obt_exog_coint[:, :-1] se_seas_des = se_desired[:, :-1] else: se_seas_obt = se_C_obt se_seas_obt_exog = se_C_obt_exog se_seas_obt_exog_coint = se_C_obt_exog_coint se_seas_des = se_desired assert_allclose( se_seas_obt, se_seas_des, rtol, atol, False, err_msg ) if exog: assert_equal( se_seas_obt_exog, se_seas_obt, "WITH EXOG: " + err_msg ) if exog_coint: assert_equal( se_seas_obt_exog_coint, se_seas_obt, "WITH EXOG_COINT: " + err_msg, ) if "lo" in dt_string: err_msg = build_err_msg(ds, dt, "SE LIN. TREND") se_lt_obt = se_C_obt[:, -1:] se_lt_obt_exog = se_C_obt_exog[:, -1:] se_lt_obt_exog_coint = se_C_obt_exog_coint[:, -1:] se_lt_des = se_desired[:, -1:] assert_allclose( se_lt_obt, se_lt_des, rtol, atol, False, err_msg ) if exog: assert_equal( se_lt_obt_exog, se_lt_obt, "WITH EXOG: " + err_msg ) if exog_coint: assert_equal( se_lt_obt_exog_coint, se_lt_obt, "WITH EXOG_COINT: " + err_msg, ) # t-values t_desired = results_ref[ds][dt]["t"]["C"] if "co" in dt_string: t_const_obt = t_C_obt[:, 0][:, None] t_C_obt = t_C_obt[:, 1:] t_const_obt_exog = t_C_obt_exog[:, 0][:, None] t_C_obt_exog = t_C_obt_exog[:, 1:] t_const_obt_exog_coint = t_C_obt_exog_coint[:, 0][:, None] t_C_obt_exog_coint = t_C_obt_exog_coint[:, 1:] t_const_des = t_desired[:, 0][:, None] t_desired = t_desired[:, 1:] assert_allclose( t_const_obt, t_const_des, rtol, atol, False, build_err_msg(ds, dt, "T CONST"), ) if exog: assert_equal( t_const_obt_exog, t_const_obt, "WITH EXOG: " + err_msg ) if exog_coint: assert_equal( t_const_obt_exog_coint, t_const_obt, "WITH EXOG_COINT: " + err_msg, ) if "s" in dt_string: if "lo" in dt_string: t_seas_obt = t_C_obt[:, :-1] t_seas_obt_exog = t_C_obt_exog[:, :-1] t_seas_obt_exog_coint = t_C_obt_exog_coint[:, :-1] t_seas_des = t_desired[:, :-1] else: t_seas_obt = t_C_obt t_seas_obt_exog = t_C_obt_exog t_seas_obt_exog_coint = t_C_obt_exog_coint t_seas_des = t_desired assert_allclose( t_seas_obt, t_seas_des, rtol, atol, False, build_err_msg(ds, dt, "T SEASONAL"), ) if exog: assert_equal( t_seas_obt_exog, t_seas_obt, "WITH EXOG" + err_msg ) if exog_coint: assert_equal( t_seas_obt_exog_coint, t_seas_obt, "WITH EXOG_COINT" + err_msg, ) if "lo" in dt_string: t_lt_obt = t_C_obt[:, -1:] t_lt_obt_exog = t_C_obt_exog[:, -1:] t_lt_obt_exog_coint = t_C_obt_exog_coint[:, -1:] t_lt_des = t_desired[:, -1:] assert_allclose( t_lt_obt, t_lt_des, rtol, atol, False, build_err_msg(ds, dt, "T LIN. TREND"), ) if exog: assert_equal( t_lt_obt_exog, t_lt_obt, "WITH EXOG" + err_msg ) if exog_coint: assert_equal( t_lt_obt_exog_coint, t_lt_obt, "WITH EXOG_COINT" + err_msg, ) # p-values p_desired = results_ref[ds][dt]["p"]["C"] if "co" in dt_string: p_const_obt = p_C_obt[:, 0][:, None] p_C_obt = p_C_obt[:, 1:] p_const_obt_exog = p_C_obt_exog[:, 0][:, None] p_C_obt_exog = p_C_obt_exog[:, 1:] p_const_obt_exog_coint = p_C_obt_exog_coint[:, 0][:, None] p_C_obt_exo_cointg = p_C_obt_exog_coint[:, 1:] p_const_des = p_desired[:, 0][:, None] p_desired = p_desired[:, 1:] assert_allclose( p_const_obt, p_const_des, rtol, atol, False, build_err_msg(ds, dt, "P CONST"), ) if exog: assert_equal( p_const_obt, p_const_obt_exog, "WITH EXOG" + err_msg ) if exog_coint: assert_equal( p_const_obt, p_const_obt_exog_coint, "WITH EXOG_COINT" + err_msg, ) if "s" in dt_string: if "lo" in dt_string: p_seas_obt = p_C_obt[:, :-1] p_seas_obt_exog = p_C_obt_exog[:, :-1] p_seas_obt_exog_coint = p_C_obt_exog_coint[:, :-1] p_seas_des = p_desired[:, :-1] else: p_seas_obt = p_C_obt p_seas_obt_exog = p_C_obt_exog p_seas_obt_exog_coint = p_C_obt_exog_coint p_seas_des = p_desired assert_allclose( p_seas_obt, p_seas_des, rtol, atol, False, build_err_msg(ds, dt, "P SEASONAL"), ) if exog: assert_equal( p_seas_obt_exog, p_seas_obt, "WITH EXOG" + err_msg ) if exog_coint: assert_equal( p_seas_obt_exog_coint, p_seas_obt, "WITH EXOG_COINT" + err_msg, ) if "lo" in dt_string: p_lt_obt = p_C_obt[:, -1:] p_lt_obt_exog = p_C_obt_exog[:, -1:] p_lt_obt_exog_coint = p_C_obt_exog_coint[:, -1:] p_lt_des = p_desired[:, -1:] assert_allclose( p_lt_obt, p_lt_des, rtol, atol, False, build_err_msg(ds, dt, "P