Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!
Multiple Linear Regression - Estimated Regression Equation | JapanRES[t] = -0.216606458736063 -0.0300696870655007VSARES[t] + e[t] |
Multiple Linear Regression - Regression Statistics | Multiple R | 0.0797986780766265 | R-squared | 0.00636782902277707 | Adjusted R-squared | 0.00383950288797508 | F-TEST (value) | 2.51859478693223 | F-TEST (DF numerator) | 1 | F-TEST (DF denominator) | 393 | p-value | 0.113315017829027 | Multiple Linear Regression - Residual Statistics | Residual Standard Deviation | 2.00330172425999 | Sum Squared Residuals | 1577.19459478025 |
Multiple Linear Regression - Actuals, Interpolation, and Residuals | Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error | 1 | 0.122359934 | -0.22738914201743 | 0.34974907601743 | 2 | 0.931905548 | -0.343335512131571 | 1.27524106013157 | 3 | -0.055775589 | -0.229252203357137 | 0.173476614357137 | 4 | 1.468985837 | -0.307829012149846 | 1.77681484914985 | 5 | 1.055123159 | -0.199751666337576 | 1.25487482533758 | 6 | 0.944996502 | -0.13125837567785 | 1.07625487767785 | 7 | 1.068458953 | -0.108597406625732 | 1.17705635962573 | 8 | 0.316141818 | -0.0210389224012296 | 0.337180740401230 | 9 | 0.760538937 | -0.367970004030011 | 1.12850894103001 | 10 | -2.20217258 | -0.271404019985210 | -1.93076856001479 | 11 | -0.131541265 | -0.167172834171097 | 0.0356315691710969 | 12 | -0.532224765 | -0.356041698507546 | -0.176183066492454 | 13 | -1.741321669 | -0.294961607958547 | -1.44636006104145 | 14 | 0.204726282 | -0.288015636539102 | 0.492741918539102 | 15 | -0.147865278 | -0.193328863781815 | 0.0454635857818152 | 16 | 1.396025769 | -0.210964918761031 | 1.60699068776103 | 17 | 0.162345476 | -0.051943493065177 | 0.214288969065177 | 18 | 0.015665853 | -0.260122061754754 | 0.275787914754754 | 19 | 0.217672981 | -0.0601143379719736 | 0.277787318971974 | 20 | -0.917486426 | -0.131646306454724 | -0.785840119545276 | 21 | -0.903483082 | -0.126945681568112 | -0.776537400431888 | 22 | 0.37057944 | -0.321163446873189 | 0.69174288687319 | 23 | -1.528186024 | -0.214357120963456 | -1.31382890303654 | 24 | 1.425442977 | -0.194141894114908 | 1.61958487111491 | 25 | 0.823776349 | -0.219239265695725 | 1.04301561469573 | 26 | 1.16416148 | -0.268252228388888 | 1.43241370838889 | 27 | 0.548322968 | -0.224577706322314 | 0.772900674322314 | 28 | -0.145197568 | -0.24613004895184 | 0.10093248095184 | 29 | 0.144016721 | -0.237185762890617 | 0.381202483890617 | 30 | -0.008729414 | -0.239025845516722 | 0.230296431516722 | 31 | -0.044488825 | -0.00439672335182881 | -0.0400921016481712 | 32 | 2.265085141 | -0.176325022499736 | 2.44141016349974 | 33 | -2.30024615 | -0.397994164657135 | -1.90225198534287 | 34 | -0.802974595 | -0.316828742173495 | -0.486145852826505 | 35 | 1.905081291 | -0.228256379456512 | 2.13333767045651 | 36 | 0.141275448 | -0.201794995571518 | 0.343070443571518 | 37 | -0.274557318 | -0.183092154816196 | -0.0914651631838045 | 38 | -1.089390275 | -0.246266061212814 | -0.843124213787186 | 39 | -0.106056668 | -0.0909714045142252 | -0.0150852634857748 | 40 | 0.153839662 | -0.243963159314338 | 0.397802821314338 | 41 | 1.378581549 | 0.0109119886662892 | 1.36766956033371 | 42 | 0.984071836 | -0.308020603175653 | 1.29209243917565 | 43 | -1.093840675 | 0.0425505045520964 | -1.13639117955210 | 44 | -2.335855288 | -0.0669952351606646 | -2.26886005283934 | 45 | 0.019620673 | -0.224709462369432 | 0.244330135369432 | 46 | -0.773231639 | -0.111294264725115 | -0.661937374274885 | 47 | 0.531269746 | -0.269409617469859 | 