74 #Exported Settings for TSM Version 4.32.09-11-10 at 8:10:09 on 11-11-2010 13 4.15.02-12-05 1 152 0 1 0 0 0 1 0 1 1 0 0 0 0 2 0 0 0 1 0 0 10 2 10 9 2 2 0 1 0 100 0 0 0 1 1 1 1000 0 0 0 0 7 0 8 1 0 0 0 0 0 640 480 50 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 2 12 0 0 0 1 7 0 0 8 0 1 0 2 0 1 0 0 0 13 0 1 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 500 1 500 0 0 1 1 123456789 0 0 1000 1 500 0 0 0 0 1 0 10 0 0 0 0 0 0 0 5 11 2 7 5 3 4 12 6 8 0 0 0 1 4 3 5 6 2 4 3 0 0 0 7 1 9 4 11 2 3 8 0 0 0 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0 10 9 0 11 results.txt 0 12 selected.txt 13 minmintest.ox 0 0 9 notes.txt 0 1 0 1 10 Empty Slot 0 0 0 0 0 0 6 17 1_Intercept_Break 18 2_Intercept_Breaks 13 1_Slope_Break 14 NonCoint_Break 17 Run_SubsampleTest 13 SubsampleTest 0 0 5 1 178 0 0 0 0 0 0 0 0 1 1 99 0 0 0 1 3 1 0 0 -1 2 1 1 0 0 0 1 0 0 50 500 0 0 0 0 0 0 0 0 11 0 0 0 2 0 0 0 1 0 10 5 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 -1 0 0 1000 1 0 0 0 0 0 0 0 50 0 2 0 12 0 0 0 0 0 0 0 0 0 1 1 0 0 1 -1 2 0 0 0 0 1 -1 0 1 0 0 0 0 -1 0 0 0 1 1 0 0 0 7 0 1 0 0 0 0 0 0 0 2 0 1 0 1 1 500 1 500 0 0 0 0 0 0 0 0 0 1 500 1 500 0 0 0 0 0 0 1 0 1 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 1 1 1e-005 1 2 1 1 1 4 500 5 0.85 10 0 0 1 2 1 0.66 1 4 0.5 0.5 0 0 34 11 1 1 14 minmindata.xls 17 SubsampleTest.tsd 1 0 0 0 0 295 Sample Settings: START_SAMPLE = 1; END_SAMPLE = 500; Model Settings: CORRELOGRAM_ORDER = 1; COVMAT_TYPE = 0; INPUT_FILE = "minmindata.xls"; LINEAR_REGRESSION = 1; LISTING_FILE = "SubsampleTest.tsd"; METHOD = LSQ; SERIES = {"X1"}; Simulation Settings: Parameter Values: 0 0 0 0 0 0 0 0 0 0 0 0 1 2 X1 0 0 0 0 1 2 X1 1 2 X1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 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/***************************************************************************** 68 **** Testing for Cointegration under Structural Breaks **** 0 73 This file implements the tests described in "Tests for cointegration with 77 structural breaks based on subsamples" by James Davidson and Andrea Monticini 47 (http://people.ex.ac.uk/jehd201/minmintest.pdf) 0 76 A total of 14 tests are available, for models with one or two regressors and 81 intercept with optional trend. These are the seven subsampling variants described 77 in the paper, implemented with a choice of two test statistics, the augmented 80 Dickey Fuller test (ADF), and the Phillips-Perron (1988) nonparametric test(PP). 0 83 The tests are tabulated for the minima of the statistics over subsamples defined by 59 pairs {L1, L2} (start = [L1.T]+1, end = [L2.T]) as follows. 0 27 QS: {0, 0.5} and {0.5, 1} 35 QS*: {0, 0.5}, {0.5, 1} and {0,1} 65 QI(0.5): {0, L2}, 0.5 <= L2 <= 1} and {L1, 1}, 0 <= L1 <= 0.5} 66 QI(0.35): {0, L2}, 0.35 <= L2 <= 1} and {L1, 1}, 0 <= L1 <= 0.65} 62 QI(0.2): {0, L2}, 0.2 <= L2 <= 1 and {L1, 1}, 0 <= L1 <= 0.8 40 QR(0.5): {L1, L1 + 0.5}, 0 <= L1 <= 0.5 50 QR(0.5): {L1, L1 + 0.5}, 0 <= L1 <= 0.5 and {0,1} 0 80 Critical values for other cases have not yet been tabulated, but TSM can be used 42 to generate additional tables as required. 0 20 TO IMPLEMENT A TEST: 44 1. Open the dialog Model / Dynamic Equation. 