LIN. TREND"), ) if exog: assert_equal( p_lt_obt_exog, p_lt_obt, "WITH EXOG" + err_msg ) if exog_coint: assert_equal( p_lt_obt_exog_coint, p_lt_obt, "WITH EXOG_COINT" + err_msg, ) def test_ml_det_terms_in_coint_relation(): if debug_mode: if "det_coint" not in to_test: # pragma: no cover return print("\n\nDET_COEF_COINT", end="") for ds in datasets: for dt in ds.dt_s_list: if debug_mode: print("\n" + dt_s_tup_to_string(dt) + ": ", end="") exog = results_sm_exog[ds][dt].exog is not None exog_coint = results_sm_exog_coint[ds][dt].exog_coint is not None err_msg = build_err_msg(ds, dt, "det terms in coint relation") dt_string = dt_s_tup_to_string(dt) obtained = results_sm[ds][dt].det_coef_coint obtained_exog = results_sm_exog[ds][dt].det_coef_coint obtained_exog_coint = results_sm_exog_coint[ds][dt].det_coef_coint if "ci" not in dt_string and "li" not in dt_string: if obtained.size > 0: assert_( False, build_err_msg( ds, dt, "There should not be any det terms in " + "cointegration for deterministic terms " + dt_string, ), ) else: assert_(True) continue desired = results_ref[ds][dt]["est"]["det_coint"] assert_allclose(obtained, desired, rtol, atol, False, err_msg) if exog: assert_equal(obtained_exog, obtained, "WITH EXOG" + err_msg) if exog_coint: assert_equal( obtained_exog_coint, obtained, "WITH EXOG_COINT" + err_msg ) # standard errors se_obtained = results_sm[ds][dt].stderr_det_coef_coint se_obtained_exog = results_sm_exog[ds][dt].stderr_det_coef_coint se_obtained_exog_coint = results_sm_exog_coint[ds][ dt ].stderr_det_coef_coint se_desired = results_ref[ds][dt]["se"]["det_coint"] assert_allclose( se_obtained, se_desired, rtol, atol, False, "STANDARD ERRORS\n" + err_msg, ) if exog: assert_equal( se_obtained_exog, se_obtained, "WITH EXOG" + err_msg ) if exog_coint: assert_equal( se_obtained_exog_coint, se_obtained, "WITH EXOG_COINT" + err_msg, ) # t-values t_obtained = results_sm[ds][dt].tvalues_det_coef_coint t_obtained_exog = results_sm_exog[ds][dt].tvalues_det_coef_coint t_obtained_exog_coint = results_sm_exog_coint[ds][ dt ].tvalues_det_coef_coint t_desired = results_ref[ds][dt]["t"]["det_coint"] assert_allclose( t_obtained, t_desired, rtol, atol, False, "t-VALUES\n" + err_msg, ) if exog: assert_equal( t_obtained_exog, t_obtained, "WITH EXOG" + err_msg ) if exog_coint: assert_equal( t_obtained_exog_coint, t_obtained, "WITH EXOG_COINT" + err_msg, ) # p-values p_obtained = results_sm[ds][dt].pvalues_det_coef_coint p_obtained_exog = results_sm_exog[ds][dt].pvalues_det_coef_coint p_obtained_exog_coint = results_sm_exog_coint[ds][ dt ].pvalues_det_coef_coint p_desired = results_ref[ds][dt]["p"]["det_coint"] assert_allclose( p_obtained, p_desired, rtol, atol, False, "p-VALUES\n" + err_msg, ) if exog: assert_equal( p_obtained_exog, p_obtained, "WITH EXOG" + err_msg ) if exog_coint: assert_equal( p_obtained_exog_coint, p_obtained, "WITH EXOG_COINT" + err_msg, ) def test_ml_sigma(): if debug_mode: if "Sigma_u" not in to_test: # pragma: no cover return print("\n\nSIGMA_U", end="") for ds in datasets: for dt in ds.dt_s_list: if debug_mode: print("\n" + dt_s_tup_to_string(dt) + ": ", end="") exog = results_sm_exog[ds][dt].exog is not None exog_coint = results_sm_exog_coint[ds][dt].exog_coint is not None err_msg = build_err_msg(ds, dt, "Sigma_u") obtained = results_sm[ds][dt].sigma_u obtained_exog = results_sm_exog[ds][dt].sigma_u obtained_exog_coint = results_sm_exog_coint[ds][dt].sigma_u desired = results_ref[ds][dt]["est"]["Sigma_u"] assert_allclose(obtained, desired, rtol, atol, False, err_msg) if exog: assert_equal(obtained_exog, obtained, "WITH EXOG" + err_msg) if exog_coint: assert_equal( obtained_exog_coint, obtained, "WITH EXOG_COINT" + err_msg ) def test_var_rep(): if debug_mode: if "VAR repr. A" not in to_test: # pragma: no cover return print("\n\nVAR REPRESENTATION", end="") for ds in datasets: for dt in ds.dt_s_list: if debug_mode: print("\n" + dt_s_tup_to_string(dt) + ": ", end="") exog = results_sm_exog[ds][dt].exog is not None exog_coint = results_sm_exog_coint[ds][dt].exog_coint is not None err_msg = build_err_msg(ds, dt, "VAR repr. A") obtained = results_sm[ds][dt].var_rep obtained_exog = results_sm_exog[ds][dt].var_rep obtained_exog_coint = results_sm_exog_coint[ds][dt].var_rep p = obtained.shape[0] desired = np.hsplit(results_ref[ds][dt]["est"]["VAR A"], p) assert_allclose(obtained, desired, rtol, atol, False, err_msg) if exog: assert_equal(obtained_exog, obtained, "WITH EXOG" + err_msg) if exog_coint: assert_equal( obtained_exog_coint, obtained, "WITH EXOG_COINT" + err_msg ) def test_var_to_vecm(): if debug_mode: if "VAR to VEC representation" not in to_test: # pragma: no cover return print("\n\nVAR TO VEC", end="") for ds in datasets: for dt in ds.dt_s_list: if debug_mode: print("\n" + dt_s_tup_to_string(dt) + ": ", end="") err_msg = build_err_msg(ds, dt, "VAR to VEC representation") sigma_u = results_sm[ds][dt].sigma_u coefs = results_sm[ds][dt].var_rep intercept = np.zeros(len(sigma_u)) # Note: _params_info k_trend, k_exog, ... is inferred with defaults var = VARProcess(coefs, intercept, sigma_u) vecm_results = var.to_vecm() obtained_pi = vecm_results["Pi"] obtained_gamma = vecm_results["Gamma"] desired_pi = np.dot( results_sm[ds][dt].alpha, results_sm[ds][dt].beta.T ) desired_gamma = results_sm[ds][dt].gamma assert_allclose( obtained_pi, desired_pi, rtol, atol, False, err_msg + " Pi" ) assert_allclose( obtained_gamma, desired_gamma, rtol, atol, False, err_msg + " Gamma", ) # Commented out since JMulTi shows the same det. terms for both VEC & VAR repr. # def test_var_rep_det(): # for ds in datasets: # for dt in dt_s_list: # if dt != "n": # err_msg = build_err_msg(ds, dt, "VAR repr. deterministic") # obtained = 0 # not implemented since the same values as VECM # desired = results_ref[ds][dt]["est"]["VAR deterministic"] # assert_allclose(obtained, desired, rtol, atol, False, err_msg) def test_log_like(): if debug_mode: if "log_like" not in to_test: # pragma: no cover return else: print("\n\nLOG LIKELIHOOD", end="") for ds in datasets: for dt in ds.dt_s_list: if debug_mode: print("\n" + dt_s_tup_to_string(dt) + ": ", end="") exog = results_sm_exog[ds][dt].exog is not None exog_coint = results_sm_exog_coint[ds][dt].exog_coint is not None err_msg = build_err_msg(ds, dt, "Log Likelihood") obtained = results_sm[ds][dt].llf obtained_exog = results_sm_exog[ds][dt].llf obtained_exog_coint = results_sm_exog_coint[ds][dt].llf # JMulTi's llf seems to have a bug (Stata and tsdyn suggest that # our code is correct). We use nobs to correct this inconsistency. nobs = results_sm[ds][dt].nobs desired = results_ref[ds][dt]["log_like"] * nobs / (nobs - 1) assert_allclose(obtained, desired, rtol, atol, False, err_msg) if exog: assert_equal(obtained_exog, obtained, "WITH EXOG" + err_msg) if exog_coint: assert_equal( obtained_exog_coint, obtained, "WITH EXOG_COINT" + err_msg ) def test_fc(): if debug_mode: if "fc" not in to_test: # pragma: no cover return else: print("\n\nFORECAST", end="") for ds in datasets: for dt in ds.dt_s_list: if debug_mode: print("\n" + dt_s_tup_to_string(dt) + ": ", end="") STEPS = 5 ALPHA = 0.05 err_msg = build_err_msg(ds, dt, "FORECAST") # test point forecast functionality of predict method obtained = results_sm[ds][dt].predict(steps=STEPS) desired = results_ref[ds][dt]["fc"]["fc"] assert_allclose(obtained, desired, rtol, atol, False, err_msg) # ----------------------------------------------------------------- # with exog: exog = results_sm_exog[ds][dt].exog is not None exog_fc = None if exog: # build future values of exog and test: seasons = dt[1] exog_model = results_sm_exog[ds][dt].exog exog_seasons_fc = exog_model[-seasons:, : seasons - 1] exog_seasons_fc = np.pad( exog_seasons_fc, ((0, STEPS - exog_seasons_fc.shape[0]), (0, 0)), "wrap", ) # if linear trend in exog if exog_seasons_fc.shape[1] + 1 == exog_model.shape[1]: exog_lt_fc = exog_model[-1, -1] + 1 + np.arange(STEPS) exog_fc = np.column_stack((exog_seasons_fc, exog_lt_fc)) else: exog_fc = exog_seasons_fc obtained_exog = results_sm_exog[ds][dt].predict( steps=STEPS, exog_fc=exog_fc ) assert_allclose( obtained_exog, obtained, 