""" Results for ARMA models. Produced by gretl. """ import os from numpy import genfromtxt current_path = os.path.dirname(os.path.abspath(__file__)) with open(current_path+"/yhat_exact_nc.csv", "rb") as fd: yhat_mle = genfromtxt(fd, delimiter=",", skip_header=1, dtype=float) with open(current_path+"/yhat_css_nc.csv", "rb") as fd: yhat_css = genfromtxt(fd, delimiter=",", skip_header=1, dtype=float) with open(current_path+"/yhat_exact_c.csv", "rb") as fd: yhatc_mle = genfromtxt(fd, delimiter=",", skip_header=1, dtype=float) with open(current_path+"/yhat_css_c.csv", "rb") as fd: yhatc_css = genfromtxt(fd, delimiter=",", skip_header=1, dtype=float) with open(current_path+"/resids_exact_nc.csv", "rb") as fd: resids_mle = genfromtxt(fd, delimiter=",", skip_header=1, dtype=float) with open(current_path+"/resids_css_nc.csv", "rb") as fd: resids_css = genfromtxt(fd, delimiter=",", skip_header=1, dtype=float) with open(current_path+"/resids_exact_c.csv", "rb") as fd: residsc_mle = genfromtxt(fd, delimiter=",", skip_header=1, dtype=float) with open(current_path+"/resids_css_c.csv", "rb") as fd: residsc_css = genfromtxt(fd, delimiter=",", skip_header=1, dtype=float) with open(current_path+"/results_arma_forecasts.csv", "rb") as fd: forecast_results = genfromtxt(fd, names=True, delimiter=",", dtype=float) class Y_arma11: def __init__(self, method="mle"): if method == "mle": self.params = [0.788452102751, 0.381793815167] self.aic = 714.489820273473 self.bic = 725.054203027060 self.arroots = 1.2683 + 0j self.maroots = -2.6192 + 0j self.bse = [0.042075906061, 0.060925105865] self.hqic = 718.741675179309 self.llf = -354.244910136737 self.resid = resids_mle[:, 0] self.fittedvalues = yhat_mle[:, 0] self.pvalues = [2.39e-78, 3.69e-10] self.tvalues = [18.74, 6.267] self.sigma2 = 0.994743350844 ** 2 self.cov_params = [[0.0017704, -0.0010612], [-0.0010612, 0.0037119]] self.forecast = forecast_results['fc11'] self.forecasterr = forecast_results['fe11'] elif method == "css": self.params = [0.791515576984, 0.383078056824] self.aic = 710.994047176570 self.bic = 721.546405865964 self.arroots = [1.2634 + 0.0000j] self.maroots = [-2.6104 + 0.0000j] # NOTE: bse, cov_params, tvalues taken from R; commented-out # versions below are from [TODO: finish this sentence] # self.bse = [0.042369318062, 0.065703859674] self.bse = [0.0424015620491, 0.0608752234378] # self.cov_params = [ # [0.0017952, -0.0010996], # [-0.0010996, 0.0043170]] self.cov_params = [ [0.00179789246421, -0.00106195321540], [-0.00106195321540, 0.00370579282860]] self.hqic = 715.241545108550 self.llf = -352.497023588285 self.resid = resids_css[1:, 0] self.fittedvalues = yhat_css[1:, 0] self.pvalues = [7.02e-78, 5.53e-09] # self.tvalues = [18.68, 5.830] self.tvalues = [18.6671317239, 6.2928857557] self.sigma2 = 0.996717562780**2 class Y_arma14: def __init__(self, method="mle"): if method == "mle": self.params = [0.763798613302, 0.306453049063, -0.835653786888, 0.151382611965, 0.421169903784] self.aic = 736.001094752429 self.bic = 757.129860259603 self.arroots = 1.3092 + 0j self.maroots = [1.0392 - 0.7070j, 1.0392 + 0.7070j, -1.2189 - 0.1310j, -1.2189 + 0.1310j] self.bse = [0.064888368113, 0.078031359430, 0.076246826219, 0.069267771804, 0.071567389557] self.cov_params = [ [0.0042105, -0.0031074, -0.0027947, -0.00027766, -0.00037373], [-0.0031074, 0.0060889, 0.0033958, -0.0026825, -0.00062289], [-0.0027947, 0.0033958, 0.0058136, -0.00063747, -0.0028984], [-0.00027766, -0.0026825, -0.00063747, 0.0047980, 0.0026998], [-0.00037373, -0.00062289, -0.0028984, 0.0026998, 0.0051219]] self.hqic = 744.504804564101 self.llf = -362.000547376215 self.resid = resids_mle[:, 1] self.fittedvalues = yhat_mle[:, 1] self.pvalues = [5.51e-32, 8.59e-05, 5.96e-28, 0.0289, 3.98e-09] self.tvalues = [11.77, 3.927, -10.96, 2.185, 5.885] self.sigma2 = 1.022607088673 ** 2 self.bse = [0.064888368113, 0.078031359430, 0.076246826219, 0.069267771804, 0.071567389557] elif method == "css": self.params = [0.772072791055, 0.283961556581, -0.834797380642, 0.157773469382, 0.431616426021] self.aic = 734.294057687460 self.bic = 755.398775066249 self.arroots = [1.2952 + 0.0000j] self.maroots = [1.0280 - 0.6987j, 1.0280 + 0.6987j, -1.2108 - 0.1835j, -1.2108 + 0.1835j] # NOTE: bse, cov_params, tvalues taken from R; commented-out # versions below are from [TODO: finish this sentence] # self.bse = [0.083423762397, 0.086852297123, 0.093883465705, # 0.068170451942, 0.065938183073] self.bse = [0.06106330, 0.07381130, 0.07257705, 0.06857992, 0.07046048] # self.cov_params = [ # [0.0069595, -0.0053083, -0.0054522, -0.0016324, -0.00099984], # [-0.0053083, 0.0075433, 0.0052442, -0.00071680, 0.0010335], # [-0.0054522, 0.0052442, 0.0088141, 0.0019754, -0.0018231], # [-0.0016324, -0.00071680, 0.0019754, 0.0046472, 0.0011853], # [-0.00099984, 0.0010335, -0.0018231, 0.0011853, 0.0043478]] self.cov_params = [ [0.0037287270, -0.0025337305, -0.0023475489, -0.0001894180, -0.0002716368], [-0.0025337305, 0.0054481087, 0.0029356374, -0.0027307668, -0.0008073432], [-0.0023475489, 0.0029356374, 0.0052674275, -0.0007578638, -0.0028534882], [-0.0001894180, -0.0027307668, -0.0007578638, 0.0047032056, 0.0026710177], [-0.0002716368, -0.0008073432, -0.0028534882, 0.0026710177, 0.0049646795] ] self.hqic = 742.789053551421 self.llf = -361.147028843730 self.resid = resids_css[1:, 1] self.fittedvalues = yhat_css[1:, 1] self.pvalues = [2.15e-20, 0.0011, 6.01e-19, 0.0206, 5.92e-11] # self.tvalues = [9.255, 3.269, -8.892, 2.314, 6.546] self.tvalues = [12.643194, 3.847252, -11.501785, 2.301399, 6.126120] self.sigma2 = 1.031950951582**2 class Y_arma41: def __init__(self, method="mle"): if method == "mle": self.params = [0.859167822255, -0.445990454620, -0.094364739597, 0.633504596270, 0.039251240870] self.aic = 680.801215465509 self.bic = 701.929980972682 self.arroots = [1.0209-0j, 0.2966-0.9835j, 0.2966+0.9835j, -1.4652 + 0.0000j] self.maroots = [-25.4769 + 0.0000] self.bse = [0.097363938243, 0.136020728785, 0.128467873077, 0.081059611396, 0.138536155409] self.cov_params = [ [0.0094797, -0.012908, 0.011870, -0.0073247, -0.011669], [-0.012908, 0.018502, -0.017103, 0.010456, 0.015892], [0.011870, -0.017103, 0.016504, -0.010091, -0.014626], [-0.0073247, 0.010456, -0.010091, 0.0065707, 0.0089767], [-0.011669, 0.015892, -0.014626, 0.0089767, 0.019192]] self.hqic = 689.304925277181 self.llf = -334.400607732754 self.resid = resids_mle[:, 2] self.fittedvalues = yhat_mle[:, 2] self.pvalues = [1.10e-18, 0.0010, 0.4626, 5.48e-15, 0.7769] self.tvalues = [8.824, -3.279, -.7345, 7.815, .2833] self.sigma2 = 0.911409665692 ** 2 self.forecast = forecast_results['fc41'] self.forecasterr = forecast_results['fe41'] elif method == "css": self.params = [0.868370308475, -0.459433478113, -0.086098063077, 0.635050245511, 0.033645204508] self.aic = 666.171731561927 self.bic = 687.203720777521 self.arroots = [1.0184+0.0000j, 0.2960-0.9803j, 0.2960+0.9803j, -1.4747+0.0000j] self.maroots = [-29.7219 + 0.0000j] # NOTE: bse, cov_params, tvalues taken from R; commented-out # versions below are from [TODO: finish this sentence] # self.bse = [0.077822066628, 0.112199961491, 0.104986211369, # 0.068394652456, 0.113996438269] self.bse = [0.09554032, 0.13387533, 0.12691479, 0.08045129, 0.13456419] # self.cov_params = [ # [0.0060563, -0.0083712, 0.0076270, -0.0047067, -0.0070610], # [-0.0083712, 0.012589, -0.011391, 0.0069576, 0.0098601], # [0.0076270, -0.011391, 0.011022, -0.0067771, -0.0089971], # [-0.0047067, 0.0069576, -0.0067771, 0.0046778, 0.0054205], # [-0.0070610, 0.0098601, -0.0089971, 0.0054205, 0.012995] # ] self.cov_params = [ [.009127952, -.01243259, .011488329, -.007070855, -.011031907], [-.012432590, .01792260, -.016597806, .010136298, .015053122], [.011488329, -.01659781, .016107364, -.009851695, -.013923062], [-.007070855, .01013630, -.009851695, .006472410, .008562476], [-.011031907, .01505312, -.013923062, .008562476, .018107521] ] self.hqic = 674.640335476392 self.llf = -327.085865780964 self.resid = resids_css[4:, 2] self.fittedvalues = yhat_css[4:, 2] self.pvalues = [6.51e-29, 4.23e-05, 0.4122, 1.62e-20, 0.7679] # self.tvalues = [11.16, -4.095, -0.8201, 9.285, 0.2951] self.tvalues = [9.0887381, -3.4315100, -0.6786792, 7.8938778, 0.2503143] self.sigma2 = 0.914551777765**2 class Y_arma22: def __init__(self, method="mle"): if method == "mle": self.params = [0.810898877154, -0.535753742985, 0.101765385197, -0.691891368356] self.aic = 756.286535543453 self.bic = 773.893840132765 self.arroots = [0.7568 - 1.1375j, 0.7568 + 1.1375j] self.maroots = [-1.1309, 1.2780] self.bse = [0.065073834100, 0.060522519771, 0.065569474599, 0.071275323591] self.cov_params = [ [0.0042346, -0.0012416, -0.0024319, -0.0012756], [-0.0012416, 0.0036630, -0.00022460, -0.0019999], [-0.0024319, -0.00022460, 0.0042994, 0.0017842], [-0.0012756, -0.0019999, 0.0017842, 0.0050802]] self.hqic = 763.372960386513 self.llf = -373.143267771727 self.resid = resids_mle[:, 3] self.fittedvalues = yhat_mle[:, 3] self.pvalues = [1.22e-35, 8.59e-19, 0.1207, 2.81e-22] self.tvalues = [12.46, -8.852, 1.552, -9.707] self.sigma2 = 1.069529754715**2 elif method == "css": self.params = [0.811172493623, -0.538952207139, 0.108020549805, -0.697398037845] self.aic = 749.652327535412 self.bic = 767.219471266237 self.arroots = [0.7525 - 1.1354j, 0.7525 + 1.1354j] self.maroots = [-1.1225 + 0.0000j, 1.2774 + 0.0000j] # NOTE: bse, cov_params, tvalues taken from R; commented-out # versions below are from [TODO: finish this sentence] # self.bse = [0.063356402845, 0.064719801680, 0.058293106832, # 0.061453528114] self.bse = [0.06549657, 0.06127495, 0.06514116, 0.07148213] # self.cov_params = [ # [0.0040140, -0.0016670, -0.0019069, -0.0011369], # [-0.0016670, 0.0041887, -0.00019356, -0.0014322], # [-0.0019069, -0.00019356, 0.0033981, 0.0020063], # [-0.0011369, -0.0014322, 0.0020063, 0.0037765]] self.cov_params = [ [0.004289801, -0.0012980774, -0.0024461381, -0.001244467], [-0.001298077, 0.0037546193, -0.0001725373, -0.002039177], [-0.002446138, -0.0001725373, 0.0042433713, 0.001720042], [-0.001244467, -0.0020391767, 0.0017200417, 0.005109695] ] self.hqic = 756.724194601530 self.llf = -369.826163767706 self.resid = resids_css[2:, 3] self.fittedvalues = yhat_css[2:, 3] self.pvalues = [1.57e-37, 8.26e-17, 0.0639, 7.55e-30] # self.tvalues = [12.80, -8.327, 1.853, -11.35] self.tvalues = [12.385077, -8.795883, 1.657944, -9.755738] self.sigma2 = 1.074973483083**2 class Y_arma50: def __init__(self, method="mle"): if method == "mle": self.params = [0.726892679311, -0.312619864536, 0.323740181610, 0.226499145083, -0.089562902305] self.aic = 691.422630427314 self.bic = 712.551395934487 self.arroots = [1.0772 + 0.0000j, 0.0087 - 1.2400j, 0.0087 + 1.2400j, -1.9764 + 0.0000j, 3.4107 + 0.0000j] self.maroots = None # TODO: empty array? self.bse = [0.062942787895, 0.076539691571, 0.076608230545, 0.077330717503, 0.063499540628] self.cov_params = [ [0.0039618, -0.0028252, 0.0013351, -0.0013901, -0.00066624], [-0.0028252, 0.0058583, -0.0040200, 0.0026059, -0.0014275], [0.0013351, -0.0040200, 0.0058688, -0.0041018, 0.0013917], [-0.0013901, 0.0026059, -0.0041018, 0.0059800, -0.0028959], [-0.00066624, -0.0014275, 0.0013917, -0.0028959, 0.0040322]] self.hqic = 699.926340238986 self.llf = -339.711315213657 self.resid = resids_mle[:, 4] self.fittedvalues = yhat_mle[:, 4] self.pvalues = [7.51e-31, 4.42e-05, 2.38e-05, 0.0034, 0.1584] self.tvalues = [11.55, -4.084, 4.226, 2.929, -1.410] self.sigma2 = 0.938374940397 ** 2 self.forecast = forecast_results['fc50'] self.forecasterr = forecast_results['fe50'] elif method == "css": # NOTE: some results use x-12 arima because gretl uses # LS estimates for AR CSS self.params = [0.725706505843, -0.305501865989, 0.320719417706, 0.226552951649, -0.089852608091] # self.aic = 674.817286564674 self.aic = 676.8173 # self.bic = 692.323577617397 self.bic = 697.8248 self.arroots = [1.0755 + 0.0000j, 0.0075-1.2434j, 0.0075 + 