0 68 2. Select a dependent variable, and one or two regressors of Type 1. 0 43 3. Open the Model / Coded Equations dialog. 0 68 4. Select the 'Statistic' radio button, under Supplied Ox Functions. 0 71 5 Select the test required by name, using the Next and Previous buttons 26 to move through the list. 0 69 6. In the Model / Dynamic Equation diolog, check the 'Coded Function' 11 checkbox. 0 75 7. Select the settings required for autocorrelation correction, as follows. 0 80 PP test: choose the kernel and bandwidth in the Options / Tests and Diagnostics 8 dialog. 0 54 ADF test: the lag length can be selected in two ways. 79 * To maximize a model selection criterion. The choice set is 0,...,0.6T^(1/3), 67 where T denotes the subsample length (NOT the full sample size). 75 Choose from the Akaike, Schwarz and Hannan Quinn criteria using the radio 71 buttons in Options / Tests and Diagnostics, Model Selection Criteria. 76 * Manually. Choose the option "None" under 'Model Selection Criteria', and 74 set the lag length as for Type 1 regressors in Model / Dynamic Equation. 1 76 8. Choose the (overall) test sample by pressing the <<< Sample >>> button in 48 Model / Dynamic Equation and moving the slider. 0 82 9. (Optional) Open the Model Manager dialog, click 'Store Current Model' and enter 77 a name to identify the specifications. The setup can then be recalled at any 25 time using 'Load Model'. 0 78 10. To Run the test, press the 'Run' or 'Evaluate' buttons on the tool bar, or 40 press 'Go' in Model / Dynamic Equation. 1 8 OUTPUTS: 82 1. The chosen statistic(s), with p-value(s) (expressed as inequalities) drawn from 80 the included tabulations. When a single test is run the corresponding subsample 13 is reported. 0 66 2. The regular full sample test (PP or ADF) and p-value inequality 0 84 3. The test of I(1) (PP or ADF) applied to the normalized (LHS) variable in the test 24 and p-value inequality. 0 75 NOTE: The signs of the statistics are reversed so that the rejection region 75 corresponds to the upper tail. This is to allow p-values to be calculated 20 in the usual way. 0 18 ADDITIONAL OPTION: 76 Test each variable in the set for I(1) (same statistic as for main test, PP 79 or ADF) and normalize the regression on the case having the largest statistic. 64 * Implement this option by naming parameter P[0] as 'Option' in 73 Model / Coded Function (any name will serve). Then set P[0] = 1 in the 27 Values / Equation dialog. 75 CAUTION: The reported I(1) test is for this 'best' case, hence it does not 52 have its nominal distribution. Ignore the p-value! 0 82 *********************************************************************************/ 0 47 extern run_olsc1(const vY, const mX, const vB); 48 extern PP(const vcData, const dBW, const iType); 71 extern ADFTest(const vcData, const iMaxlag, const iType, const iERSTr); 19 #include 16 decl CRITVALS = 2 { 29 // 0: 0 regressors, intercept 49 // 0.5 0.9 0.95 0.975 0.99 59 < 1.58238 , 2.583435, 2.878835, 3.14468, 3.447755 ; // DF 59 1.99951 , 2.87323 , 3.12748 , 3.375115, 3.668695 ; // QS 60 2.12464 , 2.98233 , 3.252595, 3.49584, 3.779345 ; // QS* 62 2.7265 , 3.597255, 3.85981 , 4.10037, 4.371325 ; // QI0.5 63 2.913435, 3.75304 , 4.01498 , 4.22723, 