1e-07, 0, False, "WITH EXOG" + err_msg, ) # test predict method with confidence interval calculation err_msg = build_err_msg(ds, dt, "FORECAST WITH INTERVALS") obtained_w_intervals = results_sm[ds][dt].predict( steps=STEPS, alpha=ALPHA ) obtained_w_intervals_exog = results_sm_exog[ds][dt].predict( steps=STEPS, alpha=ALPHA, exog_fc=exog_fc ) obt = obtained_w_intervals[0] # forecast obt_l = obtained_w_intervals[1] # lower bound obt_u = obtained_w_intervals[2] # upper bound obt_exog = obtained_w_intervals_exog[0] obt_exog_l = obtained_w_intervals_exog[1] obt_exog_u = obtained_w_intervals_exog[2] des = results_ref[ds][dt]["fc"]["fc"] des_l = results_ref[ds][dt]["fc"]["lower"] des_u = results_ref[ds][dt]["fc"]["upper"] assert_allclose(obt, des, rtol, atol, False, err_msg) assert_allclose(obt_l, des_l, rtol, atol, False, err_msg) assert_allclose(obt_u, des_u, rtol, atol, False, err_msg) if exog: assert_allclose( obt_exog, obt, 1e-07, 0, False, "WITH EXOG" + err_msg ) assert_allclose( obt_exog_l, obt_l, 1e-07, 0, False, "WITH EXOG" + err_msg ) assert_allclose( obt_exog_u, obt_u, 1e-07, 0, False, "WITH EXOG" + err_msg ) # ----------------------------------------------------------------- # with exog_coint: exog_coint_model = results_sm_exog_coint[ds][dt].exog_coint exog_coint = exog_coint_model is not None exog_coint_fc = None if exog_coint: # build future values of exog_coint and test: # const is in exog_coint in all tests that have exog_coint exog_coint_fc = np.ones(STEPS - 1) # if linear trend in exog_coint if exog_coint_model.shape[1] == 2: exog_coint_fc = np.column_stack( ( exog_coint_fc, exog_coint_model[-1, -1] + 1 + np.arange(STEPS - 1), ) ) obtained_exog_coint = results_sm_exog_coint[ds][dt].predict( steps=STEPS, exog_coint_fc=exog_coint_fc ) assert_allclose( obtained_exog_coint, obtained, 1e-07, 0, False, "WITH EXOG_COINT" + err_msg, ) # test predict method with confidence interval calculation err_msg = build_err_msg(ds, dt, "FORECAST WITH INTERVALS") obtained_w_intervals = results_sm[ds][dt].predict( steps=STEPS, alpha=ALPHA ) obtained_w_intervals_exog_coint = results_sm_exog_coint[ds][ dt ].predict(steps=STEPS, alpha=ALPHA, exog_coint_fc=exog_coint_fc) obt = obtained_w_intervals[0] # forecast obt_l = obtained_w_intervals[1] # lower bound obt_u = obtained_w_intervals[2] # upper bound obt_exog_coint = obtained_w_intervals_exog_coint[0] obt_exog_coint_l = obtained_w_intervals_exog_coint[1] obt_exog_coint_u = obtained_w_intervals_exog_coint[2] des = results_ref[ds][dt]["fc"]["fc"] des_l = results_ref[ds][dt]["fc"]["lower"] des_u = results_ref[ds][dt]["fc"]["upper"] assert_allclose(obt, des, rtol, atol, False, err_msg) assert_allclose(obt_l, des_l, rtol, atol, False, err_msg) assert_allclose(obt_u, des_u, rtol, atol, False, err_msg) if exog_coint: assert_allclose( obt_exog_coint, obt, 1e-07, 0, False, "WITH EXOG_COINT" + err_msg, ) assert_allclose( obt_exog_coint_l, obt_l, 1e-07, 0, False, "WITH EXOG_COINT" + err_msg, ) assert_allclose( obt_exog_coint_u, obt_u, 1e-07, 0, False, "WITH EXOG_COINT" + err_msg, ) def test_granger_causality(): if debug_mode: if "granger" not in to_test: # pragma: no cover return else: print("\n\nGRANGER", end="") for ds in datasets: for dt in ds.dt_s_list: if debug_mode: print("\n" + dt_s_tup_to_string(dt) + ": ", end="") exog = results_sm_exog[ds][dt].exog is not None exog_coint = results_sm_exog_coint[ds][dt].exog_coint is not None err_msg_g_p = build_err_msg(ds, dt, "GRANGER CAUS. - p-VALUE") err_msg_g_t = build_err_msg(ds, dt, "GRANGER CAUS. - TEST STAT.") v_ind = range(len(ds.variable_names)) for causing_ind in sublists(v_ind, 1, len(v_ind) - 1): causing_names = ["y" + str(i + 1) for i in causing_ind] causing_key = tuple(ds.variable_names[i] for i in causing_ind) caused_ind = [i for i in v_ind if i not in causing_ind] caused_names = ["y" + str(i + 1) for i in caused_ind] caused_key = tuple(ds.variable_names[i] for i in caused_ind) granger_sm_ind = results_sm[ds][dt].test_granger_causality( caused_ind, causing_ind ) granger_sm_ind_exog = results_sm_exog[ds][ dt ].test_granger_causality(caused_ind, causing_ind) granger_sm_ind_exog_coint = results_sm_exog_coint[ds][ dt ].test_granger_causality(caused_ind, causing_ind) granger_sm_str = results_sm[ds][dt].test_granger_causality( caused_names, causing_names ) # call methods to assure they do not raise exceptions granger_sm_ind.summary() str(granger_sm_ind) # __str__() assert_(granger_sm_ind == granger_sm_str) # __eq__() # test test-statistic for Granger non-causality: g_t_obt = granger_sm_ind.test_statistic g_t_obt_exog = granger_sm_ind_exog.test_statistic g_t_obt_exog_coint = granger_sm_ind_exog_coint.test_statistic g_t_des = results_ref[ds][dt]["granger_caus"]["test_stat"][ (causing_key, caused_key) ] assert_allclose( g_t_obt, g_t_des, rtol, atol, False, err_msg_g_t ) if exog: assert_allclose( g_t_obt_exog, g_t_obt, 1e-07, 0, False, "WITH EXOG" + err_msg_g_t, ) if exog_coint: assert_allclose( g_t_obt_exog_coint, g_t_obt, 1e-07, 0, False, "WITH EXOG_COINT" + err_msg_g_t, ) # check whether string sequences as args work in the same way: g_t_obt_str = granger_sm_str.test_statistic assert_allclose( g_t_obt_str, g_t_obt, 1e-07, 0, False, err_msg_g_t + " - sequences of integers and ".upper() + "strings as arguments do not yield the same result!".upper(), ) # check if int (e.g. 0) as index and list of int ([0]) yield # the same result: if len(causing_ind) == 1 or len(caused_ind) == 1: ci = ( causing_ind[0] if len(causing_ind) == 1 else causing_ind ) ce = caused_ind[0] if len(caused_ind) == 1 else caused_ind granger_sm_single_ind = results_sm[ds][ dt ].test_granger_causality(ce, ci) g_t_obt_single = granger_sm_single_ind.test_statistic assert_allclose( g_t_obt_single, g_t_obt, 1e-07, 0, False, err_msg_g_t + " - list of int and int as ".upper() + "argument do not yield the same result!".upper(), ) # test p-value for Granger non-causality: g_p_obt = granger_sm_ind.pvalue g_p_des = results_ref[ds][dt]["granger_caus"]["p"][ (causing_key, caused_key) ] assert_allclose( g_p_obt, g_p_des, rtol, atol, False, err_msg_g_p ) # check whether string sequences as args work in the same way: g_p_obt_str = granger_sm_str.pvalue assert_allclose( g_p_obt_str, g_p_obt, 1e-07, 0, False, err_msg_g_t + " - sequences of integers and ".upper() + "strings as arguments do not yield the same result!".upper(), ) # check if int (e.g. 0) as index and list of int ([0]) yield # the same result: if len(causing_ind) == 1: g_p_obt_single = granger_sm_single_ind.pvalue assert_allclose( g_p_obt_single, g_p_obt, 1e-07, 0, False, err_msg_g_t + " - list of int and int as ".upper() + "argument do not yield the same result!".upper(), ) def test_inst_causality(): # test instantaneous causality if debug_mode: if "inst. causality" not in to_test: # pragma: no cover return else: print("\n\nINST. CAUSALITY", end="") for ds in datasets: for dt in ds.dt_s_list: if debug_mode: print("\n" + dt_s_tup_to_string(dt) + ": ", end="") exog = results_sm_exog[ds][dt].exog is not None exog_coint = results_sm_exog_coint[ds][dt].exog_coint is not None err_msg_i_p = build_err_msg(ds, dt, "INSTANT. CAUS. - p-VALUE") err_msg_i_t = build_err_msg(ds, dt, "INSTANT. CAUS. - TEST STAT.") v_ind = range(len(ds.variable_names)) for causing_ind in sublists(v_ind, 1, len(v_ind) - 1): causing_names = ["y" + str(i + 1) for i in causing_ind] causing_key = tuple(ds.variable_names[i] for i in causing_ind) caused_ind = [i for i in v_ind if i not in causing_ind] caused_key = tuple(ds.variable_names[i] for i in caused_ind) inst_sm_ind = results_sm[ds][dt].test_inst_causality( causing_ind ) inst_sm_ind_exog = results_sm_exog[ds][dt].test_inst_causality( causing_ind ) inst_sm_ind_exog_coint = results_sm_exog_coint[ds][ dt ].test_inst_causality(causing_ind) inst_sm_str = results_sm[ds][dt].test_inst_causality( causing_names ) # call methods to assure they do not raise exceptions inst_sm_ind.summary() str(inst_sm_ind) # __str__() assert_(inst_sm_ind == inst_sm_str) # __eq__() # test test-statistic for instantaneous non-causality t_obt = inst_sm_ind.test_statistic t_obt_exog = inst_sm_ind_exog.test_statistic t_obt_exog_coint = inst_sm_ind_exog_coint.test_statistic t_des = results_ref[ds][dt]["inst_caus"]["test_stat"][ (causing_key, caused_key) ] assert_allclose(t_obt, t_des, rtol, atol, False, err_msg_i_t) if exog: assert_allclose( t_obt_exog, t_obt, 1e-07, 0, False, "WITH EXOG" + err_msg_i_t, ) if exog_coint: assert_allclose( t_obt_exog_coint, t_obt, 1e-07, 0, False, "WITH EXOG_COINT" + err_msg_i_t, ) # check whether string sequences as args work in the same way: t_obt_str = inst_sm_str.test_statistic assert_allclose( t_obt_str, t_obt, 1e-07, 0, False, err_msg_i_t + " - sequences of integers and ".upper() + "strings as arguments do not yield the same result!".upper(), ) # check if int (e.g. 0) as index and list of int ([0]) yield # the same result: if len(causing_ind) == 1: inst_sm_single_ind = results_sm[ds][ dt ].test_inst_causality(causing_ind[0]) t_obt_single = inst_sm_single_ind.test_statistic assert_allclose( t_obt_single, t_obt, 1e-07, 0, False, err_msg_i_t + " - list of int and int as ".upper() + "argument do not yield the same result!".upper(), ) # test p-value for instantaneous non-causality p_obt = ( results_sm[ds][dt].test_inst_causality(causing_ind).pvalue ) p_des = results_ref[ds][dt]["inst_caus"]["p"][ (causing_key, caused_key) ] assert_allclose(p_obt, p_des, rtol, atol, False, err_msg_i_p) # check whether string sequences as args work in the same way: p_obt_str = inst_sm_str.pvalue assert_allclose( p_obt_str, p_obt, 1e-07, 0, False, err_msg_i_p + " - sequences of integers and ".upper() + "strings as arguments do not yield the same result!".upper(), ) # check if int (e.g. 0) as index and list of int ([0]) yield # the same result: if len(causing_ind) == 1: inst_sm_single_ind = results_sm[ds][ dt ].test_inst_causality(causing_ind[0]) p_obt_single = inst_sm_single_ind.pvalue assert_allclose( p_obt_single, p_obt, 1e-07, 0, False, err_msg_i_p + " - list of int and int as ".upper() + "argument do not yield the same result!".upper(), ) def test_impulse_response(): if debug_mode: if "impulse-response" not in to_test: # pragma: no cover return else: print("\n\nIMPULSE-RESPONSE", end="") for ds in datasets: for dt in ds.dt_s_list: if debug_mode: print("\n" + dt_s_tup_to_string(dt) + ": ", end="") exog = results_sm_exog[ds][dt].exog is not None exog_coint = results_sm_exog_coint[ds][dt].exog_coint is not None err_msg = build_err_msg(ds, dt, "IMULSE-RESPONSE") periods = 20 obtained_all = results_sm[ds][dt].irf(periods=periods).irfs obtained_all_exog = ( results_sm_exog[ds][dt].irf(periods=periods).irfs ) obtained_all_exog_coint = ( results_sm_exog_coint[ds][dt].irf(periods=periods).irfs ) # flatten inner arrays to make them comparable to parsed results: obtained_all = obtained_all.reshape(periods + 1, -1) obtained_all_exog = obtained_all_exog.reshape(periods + 1, -1) obtained_all_exog_coint = obtained_all_exog_coint.reshape( periods + 1, -1 ) desired_all = results_ref[ds][dt]["ir"] assert_allclose( obtained_all, desired_all, rtol, atol, False, err_msg ) if exog: assert_equal( obtained_all_exog, obtained_all, "WITH EXOG" + err_msg ) if exog_coint: assert_equal( obtained_all_exog_coint, obtained_all, "WITH EXOG_COINT" + err_msg, ) def test_lag_order_selection(): if debug_mode: if "lag order" not in to_test: # pragma: no cover return else: print("\n\nLAG ORDER SELECTION", end="") for ds in datasets: for dt in ds.dt_s_list: if debug_mode: print("\n" + dt_s_tup_to_string(dt) + ": ", end="") deterministic = dt[0] endog_tot = data[ds] trend = "n" if dt[0] == "nc" else dt[0] obtained_all = select_order( endog_tot, 10, deterministic=dt[0], seasons=dt[1] ) deterministic_outside_exog = "" # "co" is not in exog in any test case if "co" in deterministic: deterministic_outside_exog += "co" # "lo" is is in exog only in test cases with seasons>0 if "lo" in deterministic and dt[1] == 0: deterministic_outside_exog += "lo" exog_model = results_sm_exog[ds][dt].exog exog = exog_model is not None exog_coint_model = results_sm_exog_coint[ds][dt].exog_coint exog_coint = exog_coint_model is not None obtained_all_exog = select_order( endog_tot, 