1.2434j, -1.9686 + 0.0000j, 3.3994 + 0.0000j] self.maroots = None self.bse = [0.064344956583, 0.078060866211, 0.077980166982, 0.078390791831, 0.064384559496] self.cov_params = [ [0.0041403, -0.0029335, 0.0013775, -0.0014298, -0.00068813], [-0.0029335, 0.0060935, -0.0041786, 0.0026980, -0.0014765], [0.0013775, -0.0041786, 0.0060809, -0.0042177, 0.0014572], [-0.0014298, 0.0026980, -0.0042177, 0.0061451, -0.0029853], [-0.00068813, -0.0014765, 0.0014572, -0.0029853, 0.0041454]] # self.hqic = 681.867054880965 self.hqic = 685.2770 self.llf = -332.408643282337 self.resid = resids_css[5:, 4] self.fittedvalues = yhat_css[5:, 4] self.pvalues = [1.68e-29, 9.09e-05, 3.91e-05, 0.0039, 0.1628] self.tvalues = [11.28, -3.914, 4.113, 2.890, -1.396] # self.sigma2 = 0.949462810435**2 self.sigma2 = .939724 ** 2 class Y_arma02: def __init__(self, method="mle"): if method == "mle": self.params = [0.169096401142, -0.683713393265] self.aic = 775.017701544762 self.bic = 785.582084298349 self.arroots = None self.maroots = [-1.0920 + 0j, 1.3393 + 0j] self.bse = [0.049254112414, 0.050541821979] self.cov_params = [[0.0024260, 0.00078704], [0.00078704, 0.0025545]] self.hqic = 779.269556450598 self.llf = -384.508850772381 self.resid = resids_mle[:, 5] self.fittedvalues = yhat_mle[:, 5] self.pvalues = [.0006, 1.07e-41] self.tvalues = [3.433, -13.53] self.sigma2 = 1.122887152869 ** 2 elif method == "css": # NOTE: bse, cov_params, tvalues taken from R; commented-out # versions below are from [TODO: finish this sentence] self.params = [0.175605240783, -0.688421349504] self.aic = 773.725350463014 self.bic = 784.289733216601 self.arroots = None self.maroots = [-1.0844 + 0.j, 1.3395 + 0.j] # self.bse = [0.044465497496, 0.045000813836] self.bse = [0.04850046, 0.05023068] # self.cov_params = [ # [0.0019772, 0.00090016], # [0.00090016, 0.0020251]] self.cov_params = [ [0.0023522942, 0.0007545702], [0.0007545702, 0.0025231209] ] self.hqic = 777.977205368850 self.llf = -383.862675231507 self.resid = resids_css[:, 5] self.fittedvalues = yhat_css[:, 5] self.pvalues = [7.84e-05, 7.89e-53] # self.tvalues = [3.949, -15.30] self.tvalues = [3.620967, -13.705514] self.sigma2 = 1.123571177436**2 class Y_arma11c: def __init__(self, method="mle"): if method == "mle": self.params = [4.856475759430, 0.664363281011, 0.407547531124] self.aic = 737.922644877973 self.bic = 752.008488549422 self.arroots = [1.5052 + 0j] self.maroots = [-2.4537 + 0j] self.bse = [0.273164176960, 0.055495689209, 0.068249092654] self.cov_params = [ [0.074619, -0.00012834, 1.5413e-05], [-0.00012834, 0.0030798, -0.0020242], [1.5413e-05, -0.0020242, 0.0046579]] self.hqic = 743.591784752421 self.llf = -364.961322438987 self.resid = residsc_mle[:, 0] self.fittedvalues = yhatc_mle[:, 0] self.pvalues = [1.04e-70, 5.02e-33, 2.35e-9] self.tvalues = [17.78, 11.97, 5.971] self.sigma2 = 1.039168068701 ** 2 self.forecast = forecast_results['fc11c'] self.forecasterr = forecast_results['fe11c'] elif method == "css": # NOTE: params, bse, cov_params, tvalues taken from R; # commented-out versions below are from gretl # NOTE: gretl gives the intercept not the mean, x-12-arima # and R agree with us # self.params = [1.625462134333, 0.666386002049, 0.409512270580] self.params = [4.872477127267, 0.666395534262, 0.409517026658] self.aic = 734.613526514951 self.bic = 748.683338100810 self.arroots = [1.5006 + 0.0000j] self.maroots = [-2.4419 + 0.0000] # self.bse = [0.294788633992, 0.057503298669, 0.063059352497] self.bse = [0.2777238133284, 0.0557583459688, 0.0681432545482] # self.cov_params = [ # [0.086900, -0.016074, 0.010536], # [-0.016074, 0.0033066, -0.0021977], # [0.010536, -0.0021977, 0.0039765] # ] self.cov_params = [ [7.71305164897e-02, 5.65375305967e-06, 1.29481824075e-06], [5.65375305967e-06, 3.10899314518e-03, -2.02754322743e-03], [1.29481824075e-06, -2.02754322743e-03, 4.64350314042e-03] ] self.hqic = 740.276857090925 self.llf = -363.306763257476 self.resid = residsc_css[1:, 0] self.fittedvalues = yhatc_css[1:, 