4.499695 ; // QI0.35 62 3.07985 , 3.865275, 4.110185, 4.319465, 4.59499 ; // QI0.2 59 2.88944 , 3.70529 , 3.95335 , 4.1824, 4.451945 ; // QR 60 2.89506 , 3.71634 , 3.966 , 4.18452, 4.4632 >, // QR* 0 37 // 1: 0 regressors, intercept + trend 48 // 0.5 0.9 0.95 0.975 0.99 57 < 2.19146 , 3.13294 , 3.43312 , 3.69237 , 4.0062 ; // DF 58 2.58651 , 3.41958 , 3.67591 , 3.90661 , 4.18857 ; // QS 59 2.72697 , 3.53719 , 3.78825 , 4.01648 , 4.30177 ; // QS* 61 3.32647 , 4.12508 , 4.37525 , 4.60617 , 4.87558 ; // QI0.5 62 3.48251 , 4.24329 , 4.48749 , 4.70351 , 4.97589 ; // QI0.35 61 3.63877 , 4.36689 , 4.59682 , 4.81717 , 5.06626 ; // QI0.2 57 3.43497 , 4.19928 , 4.43885 , 4.667 , 4.92938 ; // QR 60 3.44637 , 4.21069 , 4.45026 , 4.67841 , 4.92938 >, // QR* 0 28 // 2: 1 regressor, intercept 49 // 0.5 0.9 0.95 0.975 0.99 61 < 2.07266 , 3.06435 , 3.367295, 3.62166 , 3.91182 ; //ADF1 60 2.48687 , 3.346105, 3.616015, 3.8432 , 4.138005 ; //QS 59 2.61854 , 3.463695, 3.73189 , 3.96511 , 4.26828 ; //QS* 62 3.212635, 4.05839 , 4.30122 , 4.541975, 4.815155 ; //QI0.5 62 3.39617 , 4.200435, 4.452855, 4.668355, 4.941585 ; //QI0.35 62 3.563505, 4.32718 , 4.56742 , 4.772375, 5.03685 ; //QI0.2 58 3.34064 , 4.155415, 4.406395, 4.61776 , 4.88134 ; //QR 61 3.36463 , 4.159445, 4.3989 , 4.623925, 4.877685 >, //QR* 0 25 // 3: 1 regressor + trend 48 // 0.5 0.9 0.95 0.975 0.99 57 < 2.55217, 3.50336 , 3.79355 , 4.0515 , 4.35782 ; //DF 57 2.95916 , 3.79132 , 4.06121 , 4.29736 , 4.57849 ; //QS 58 3.08246 , 3.90957 , 4.16585 , 4.39884 , 4.66678 ; //QS* 60 3.6728 , 4.48019 , 4.7449 , 4.95668 , 5.22139 ; //QI0.5 61 3.84076 , 4.60268 , 4.86055 , 5.07154 , 5.32942 ; //QI0.35 60 3.99951 , 4.73577 , 4.96893 , 5.17754 , 5.43523 ; //QI0.2 57 3.78188 , 4.56328 , 4.80274 , 5.01699 , 5.29426 ; //QR 59 3.79448 , 4.56328 , 4.80274 , 5.0422 , 5.29426 >, //QR* 0 29 // 4: 2 regressors, intercept 49 // 0.5 0.9 0.95 0.975 0.99 58 < 2.478845, 3.466265, 3.778275, 4.028405, 4.340415 ; // DF 58 2.89262 , 3.74902 , 4.015005, 4.263515, 4.56164 ; // QS 59 3.03658 , 3.87548 , 4.134525, 4.375695, 4.652605 ; // QS* 61 3.62775 , 4.454475, 4.710335, 4.94175 , 5.20273 ; // QI0.5 62 3.79156 , 4.58551 , 4.83209 , 5.035185, 5.292485 ; // QI0.35 61 3.95213 , 4.708695, 4.947085, 5.14407 , 5.40068 ; // QI0.2 58 3.726 , 4.535305, 4.782855, 4.99685 , 5.249915 ; // QR 60 3.751055, 4.540825, 4.78837 , 5.016385, 5.269455 >, // QR* 0 37 // 5: 2 regressors, intercept + trend 49 // 0.5 0.9 0.95 0.975 0.99 57 < 2.88484 , 3.85633 , 4.15525 , 4.39438 , 4.6933 ; // DF 58 3.29425 , 4.14108 , 4.40723 , 4.64918 , 4.92742 ; // QS 59 3.4292 , 4.26052 , 4.52108 , 4.75682 , 5.05461 ; // QS* 61 4.00663 , 4.81757 , 5.07307 , 5.30635 , 5.57296 ; // QI0.5 62 4.16875 , 4.94402 , 5.18768 , 5.42026 , 5.68606 ; // QI0.35 61 4.33656 , 5.06655 , 5.30015 , 5.52402 , 5.77708 ; // QI0.2 58 4.09769 , 4.89366 , 5.14793 , 5.35798 , 5.61225 ; // QR 61 4.11791 , 4.90409 , 5.15178 , 5.36717 , 5.61487 >}; // QR* 0 0 13 MinMinNames() 1 { 56 return {"PP-QS Test", "PP-QS* Test", "PP-QI(0.5) Test", 58 "PP-QI(0.35) Test", "PP-QI(0.2) Test", "PP-QR(0.5) Test", 53 "PP-QR*(0.5) Test", "All PP-Q Tests", "ADF-QS Test", 57 "ADF-QS* Test", "ADF-QI(0.5) Test", "ADF-QI(0.35) Test", 61 "ADF-QI(0.2) Test", "ADF-QR(0.5) Test", "ADF-QR*(0.5) Test", 20 "All ADF-Q Tests"}; 1 } 0 38 CheckData(const mDataset, const vVars) 2 { 40 decl bgn = 0, end = rows(mDataset) - 1; 38 for (decl k=0; k 0 && ismissing(mDataset[end][vVars[k]])) end--; 32 for (decl j=bgn; j < end; j++) 53 if (ismissing(mDataset[j][vVars[k]])) bgn = j+1; 2 } 17 return bgn~end; 1 } 0 24 PPStat(const y, const x) 1 { 37 decl res = y - x*invertsym(x'x)*x'y; 26 decl bw = HAC_BANDWIDTH; 27 return PP(res, &bw, 0)[0]; 1 } 0 37 ADFStat(const y, const x, const lags) 1 { 11 decl beta; 24 run_olsc1(y, x, &beta); 48 decl stat = ADFTest(y - x*beta, lags, 0, 0); 16 return stat[0]; 1 } 0 40 CointStat(const y, const x, const lags ) 1 { 37 if (lags == -1) return PPStat(y, x); 33 else return ADFStat(y, x, lags); 1 } 0 68 MinMin_Test(const y, const x, const lags, const init, const rolling, 41 const star, const cStart, const sSample) 1 { 36 decl crit, t0, minmin, n = rows(y); 30 if (rolling) t0 = floor(n/2); 25 else t0 = floor(init*n); 17 if (rolling < 0) 22 { crit = zeros(1, 3); 45 if (star) crit[0] = -CointStat(y, x, lags); 22 else crit[0] = -100; 49 crit[1] = -CointStat(y[:t0][], x[:t0][], lags); 53 crit[2] = -CointStat(y[t0+1:][], x[t0+1:][], lags); 25 minmin = limits(crit'); 25 if (minmin[3][0] == 0) 41 sSample[0] = (1+ cStart)~(n + cStart); 29 else if (minmin[3][0] == 1) 43 sSample[0] = (1 + cStart)~(t0 + cStart); 51 else sSample[0] = (t0 + cStart + 1)~(n + cStart); 2 } 5 else 24 { crit = zeros(2,n-t0); 14 if (rolling) 39 { for (decl t = 0; t < n-t0; t += 5) 67 { crit[0][t] = -CointStat(y[t:t+t0-1][], x[t:t+t0-1][], lags); } 26 minmin = limits(crit'); 46 sSample[0] = int(minmin[3][0] + 1 + cStart) 40 ~int(minmin[3][0] + t0 + cStart); 5 } 7 else 35 { for (decl t = t0; t minmin[1][1]) 67 sSample[0] = (1 + cStart)~int(minmin[3][0] + t0 + 1 + cStart); 72 else sSample[0] = int(minmin[3][1] + 1 + cStart + t0)~(n + cStart); 4 } } 34 if (rolling) return minmin[1][0]; 46 else return max(minmin[1][0], minmin[1][1]); 1 } 0 82 Do_MinTests(const tests, const y, const x, const lags, const cStart, const Sample) 3 { 38 decl init = 0, rolling = 0, star = 0; 15 if (tests > 1) 23 { if (tests > 4) 28 { if (tests > 5) star = 1; 15 rolling = 1; 14 init = 0.5; 4 } 6 else 30 { if (tests > 3) init = 0.2; 35 else if (tests > 2) init = 0.35; 19 else init = 0.5; 4 } } 5 else 16 { rolling = -1; 26 if (tests > 0) star = 1; 2 } 13 decl sample; 75 decl stat = MinMin_Test(y, x, lags, init, rolling, star, cStart, &sample); 20 Sample[0] = sample; 13 return stat; 1 } 2 57 Run_MinTests(const vParam, const mcDataset, const cStart, 52 const cEnd, const cTests, const Norm, const bMode) 1 { 12 decl crit; 11 decl lags; 21 decl tests = cTests; 24 if (tests > 7) // ADF 37 { if (!INFO_CRIT) lags = REGR1_LAGS; 40 else lags = (cEnd - cStart + 1)^(1/3); 13 tests -= 8; 2 } 22 else lags = -1; //PP 12 if (!bMode) 47 { decl regs = columns(REGRESSORS_1)*2 + TREND; 17 if (tests == 7) 41 { crit = constant(-1, 1, 9)|zeros(5,9); 44 crit[1:][:6] = (CRITVALS[regs][1:][])'; 40 crit[1:][7] = (CRITVALS[regs][0][])'; 42 crit [1:][8] = (CRITVALS[TREND][0][])'; 3 } 7 else 41 { crit = constant(-1, 1, 3)|zeros(5,3); 46 crit[1:][0] = (CRITVALS[regs][tests+1][])'; 