10, deterministic_outside_exog, seasons=0, exog=exog_model, ) # "ci" and "li" are always in exog_coint, so pass "n" as det. term obtained_all_exog_coint = select_order( endog_tot, 10, "n", seasons=dt[1], exog_coint=exog_coint_model ) for ic in ["aic", "fpe", "hqic", "bic"]: err_msg = build_err_msg( ds, dt, "LAG ORDER SELECTION - " + ic.upper() ) obtained = getattr(obtained_all, ic) desired = results_ref[ds][dt]["lagorder"][ic] assert_allclose(obtained, desired, rtol, atol, False, err_msg) if exog: assert_equal( getattr(obtained_all_exog, ic), getattr(obtained_all, ic), "WITH EXOG" + err_msg, ) if exog_coint: assert_equal( getattr(obtained_all_exog_coint, ic), getattr(obtained_all, ic), "WITH EXOG_COINT" + err_msg, ) # call methods to assure they do not raise exceptions obtained_all.summary() str(obtained_all) # __str__() def test_normality(): if debug_mode: if "test_norm" not in to_test: # pragma: no cover return else: print("\n\nTEST NON-NORMALITY", end="") for ds in datasets: for dt in ds.dt_s_list: if debug_mode: print("\n" + dt_s_tup_to_string(dt) + ": ", end="") exog = results_sm_exog[ds][dt].exog is not None exog_coint = results_sm_exog_coint[ds][dt].exog_coint is not None obtained = results_sm[ds][dt].test_normality(signif=0.05) obtained_exog = results_sm_exog[ds][dt].test_normality(signif=0.05) obtained_exog_coint = results_sm_exog_coint[ds][dt].test_normality( signif=0.05 ) err_msg = build_err_msg(ds, dt, "TEST NON-NORMALITY - STATISTIC") obt_statistic = obtained.test_statistic obt_statistic_exog = obtained_exog.test_statistic obt_statistic_exog_coint = obtained_exog_coint.test_statistic des_statistic = results_ref[ds][dt]["test_norm"][ "joint_test_statistic" ] assert_allclose( obt_statistic, des_statistic, rtol, atol, False, err_msg ) if exog: assert_equal( obt_statistic_exog, obt_statistic, "WITH EXOG" + err_msg ) if exog_coint: assert_equal( obt_statistic_exog_coint, obt_statistic, "WITH EXOG_COINT" + err_msg, ) err_msg = build_err_msg(ds, dt, "TEST NON-NORMALITY - P-VALUE") obt_pvalue = obtained.pvalue des_pvalue = results_ref[ds][dt]["test_norm"]["joint_pvalue"] assert_allclose(obt_pvalue, des_pvalue, rtol, atol, False, err_msg) # call methods to assure they do not raise exceptions obtained.summary() str(obtained) # __str__() assert_(obtained == obtained_exog) # __eq__() def test_whiteness(): if debug_mode: if "whiteness" not in to_test: # pragma: no cover return else: print("\n\nTEST WHITENESS OF RESIDUALS", end="") for ds in datasets: for dt in ds.dt_s_list: if debug_mode: print("\n" + dt_s_tup_to_string(dt) + ": ", end="") exog = results_sm_exog[ds][dt].exog is not None exog_coint = results_sm_exog_coint[ds][dt].exog_coint is not None lags = results_ref[ds][dt]["whiteness"]["tested order"] obtained = results_sm[ds][dt].test_whiteness(nlags=lags) obtained_exog = results_sm_exog[ds][dt].test_whiteness(nlags=lags) obtained_exog_coint = results_sm_exog_coint[ds][dt].test_whiteness( nlags=lags ) # test statistic err_msg = build_err_msg( ds, dt, "WHITENESS OF RESIDUALS - " "TEST STATISTIC" ) desired = results_ref[ds][dt]["whiteness"]["test statistic"] assert_allclose( obtained.test_statistic, desired, rtol, atol, False, err_msg ) if exog: assert_equal( obtained_exog.test_statistic, obtained.test_statistic, "WITH EXOG" + err_msg, ) if exog_coint: assert_equal( obtained_exog_coint.test_statistic, obtained.test_statistic, "WITH EXOG_COINT" + err_msg, ) # p-value err_msg = build_err_msg( ds, dt, "WHITENESS OF RESIDUALS - " "P-VALUE" ) desired = results_ref[ds][dt]["whiteness"]["p-value"] assert_allclose( obtained.pvalue, desired, rtol, atol, False, err_msg ) obtained = results_sm[ds][dt].test_whiteness( nlags=lags, adjusted=True ) obtained_exog = results_sm_exog[ds][dt].test_whiteness( nlags=lags, adjusted=True ) obtained_exog_coint = results_sm_exog_coint[ds][dt].test_whiteness( nlags=lags, adjusted=True ) # test statistic (adjusted Portmanteau test) err_msg = build_err_msg( ds, dt, "WHITENESS OF RESIDUALS - " "TEST STATISTIC (ADJUSTED TEST)", ) desired = results_ref[ds][dt]["whiteness"]["test