0] self.pvalues = [3.51e-08, 4.70e-31, 8.35e-11] # self.tvalues = [5.514, 11.59, 6.494] self.tvalues = [17.544326, 11.951494, 6.009649] self.sigma2 = 1.040940645447**2 class Y_arma14c: def __init__(self, method="mle"): if method == "mle": self.params = [4.773779823083, 0.591149657917, 0.322267595204, -0.702933089342, 0.116129490967, 0.323009574097] self.aic = 720.814886758937 self.bic = 745.465113183973 self.arroots = [1.6916 + 0.0000j] # TODO: had to change order in maroots? self.maroots = [1.1071 - 0.7821j, 1.1071 + 0.7821j, -1.2868 - 0.1705j, -1.2868 + 0.1705j] self.bse = [0.160891073193, 0.151756542096, 0.152996852330, 0.140231020145, 0.064663675882, 0.065045468010] self.cov_params = [ [0.025886, 0.00026606, -0.00020969, -0.00021435, 4.2558e-05, 5.2904e-05], [0.00026606, 0.023030, -0.021269, -0.018787, 0.0015423, 0.0011363], [-0.00020969, -0.021269, 0.023408, 0.018469, -0.0035048, -0.0010750], [-0.00021435, -0.018787, 0.018469, 0.019665, -0.00085717, -0.0033840], [4.2558e-05, 0.0015423, -0.0035048, -0.00085717, 0.0041814, 0.0014543], [5.2904e-05, 0.0011363, -0.0010750, -0.0033840, 0.0014543, 0.0042309]] self.hqic = 730.735881539221 self.llf = -353.407443379469 self.resid = residsc_mle[:, 1] self.fittedvalues = yhatc_mle[:, 1] self.pvalues = [1.82e-193, 9.80e-05, 0.0352, 5.37e-07, 0.0725, 6.84e-07] self.tvalues = [29.67, 3.895, 2.106, -5.013, 1.796, 4.966] self.sigma2 = 0.990262659233 ** 2 elif method == "css": # NOTE: params, bse, cov_params, tvalues taken from R; # commented-out versions below are from # [TODO: Finish this sentence] # self.params = [1.502401748545, 0.683090744792, 0.197636417391, # -0.763847295045, 0.137000823589, 0.304781097398] self.params = [4.740785760452, 0.683056278882, 0.197681128402, -0.763804443884, 0.136991271488, 0.304776424257] self.aic = 719.977407193363 self.bic = 744.599577468616 self.arroots = [1.4639 + 0.0000j] self.maroots = [1.1306-0.7071j, 1.1306+0.7071j, -1.3554 - 0.0896j, -1.3554 + 0.0896j] # self.bse = [0.534723749868, 0.111273280223, 0.119840296133, # 0.111263606843, 0.070759105676, 0.061783181500] self.bse = [0.1750455599911, 0.0942341854820, 0.0999988749541, 0.0929630759694, 0.0628352649371, 0.0645444272345] # self.cov_params = [ # [0.28593, -0.059175, 0.053968, # 0.046974, 0.00085168, 0.0028000], # [-0.059175, 0.012382, -0.011333, # -0.0098375, -0.00012631, -0.00058518], # [0.053968, -0.011333, 0.014362, # 0.010298, -0.0028117, -0.00011132], # [0.046974, -0.0098375, 0.010298, # 0.012380, 0.00031018, -0.0021617], # [0.00085168, -0.00012631, -0.0028117, # 0.00031018, 0.0050069, 0.00079958], # [.0028000, -0.00058518, -0.00011132, # -0.0021617, 0.00079958, 0.0038172]] self.cov_params = [ [0.030640948072601, -1.61599091345e-03, 0.001707084515950, 0.001163372764659, -1.78587340563e-04, 0.000116062673743], [-0.001615990913449, 8.88008171345e-03, -0.007454252059003, -0.006468410832237, 5.66645379098e-05, -0.000381880917361], [0.001707084515950, -7.45425205900e-03, 0.009999774992092, 0.005860013051220, -2.27726197200e-03, 0.000757683049669], [0.001163372764659, -6.46841083224e-03, 0.005860013051220, 0.008642133493695, 4.40550745987e-04, -0.002170706208320], [-0.000178587340563, 5.66645379098e-05, -0.002277261972002, 0.000440550745987, 3.94827051971e-03, 0.000884171120090], [0.000116062673743, -3.81880917361e-04, 0.000757683049669, -0.002170706208320, 8.84171120090e-04, 0.004165983087027] ] self.hqic = 729.888235701317 self.llf = -352.988703596681 self.resid = residsc_css[1:, 1] self.fittedvalues = yhatc_css[1:, 1] self.pvalues = [.0050, 8.31e-10, .0991, 6.64e-12, .0528, 8.09e-7] # self.tvalues = [2.810, 6.139, 1.649, -6.865, 1.936, 4.933] self.tvalues = [27.08315344127, 7.24849772286, 1.97683352430, -8.21621311385, 2.18016541548, 4.72196341831] self.sigma2 = 0.998687642867**2 class Y_arma41c: def __init__(self, method="mle"): if method == "mle": self.params = [1.062980233899, 0.768972932892, -0.264824839032, -0.279936544064, 0.756963578430, 0.231557444097] self.aic = 686.468309958027 self.bic = 711.118536383063 self.arroots = [1.0077 + 0j, .3044-.9793j, .3044+.9793j, -1.2466 + 0j] self.maroots = [-4.3186 + 0.j] self.bse = [2.781653916478, 0.063404432598, 0.091047664068, 0.084679571389, 0.054747989396, 0.098952817806] self.cov_params = [ [7.7376, 0.0080220, -0.0039840, 0.0064925, 0.0022936, -0.0098015], [0.0080220, 0.0040201, -0.0054843, 0.0046548, -0.0029922, -0.0047964], [-0.0039840, -0.0054843, 0.0082897, -0.0072913, 0.0043566, 0.0067289], [0.0064925, 0.0046548, -0.0072913, 0.0071706, -0.0043610, -0.0057962], [0.0022936, -0.0029922, 0.0043566, -0.0043610, 0.0029973, 0.0036193], [-0.0098015, -0.0047964, 0.0067289, -0.0057962, 0.0036193, 0.0097917]] self.hqic = 696.389304738311 self.llf = -336.234154979014 self.resid = residsc_mle[:, 2] self.fittedvalues = yhatc_mle[:, 2] self.pvalues = [0.7024, 7.50e-34, 0.0036, 0.0009, 1.77e-43, 0.0193] self.tvalues = [0.3821, 12.13, -2.909, -3.306, 13.83, 2.340] self.sigma2 = 0.915487643192 ** 2 self.forecast = forecast_results['fc41c'] self.forecasterr = forecast_results['fe41c'] elif method == "css": # NOTE: params, bse, cov_params, tvalues taken from R; # commented-out versions below are from # [TODO: Finish this sentence] # self.params = [-0.077068926631, 0.763816531155, -0.270949972390, # -0.284496499726, 0.757135838677, 0.225247299659] self.params = [-2.234160612756, 0.763815335585, -0.270946894536, -0.284497190744, 0.757136686518, 0.225260672575] self.aic = 668.907200379791 self.bic = 693.444521131318 self.arroots = [1.0141+0.0000j, 0.3036-0.9765j, 0.3036+0.9765j, -1.2455+0.0000j] self.maroots = [-4.4396+0.0000j] # self.bse = [0.076048453921, 0.067854052128, 0.098041415680, # 0.090698349822, 0.057331126067, 0.099985455449] self.bse = [2.1842857865614, 0.0644148863289, 0.0923502391706, 0.0860004491012, 0.0558014467639, 0.1003832271008] # self.cov_params = [ # [0.0057834, 0.00052477, -0.00079965, # 0.00061291, -0.00013618, -0.0018963], # [0.00052477, 0.0046042, -0.0062505, # 0.0053416, -0.0032941, -0.0047957], # [-0.00079965, -0.0062505, 0.0096121, # -0.0084500, 0.0047967, 0.0064755], # [0.00061291, 0.0053416, -0.0084500, # 0.0082262, -0.0048029, -0.0057908], # [-0.00013618, -0.0032941, 0.0047967, # -0.0048029, 0.0032869, 0.0035716], # [-0.0018963, -0.0047957, 0.0064755, # -0.0057908, 0.0035716, 0.0099971] # ] self.cov_params = [ [4.77110439737413, -0.00908682223670, 0.00330914414276, -0.00684678121434, -0.00232348925409, 0.00950558295301], [-0.00908682223670, -0.00562941039954, 0.00852856667488, -0.00749429397372, -0.00304322809665, -0.00494984519949], [0.00330914414276, -0.00562941039954, 0.00852856667488, -0.00749429397372, 0.00443590637587, 0.00693146988144], [-0.00684678121434, 0.00482359594764, -0.00749429397372, 0.00739607724561, -0.00448059420947, -0.00600908311031], [-0.00232348925409, -0.00304322809665, 0.00443590637587, -0.00448059420947, 0.00311380146095, 0.00373734623817], [0.00950558295301, -0.00494984519949, 0.00693146988144, -0.00600908311031, 0.00373734623817, 0.01007679228317]] self.hqic = 678.787238280001 self.llf = -327.453600189896 self.resid = residsc_css[4:, 2] self.fittedvalues = yhatc_css[4:, 2] self.pvalues = [0.3109, 2.15e-29, 0.0057, 0.0017, 8.06e-40, 0.0243] # self.tvalues = [-1.013, 11.26, -2.764, -3.137, 13.21, 2.253] self.tvalues = [-1.02283347101, 11.85774561000, -2.93390571556, -3.30808959392, 13.56840602577, 2.24400708246] self.sigma2 = 0.915919923456**2 class Y_arma22c: def __init__(self, method="mle"): if method == "mle": self.params = [4.507728587708, 0.788365037622, -0.358656861792, 0.035886565643, -0.699600200796] self.aic = 813.417242529788 self.bic = 834.546008036962 self.arroots = [1.0991 - 1.2571j, 1.0991 + 1.2571j] self.maroots = [-1.1702 + 0.0000j, 1.2215 + 0.0000j] self.bse = [0.045346684035, 0.078382496509, 0.07004802526, 0.069227816205, 0.070668181454] self.cov_params = [ [0.0020563, -2.3845e-05, -6.3775e-06, 4.6698e-05, 5.8515e-05], [-2.3845e-05, 