40 crit[1:][1] = (CRITVALS[regs][0][])'; 41 crit[1:][2] = (CRITVALS[TREND][0][])'; 3 } 42 UserStore({cTests, crit, cStart, cEnd}); 2 } 27 decl stat = 0, y, yy = <>; 37 decl nvar = 1+columns(REGRESSORS_1); 38 decl x = ones(cEnd - cStart + 1, 1); 41 if (TREND) x ~= range(1, cEnd-cStart+1); 47 decl best, lim, loc, pretest = zeros(nvar, 1); 16 if (vParam[0]) 69 { yy = mcDataset[cStart:cEnd][VarNum(SERIES)~VarNum(REGRESSORS_1)]; 33 for (decl j = 0; j < nvar; j++) 47 pretest[j] = -CointStat(yy[][j], x, lags); 24 lim = limits(pretest); 17 best = lim[0]; 15 loc = lim[2]; 29 for (decl k=0; k; 26 for (decl j=0; j<7; j++) 55 stat ~= Do_MinTests(j, y, x, lags, cStart, &sample); 2 } 61 else stat = Do_MinTests(tests, y, x, lags, cStart, &sample); 32 stat ~= -CointStat(y, x, lags); 14 stat ~= best; 12 if (!bMode) 14 { decl names; 17 if (tests == 7) 54 { if (lags == -1) names = UserStatistic_Names()[:6]; 44 else names = UserStatistic_Names()[8:14]; 3 } 50 else names = { UserStatistic_Names()[cTests] }; 44 if (lags != -1) names ~= "ADF Statistic" ; 32 else names ~= "PP Statistic" ; 29 names ~= "I(1) test (PP)"; 71 if (tests < 7) names ~= sprint("Sample ", sample[0], "-", sample[1]); 23 return {stat, names}; 2 } 18 else return stat; 3 } 0 67 MinMinTest(const vParam, const mcDataset, const cStart, const cEnd, 27 const aName, const bMode) 1 { 66 decl start, end, crit = 0, test = 0, stat, names, type, norm = 0; 40 decl statnames = UserStatistic_Names(); 25 decl j, name = aName[0]; 29 decl cache = UserRetrieve(); 31 if (!isarray(cache) || !bMode) 39 { for (j=0; j columns(statnames)) 60 { PrintCall(1, "Test name ", aName[0], " not recognised"); 10 return; 4 } } 5 else 19 { test = cache[0]; 18 crit = cache[1]; 19 start = cache[2]; 17 end = cache[3]; 2 } 17 INTERCEPT_1 = 1; 12 if (!bMode) 2 { 38 decl varnames = SERIES~REGRESSORS_1; 53 decl lims = CheckData(mcDataset, VarNum(varnames)); 31 start = max(cStart, lims[0]); 27 end = min(cEnd, lims[1]); 19 if (end <= start) 31 { PrintCall(1, "No Sample!"); 10 return; 4 } 49 [stat, names] = Run_MinTests(vParam, mcDataset, 38 start, end, test, &norm, bMode); 51 type = sprint(" for ", varnames[norm], " on\n"); 38 for (j=0; j}; 0 2 2 X1 2 X2 3 4 beta 5 gamma 4 Smpl 0 0 0 0 0 0 0 0 0 2 2 X1 2 X2 0 0 0 0 2 2 X1 2 X2 1 2 X1 0 0 0 0 0 0 0 0 0 0 0 1 56 X2 - beta*X1 + gamma*(neg(pos(Trend - u#*Smpl) - 1) + 1) 0 0 0 1 1 3 1 10 200 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 3 0 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 2 1 1 0.2 1 1 -0.2 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 2 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 2 0 0 1 1 2 0 2 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 2 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 2 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 2 0 0 1 1 2 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 30 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 1 1 0 0 0 5 22 1 11 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 14 2 0 0 0 0 0 0 0 1 1 0 2 0 0 0 1 0 0 0 0 10 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 5 1 178 0 0 0 0 0 0 0 0 1 1 99 0 2 0 1 3 1 0 0 -1 2 1 1 0 1 0 1 1 0 50 200 0 0 0 0 0 0 0 0 12 0 0 0 2 0 0 0 1 0 10 