statistic adj."] assert_allclose( obtained.test_statistic, desired, rtol, atol, False, err_msg ) if exog: assert_equal( obtained_exog.test_statistic, obtained.test_statistic, "WITH EXOG" + err_msg, ) if exog_coint: assert_equal( obtained_exog_coint.test_statistic, obtained.test_statistic, "WITH EXOG_COINT" + err_msg, ) # p-value (adjusted Portmanteau test) err_msg = build_err_msg( ds, dt, "WHITENESS OF RESIDUALS - " "P-VALUE (ADJUSTED TEST)" ) desired = results_ref[ds][dt]["whiteness"]["p-value adjusted"] assert_allclose( obtained.pvalue, desired, rtol, atol, False, err_msg ) # call methods to assure they do not raise exceptions obtained.summary() str(obtained) # __str__() assert_(obtained == obtained_exog) # __eq__() def test_summary(): if debug_mode: if "summary" not in to_test: # pragma: no cover return else: print("\n\nSUMMARY", end="") for ds in datasets: for dt in ds.dt_s_list: if debug_mode: print("\n" + dt_s_tup_to_string(dt) + ": ", end="") # see if summary gets printed results_sm[ds][dt].summary(alpha=0.05) exog = results_sm_exog[ds][dt].exog is not None if exog is not None: results_sm_exog[ds][dt].summary(alpha=0.05) exog_coint = results_sm_exog_coint[ds][dt].exog_coint is not None if exog_coint is not None: results_sm_exog_coint[ds][dt].summary(alpha=0.05) def test_exceptions(): if debug_mode: if "exceptions" not in to_test: # pragma: no cover return else: print("\n\nEXCEPTIONS\n", end="") ds = datasets[0] dt = ds.dt_s_list[0] endog = data[datasets[0]] # select_coint_rank: # method argument cannot be "my_method" assert_raises( ValueError, select_coint_rank, endog, 0, 3, "my_method", 0.05 ) # det_order has to be -1, 0, or 1: assert_raises(ValueError, select_coint_rank, endog, 2, 3) assert_raises(ValueError, select_coint_rank, endog, 0.5, 3) # 0.025 is not possible (must be 0.1, 0.05, or 0.01) assert_raises(ValueError, select_coint_rank, endog, 0, 3, "trace", 0.025) # Granger_causality: # ### 0 0: assert_equal(r_1[0], r_1[1]) for i in range(rank): assert_(test_stats[i] > crit_vals[i]) if rank < neqs: assert_(test_stats[rank] < crit_vals[rank]) maxeig_result = select_coint_rank(endog, 0, 3, method="maxeig", signif=0.1) rank = maxeig_result.rank r_1 = maxeig_result.r_1 test_stats = maxeig_result.test_stats crit_vals = maxeig_result.crit_vals if maxeig_result.rank > 0: assert_equal(r_1[0], r_1[1] - 1) for i in range(rank): assert_(test_stats[i] > crit_vals[i]) if rank < neqs: assert_(test_stats[rank] < crit_vals[rank]) def test_VECM_seasonal_forecast(): # timing of seasonal dummies, VAR forecast horizon np.random.seed(964255) nobs = 200 seasons = 6 fact = np.cumsum(0.1 + np.random.randn(nobs, 2), 0) xx = np.random.randn(nobs + 2, 3) xx = xx[2:] + 0.6 * xx[1:-1] + 0.25 * xx[:-2] xx[:, :2] += fact[:, 0][:, None] xx[:, 2:] += fact[:, 1][:, None] # add large seasonal effect xx += 3 * np.log(0.1 + (np.arange(nobs)[:, None] % seasons)) res0 = VECM( xx, k_ar_diff=0, coint_rank=2, deterministic="co", seasons=seasons, first_season=0, ).fit() res2 = VECM( xx, k_ar_diff=2, coint_rank=2, deterministic="co", seasons=seasons, first_season=0, ).fit() res4 = VECM( xx, k_ar_diff=4, coint_rank=2, deterministic="co", seasons=seasons, first_season=0, ).fit() # check that seasonal dummy are independent of number of lags assert_allclose( res2._delta_x.T[-2 * seasons :, -seasons:], res0._delta_x.T[-2 * seasons :, -seasons:], rtol=1e-10, ) assert_allclose( res4._delta_x.T[-2 * seasons :, -seasons:], res0._delta_x.T[-2 * seasons :, -seasons:], rtol=1e-10, ) # check location of smallest seasonal coefficient assert_array_equal(np.argmin(res0.det_coef, axis=1), [1, 1, 1]) assert_array_equal(np.argmin(res2.det_coef, axis=1), [1, 1, 1]) assert_array_equal(np.argmin(res4.det_coef, axis=1), [1, 1, 1]) # predict 3 cycles, check location of dips dips_true = np.array([[4, 4, 4], [10, 10, 10], [16, 16, 16]]) for res in [res0, res2, res4]: forecast = res.predict(steps=3 * seasons) dips = np.sort(np.argsort(forecast, axis=0)[:3], axis=0) assert_array_equal(dips, dips_true) # res2.plot_forecast(steps=18, alpha=0.1, n_last_obs=4*seasons)