0.0061438, -0.0014403, -0.0035405, -0.0019265], [-6.3775e-06, -0.0014403, 0.0049067, -0.00059888, -0.0025716], [4.6698e-05, -0.0035405, -0.00059888, 0.0047925, 0.0022931], [5.8515e-05, -0.0019265, -0.0025716, 0.0022931, 0.0049940]] self.hqic = 821.920952341460 self.llf = -400.708621264894 self.resid = residsc_mle[:, 3] self.fittedvalues = yhatc_mle[:, 3] self.pvalues = [0.0000, 8.48e-24, 3.05e-07, 0.6042, 4.17e-23] self.tvalues = [99.41, 10.06, -5.120, 0.5184, -9.900] self.sigma2 = 1.196309833136 ** 2 elif method == "css": # NOTE: params, bse, cov_params, tvalues taken from R; # commented-out versions below are from # [TODO: Finish this sentence] # self.params = [2.571274348147, 0.793030965872, -0.363511071688, # 0.033543918525, -0.702593972949] self.params = [4.507207454494, 0.793055048760, -0.363521072479, 0.033519062805, -0.702595834943] self.aic = 806.807171655455 self.bic = 827.887744132445 # self.bse = [0.369201481343, 0.076041378729, 0.070029488852, # 0.062547355221, 0.068166970089] self.bse = [0.0446913896589, 0.0783060902603, 0.0697866176073, 0.0681463870772, 0.068958002297] # self.cov_params = [ # [0.13631, -0.017255, -0.012852, 0.014091, 0.017241], # [-0.017255, 0.0057823, -0.0020013, -0.0026493, -0.0014131], # [-0.012852, -0.0020013, 0.0049041, -0.00042960, -0.0023845], # [0.014091, -0.0026493, -0.00042960, 0.0039122, 0.0022028], # [0.017241, -0.0014131, -0.0023845, 0.0022028, 0.0046467] # ] self.cov_params = [ [1.99732030964e-03, -2.22972353619e-05, -0.000009957435095, 4.64825632252e-05, 5.98134427402e-05], [-2.22972353619e-05, 6.13184377186e-03, -0.001435210779968, -3.47284237940e-03, -1.95077811843e-03], [-9.95743509501e-06, -1.43521077997e-03, 0.004870171997068, -6.54767224831e-04, -2.44459075151e-03], [4.64825632252e-05, -3.47284237940e-03, -0.000654767224831, 4.64393007167e-03, 2.34032945541e-03], [5.98134427402e-05, -1.95077811843e-03, -0.002444590751509, 2.34032945541e-03, 4.75520608091e-03]] self.arroots = [1.0908 - 1.2494j, 1.0908 + 1.2494j] self.maroots = [-1.1694 + 0.0000j, 1.2171 + 0.0000j] self.hqic = 815.293412134796 self.llf = -397.403585827727 self.resid = residsc_css[2:, 3] self.fittedvalues = yhatc_css[2:, 3] self.pvalues = [3.30e-12, 1.83e-25, 2.09e-07, 0.5918, 6.55e-25] # self.tvalues = [6.964, 10.43, -5.191, 0.5363, -10.31] self.tvalues = [100.851808120009, 10.127629231947, -5.209036989363, 0.491868523669, -10.188749840927] self.sigma2 = 1.201409294941**2 class Y_arma50c: def __init__(self, method="mle"): if method == "mle": self.params = [4.562207236168, 0.754284447885, -0.305849188005, 0.253824706641, 0.281161230244, -0.172263847479] self.aic = 711.817562780112 self.bic = 736.467789205148 self.arroots = [-1.6535 + 0.j, .0129 - 1.2018j, .0129 + 1.2018j, 1.1546 + 0.j, 2.1052 + 0j] self.maroots = None self.bse = [0.318447388812, 0.062272737541, 0.076600312879, 0.077310728819, 0.076837326995, 0.062642955733] self.cov_params = [ [0.10141, -6.6930e-05, -7.3157e-05, -4.4815e-05, 7.7676e-05, -0.00013170], [-6.6930e-05, 0.0038779, -0.0028465, 0.0013770, -0.0012194, -0.00058978], [-7.3157e-05, -0.0028465, 0.0058676, -0.0040145, 0.0024694, -0.0012307], [-4.4815e-05, 0.0013770, -0.0040145, 0.0059769, -0.0040413, 0.0013481], [7.7676e-05, -0.0012194, 0.0024694, -0.0040413, 0.0059040, -0.0028575], [-0.00013170, -0.00058978, -0.0012307, 0.0013481, -0.0028575, 0.0039241]] self.hqic = 721.738557560396 self.llf = -348.908781390056 self.resid = residsc_mle[:, 4] self.fittedvalues = yhatc_mle[:, 4] self.pvalues = [1.50e-46, 9.06e-34, 6.53e-05, .0010, .0003, .0060] self.tvalues = [14.33, 12.11, -3.993, 3.283, 3.659, -2.750] self.sigma2 = 0.973930886014 ** 2 self.forecast = forecast_results['fc50c'] self.forecasterr = forecast_results['fe50c'] elif method == "css": # NOTE: params, bse, cov_params, tvalues taken from R; # commented-out versions below are from # [TODO: Finish this sentence] # likelihood based results from x-12 arima # self.params = [0.843173779572, 0.755433266689, -0.296886816205, # 