5 0 1 1 0 0 0 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 -1 0 0 1000 1 0 0 0 0 0 0 0 50 0 2 0 12 0 0 0 0 0 0 0 0 0 1 2 0 0 1 -1 2 0 0 1 0 1 -1 0 1 0 0 0 0 -1 0 0 0 1 1 0 0 0 7 0 1 0 0 0 0 0 0 0 2 0 1 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 -2147483648 0 0 0 0 0 0 0 1 0 1 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 1 1 1e-005 1 2 1 1 1 4 500 5 0.85 10 0 0 1 2 1 0.66 1 4 0.5 0.5 0 0 34 11 1 1 14 minmindata.xls 22 2_Intercept_Breaks.tsd 18 2_Intercept_Breaks 0 0 0 0 610 Sample Settings: START_SAMPLE = 1; END_SAMPLE = 200; Model Settings: CODED_EQUATIONS = {"X2 - beta*X1 + gamma*abs(neg(pos(Trend - u#*Smpl) - 1) - neg(pos(Trend - u#*Smpl) - 1))"}; CORRELOGRAM_ORDER = 1; COVMAT_TYPE = 0; DIFFERENCING = 1; ECM_TERMS = 1; EQUIL_VARIABLES = {"X1","X2"}; INPUT_FILE = "minmindata.xls"; INTERCEPT_2 = 1; IS_ARFIMA = 1; IS_ECM = 1; LISTING_FILE = "2_Intercept_Breaks.tsd"; METHOD = LSQ; SERIES = {"X1","X2"}; SIM_DISTRIBUTION = 2; SYSTEM = 1; VECM_TYPE = 2; Simulation Settings: Parameter Values: FUNCTION_START_VALUES = {<1, 10, 200>}; 0 2 2 X1 2 X2 3 4 beta 5 gamma 4 Smpl 0 0 0 0 0 0 0 0 0 2 2 X1 2 X2 0 0 0 0 2 2 X1 2 X2 1 2 X1 0 0 0 0 0 0 0 0 0 0 0 1 87 X2 - beta*X1 + gamma*abs(neg(pos(Trend - u#*Smpl) - 1) - neg(pos(Trend - u#*Smpl) - 1)) 0 0 0 1 1 3 1 10 200 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 3 0 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 2 1 1 0.2 1 1 -0.2 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 2 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 2 0 0 1 1 2 0 2 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 2 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 2 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 2 0 0 1 1 2 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 30 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 1 1 0 0 0 5 22 1 11 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 14 2 0 0 0 0 0 0 0 1 1 0 2 0 0 0 1 0 0 0 0 10 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 5 1 178 0 0 0 0 0 0 0 0 1 1 99 0 2 0 1 3 1 0 0 -1 2 1 1 0 1 0 1 1 0 50 200 0 0 0 0 0 0 0 0 12 0 0 0 2 0 0 0 1 0 10 5 0 1 1 0 0 0 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 -1 0 0 1000 1 0 0 0 0 0 0 0 50 0 2 0 12 0 0 0 0 0 0 0 0 0 1 2 0 0 1 -1 2 0 0 1 0 1 -1 0 1 0 0 0 0 -1 0 0 0 1 1 0 0 0 7 0 1 0 0 0 0 0 0 0 2 0 1 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 -2147483648 0 0 0 0 0 0 0 1 0 1 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 1 1 1e-005 1 2 1 1 1 4 500 5 0.85 10 0 0 1 2 1 0.66 1 4 0.5 0.5 0 0 34 11 1 1 14 minmindata.xls 17 1_Slope_Break.tsd 13 1_Slope_Break 0 0 0 0 576 Sample Settings: START_SAMPLE = 1; END_SAMPLE = 200; Model Settings: CODED_EQUATIONS = {"X2 - beta*X1 - gamma*X1*(neg(pos(Trend - u#*Smpl) - 1) + 1)"}; CORRELOGRAM_ORDER = 1; COVMAT_TYPE = 0; DIFFERENCING = 1; ECM_TERMS = 1; EQUIL_VARIABLES = {"X1","X2"}; INPUT_FILE = "minmindata.xls"; INTERCEPT_2 = 1; IS_ARFIMA = 1; IS_ECM = 1; LISTING_FILE = "1_Slope_Break.tsd"; METHOD = LSQ; SERIES = {"X1","X2"}; SIM_DISTRIBUTION = 2; SYSTEM = 1; VECM_TYPE = 2; Simulation Settings: Parameter Values: FUNCTION_START_VALUES = {<1, 1, 200>}; 0 2 2 X1 2 X2 3 4 beta 5 gamma 4 Smpl 0 0 0 0 0 0 0 0 0 2 2 X1 2 X2 0 0 0 0 2 2 X1 2 X2 1 2 X1 0 0 0 0 0 0 0 0 0 0 0 1 59 X2 - beta*X1 - gamma*X1*(neg(pos(Trend - u#*Smpl) - 1) + 1) 0 0 0 1 1 3 1 1 200 1 1 3 0 0 0 1 1 3 0 0 0 1 1 3 0 0 0 1 1 1 1 0 1 1 3 0 1 2 2 1 1 0 1 1 0 2 1 1 0 1 1 0 2 1 1 0 1 1 0 2 1 1 0 1 1 0 2 1 1 1 0 1 1 1 0 2 1 1 3 1 1 4 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 2 1 1 0.2 1 1 -0.2 2 1 1 0 1 1 0 2 1 1 0 1 1 0 2 1 1 0 1 1 0 2 1 1 1 0 1 1 1 0 2 1 1 5 1 1 6 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 2 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 2 0 0 1 1 2 0 2 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 2 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 2 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 2 0 0 1 1 2 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 30 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 1 1 0 0 0 5 22 1 11 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 14 2 0 0 0 0 0 0 0 1 1 0 2 0 0 0 1 0 0 0 0 10 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 5 1 178 0 0 0 0 0 0 0 0 1 1 99 0 2 0 1 3 1 0 0 -1 2 1 1 0 1 0 1 1 0 50 200 0 0 0 0 0 0 0 0 12 0 0 0 2 0 0 0 1 0 10 5 0 1 1 0 0 0 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 -1 0 0 1000 1 0 0 0 0 0 0 0 50 0 2 0 12 0 0 0 0 0 0 0 0 0 1 2 0 0 1 -1 2 0 0 1 0 1 -1 0 1 0 0 0 0 -1 0 0 0 1 1 0 0 0 7 0 1 0 0 0 0 0 0 0 2 0 1 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 -2147483648 0 0 0 0 0 0 0 1 0 1 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 1 1 1e-005 1 2 1 1 1 4 500 5 0.85 10 0 0 1 2 1 0.66 1 4 0.5 0.5 0 0 34 11 1 1 14 minmindata.xls 18 NonCoint_Break.tsd 14 NonCoint_Break 0 0 0 0 563 Sample Settings: START_SAMPLE = 1; END_SAMPLE = 200; Model Settings: CODED_EQUATIONS = {"-(X2 - beta*X1)*neg(pos(Trend - u#*Smpl) - 1)"}; CORRELOGRAM_ORDER = 1; COVMAT_TYPE = 0; DIFFERENCING = 1; ECM_TERMS = 1; EQUIL_VARIABLES = {"X1","X2"}; INPUT_FILE = "minmindata.xls"; INTERCEPT_2 = 1; IS_ARFIMA = 1; IS_ECM = 1; LISTING_FILE = "NonCoint_Break.tsd"; METHOD = LSQ; SERIES = {"X1","X2"}; SIM_DISTRIBUTION = 2; SYSTEM = 1; VECM_TYPE = 2; Simulation Settings: Parameter Values: FUNCTION_START_VALUES = {<1, 1, 200>}; 0 2 2 X1 2 X2 3 4 beta 5 gamma 4 Smpl 0 0 0 0 0 0 0 0 0 2 2 X1 2 X2 0 0 0 0 2 2 X1 2 X2 1 2 X1 0 0 0 0 0 0 0 0 0 0 0 1 45 -(X2 - beta*X1)*neg(pos(Trend - u#*Smpl) - 1) 0 0 0 1 1 3 1 1 200 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 3 0 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 2 1 1 0.2 1 1 -0.2 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 2 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 2 0 0 1 1 2 0 2 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 2 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 2 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 2 0 0 1 1 2 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 30 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 1 1 0 0 0 5 22 1 11 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 14 2 0 0 0 0 0 0 0 1 1 0 2 0 0 0 1 0 0 0 0 10 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 5 1 178 0 0 0 0 0 0 0 0 1 1 99 0 0 0 1 3 1 0 0 -1 2 1 1 0 0 0 1 0 0 50 200 0 0 0 0 0 0 0 0 17 0 0 0 2 0 0 0 1 0 10 5 0 1 