0.253572751789, 0.276975022313, -0.172637420881] self.params = [4.593494860193, 0.755427402630, -0.296867127441, 0.253556723526, 0.276987447724, -0.172647993470] # self.aic = 694.843378847617 self.aic = 696.8434 # self.bic = 715.850928110886 self.bic = 721.3522 self.arroots = [-1.6539+0.0000j, 0.0091-1.2069j, 0.0091+1.2069j, 1.1508+0.0000j, 2.0892+0.0000j] self.maroots = None # self.bse = [0.236922950898, 0.063573574389, 0.078206936773, # 0.078927252266, 0.078183651496, 0.063596048046] self.bse = [0.3359627893565, 0.0621593755265, 0.0764672280408, 0.0771715117870, 0.0764444608104, 0.0621813373935] # self.cov_params = [ # [0.056132, -0.0028895, -0.0012291, # -0.0031424, -0.0012502, -0.0028739], # [-0.0028895, 0.0040416, -0.0029508, # 0.0014229, -0.0012546, -0.00062818], # [-0.0012291, -0.0029508, 0.0061163, # -0.0041939, 0.0025537, -0.0012585], # [-0.0031424, 0.0014229, -0.0041939, # 0.0062295, -0.0041928, 0.0014204], # [-0.0012502, -0.0012546, 0.0025537, # -0.0041928, 0.0061127, -0.0029479], # [-0.0028739, -0.00062818, -0.0012585, # 0.0014204, -0.0029479, 0.0040445] # ] self.cov_params = [ [1.12870995832e-01, 4.32810158586e-05, -1.89697385245e-05, 0.0000465331836881, -0.000024151327384, 0.000109807500875], [4.32810158586e-05, 3.86378796585e-03, -2.82098637123e-03, 0.001360256141301, -0.001199382243647, -0.000600542191229], [-1.89697385245e-05, -2.82098637123e-03, 5.84723696424e-03, -0.004009391809667, 0.002441359768335, -0.001203154760767], [4.65331836880e-05, 1.36025614130e-03, -4.00939180967e-03, 0.005955442231484, -0.004008307295820, 0.001357917028471], [-2.41513273840e-05, -1.19938224365e-03, 2.44135976834e-03, -0.004008307295820, 0.005843755588588, -0.002818181279545], [1.09807500875e-04, -6.00542191229e-04, -1.20315476077e-03, 0.001357917028471, -0.002818181279545, 0.003866518720043]] # self.hqic = 703.303100827167 self.hqic = 706.7131 self.llf = -341.421689423809 self.resid = residsc_css[5:, 4] self.fittedvalues = yhatc_css[5:, 4] self.pvalues = [0.0004, 1.45e-32, 0.0001, 0.0013, 0.0004, 0.0066] # self.tvalues = [3.559, 11.88, -3.796, 3.213, 3.543, -2.715] self.tvalues = [13.67262984389, 12.15307258528, -3.88227918086, 3.28562597329, 3.62338153462, -2.77652428699] # self.sigma2 = 0.987100631424**2 self.sigma2 = 0.974939 ** 2 class Y_arma02c: def __init__(self, method="mle"): if method == "mle": self.params = [4.519277801954, 0.200385403960, -0.643766305844] self.aic = 758.051194540770 self.bic = 772.137038212219 self.arroots = None self.maroots = [-1.1004 + 0.j, 1.4117 + 0.j] self.bse = [0.038397713362, 0.049314652466, 0.048961366071] self.cov_params = [ [0.0014744, 6.2363e-05, 6.4093e-05], [6.2363e-05, 0.0024319, 0.0014083], [6.4093e-05, 0.0014083, 0.0023972]] self.hqic = 763.720334415218 self.llf = -375.025597270385 self.resid = residsc_mle[:, 5] self.fittedvalues = yhatc_mle[:, 5] self.pvalues = [0.0000, 4.84e-5, 1.74e-39] self.tvalues = [117.7, 4.063, -13.15] self.sigma2 = 1.081406299967 ** 2 elif method == "css": # NOTE: cov_params, tvalues taken from R; commented-out # versions below are from [TODO: Finish this sentence] self.params = [4.519869870853, 0.202414429306, -0.647482560461] self.aic = 756.679105324347 self.bic = 770.764948995796 self.arroots = None self.maroots = [-1.0962 + 0.0000j, 1.4089 + 0.0000j] self.bse = [0.038411589816, 0.047983057239, 0.043400749866] # self.cov_params = [ # [0.0014755, 9.0191e-05, 7.3561e-06], # [9.0191e-05, 0.0023024, 0.0012479], # [7.3561e-06, 0.0012479, 0.0018836]] self.cov_params = [ [1.46121526606e-03, 5.30770136338e-05, 5.34796521051e-05], [5.30770136338e-05, 2.37105883909e-03, 1.41090983316e-03], [5.34796521051e-05, 1.41090983316e-03, 2.35584355080e-03]] self.hqic = 762.348245198795 self.llf = -374.339552662174 self.resid = residsc_css[:, 5] self.fittedvalues = yhatc_css[:, 5] self.pvalues = [0.0000, 2.46e-05, 2.49e-50] # self.tvalues = [117.7, 4.218, -14.92] self.tvalues = [118.24120637494, 4.15691796413, -13.33981086206] self.sigma2 = 1.081576475937**2