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 -1 0 0 1000 1 0 0 0 0 0 0 0 50 0 2 0 12 0 0 0 0 0 0 0 0 0 1 1 0 0 1 -1 2 0 0 0 0 1 -1 0 1 0 0 0 0 -1 0 0 0 1 1 0 0 0 7 0 1 0 0 0 0 0 0 0 2 0 1 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 -2147483648 0 0 0 0 0 0 0 1 0 1 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 1 1 1e-005 1 2 1 1 1 4 500 5 0.85 10 0 0 1 2 1 0.66 1 4 0.5 0.5 0 0 34 11 14 All PP-Q Tests 1 14 minmindata.xls 21 Run_SubsampleTest.tsd 17 Run_SubsampleTest 0 0 0 0 493 Sample Settings: START_SAMPLE = 1; END_SAMPLE = 200; Model Settings: CORRELOGRAM_ORDER = 1; COVMAT_TYPE = 0; FUNCTION_HEADING = "All PP-Q Tests"; TEST_HEADING = " "; CODING_TYPE = 7; FUNCTION_NAMES = {"Best normalization"}; INPUT_FILE = "minmindata.xls"; INTERCEPT_1 = 1; IS_FUNCTION = 1; LISTING_FILE = "Run_SubsampleTest.tsd"; METHOD = LSQ; REGRESSORS_1 = {"X2"}; SERIES = {"X1"}; Simulation Settings: Parameter Values: FUNCTION_START_VALUES = {<1>}; 0 0 1 18 Best normalization 0 0 0 0 0 1 2 X2 0 0 0 1 2 X1 0 0 0 0 1 2 X1 1 2 X1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 2 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 2 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 2 0 2 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 2 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 2 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 30 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 1 1 0 0 0 5 22 1 11 1 0 1 0 0 0 0 0 1 0 1 1 1 0 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 14 2 0 0 0 0 0 0 0 1 1 1 2 X1 2 0 0 3 18 Best normalization 9 Intercept 2 X2 1 0 0 0 0 10 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 5 1 178 0 0 0 0 0 0 0 0 1 1 99 0 0 0 1 3 1 0 0 -1 2 1 1 0 0 0 1 0 0 50 200 0 0 0 0 0 0 0 0 17 0 0 0 2 0 0 0 1 0 10 5 0 1 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 -1 0 0 1000 1 0 0 0 0 0 0 0 50 0 2 0 12 0 0 0 0 0 0 0 0 0 1 1 0 0 1 -1 2 0 0 0 0 1 -1 0 1 0 0 0 0 -1 0 0 0 1 1 0 0 0 7 0 1 0 0 0 0 0 0 0 2 0 1 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 -2147483648 0 0 0 0 0 0 0 1 0 1 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 1 1 1e-005 1 2 1 1 1 4 500 5 0.85 10 0 0 1 2 1 0.66 1 4 0.5 0.5 0 0 34 11 14 All PP-Q Tests 1 14 minmindata.xls 17 SubsampleTest.tsd 13 SubsampleTest 0 0 0 0 489 Sample Settings: START_SAMPLE = 1; END_SAMPLE = 200; Model Settings: CORRELOGRAM_ORDER = 1; COVMAT_TYPE = 0; FUNCTION_HEADING = "All PP-Q Tests"; TEST_HEADING = " "; CODING_TYPE = 7; FUNCTION_NAMES = {"Best normalization"}; INPUT_FILE = "minmindata.xls"; INTERCEPT_1 = 1; IS_FUNCTION = 1; LISTING_FILE = "SubsampleTest.tsd"; METHOD = LSQ; REGRESSORS_1 = {"X2"}; SERIES = {"X1"}; Simulation Settings: Parameter Values: FUNCTION_START_VALUES = {<1>}; 0 0 1 18 Best normalization 0 0 0 0 0 1 2 X2 0 0 0 1 2 X1 0 0 0 0 1 2 X1 1 2 X1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 2 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 2 0 0 1 1 2 0 2 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 2 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 2 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 2 0 0 1 1 2 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1 1 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