0.800679363469859 | 48 | -3.130460456 | -0.275577994599908 | -2.85488246140009 | 49 | -1.063832741 | 0.0900030785350674 | -1.15383581953507 | 50 | 1.864410977 | -0.332884767915228 | 2.19729574491523 | 51 | -1.765416679 | -0.305589910309204 | -1.45982676869080 | 52 | -0.680077954 | -0.390596052613286 | -0.289481901386714 | 53 | -0.465236897 | -0.0281629912548289 | -0.437073905745171 | 54 | 2.087199736 | -0.196597497431795 | 2.28379723343179 | 55 | -3.08370281 | -0.227291037829129 | -2.85641177217087 | 56 | -0.865874128 | -0.0823358246253644 | -0.783538303374636 | 57 | 0.852274105 | -0.107257117049422 | 0.959531222049422 | 58 | -2.170886194 | -0.24415428471104 | -1.92673190928896 | 59 | -0.42542584 | -0.393785618863078 | -0.0316402211369219 | 60 | -6.885803565 | -0.301384829234701 | -6.5844187357653 | 61 | 0.797287754 | -0.257442047310859 | 1.05472980131086 | 62 | 2.743745431 | -0.321017820852146 | 3.06476325185215 | 63 | -1.351253289 | -0.192588358256328 | -1.15866493074367 | 64 | 2.723775248 | -0.282806950250634 | 3.00658219825063 | 65 | -0.265752017 | -0.134199280379826 | -0.131552736620174 | 66 | 0.628595851 | 0.0367359509046242 | 0.591859900095376 | 67 | 1.685366026 | -0.354227153555481 | 2.03959317955548 | 68 | -3.512413068 | -0.211696780935390 | -3.30071628706461 | 69 | 0.213871684 | -0.369848326307228 | 0.583720010307228 | 70 | 3.188286472 | -0.254872928068680 | 3.44315940006868 | 71 | 0.271504185 | -0.369985495048366 | 0.641489680048366 | 72 | -0.398480607 | -0.0825592100753474 | -0.315921396924653 | 73 | -0.968286123 | -0.206358694890551 | -0.76192742810945 | 74 | -0.812342823 | 0.0305730938173775 | -0.842915916817378 | 75 | 1.714817333 | -0.132245881671569 | 1.84706321467157 | 76 | -2.910379004 | -0.284157769629959 | -2.62622123437004 | 77 | -0.863810554 | -0.220936356886488 | -0.642874197113512 | 78 | -0.211153485 | -0.315660833302714 | 0.104507348302714 | 79 | -1.471741777 | -0.246681197599199 | -1.2250605794008 | 80 | 1.321517564 | -0.185555813291062 | 1.50707337729106 | 81 | 0.2632073 | -0.491784046663232 | 0.754991346663232 | 82 | -3.586108009 | -0.273805602928865 | -3.31230240607113 | 83 | 3.524638387 | -0.099828667487806 | 3.62446705448781 | 84 | 2.073869999 | -0.211685025431789 | 2.28555502443179 | 85 | -1.349714486 | -0.162481882702814 | -1.18723260329719 | 86 | 0.647566188 | -0.193355812896896 | 0.840922000896896 | 87 | 1.610373405 | -0.255460599418089 | 1.86583400441809 | 88 | -0.205131216 | -0.259610355966964 | 0.0544791399669639 | 89 | 3.117976023 | -0.155198260842119 | 3.27317428384212 | 90 | 1.983811507 | -0.317842942110931 | 2.30165444911093 | 91 | -0.574764692 | -0.284090666597419 | -0.290674025402581 | 92 | 2.005568836 | -0.206628921966884 | 2.21219775796688 | 93 | 1.261019639 | -0.24263282638274 | 1.50365246538274 | 94 | -0.024939629 | -0.326368456961996 | 0.301428827961996 | 95 | -0.237549844 | -0.273935803170375 | 0.0363859591703745 | 96 | 0.211769735 | -0.300729344065488 | 0.512499079065488 | 97 | -0.068297898 | 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1.25483892582862 | 211 | 2.715238152 | -0.302581686975031 | 3.01781983897503 | 212 | 1.590584938 | -0.0672553499816557 | 1.65784028798166 | 213 | -1.901196912 | -0.073791715341351 | -1.82740519665865 | 214 | 2.168241623 | -0.117610598848280 | 2.28585222184828 | 215 | -3.291258516 | -0.597936796768743 | -2.69332171923126 | 216 | 2.431989331 | -0.302408061564877 | 2.73439739256488 | 217 | 1.379624088 | -0.277995193092893 | 1.65761928109289 | 218 | 1.660957909 | -0.159873550844660 | 1.82083145984466 | 219 | 0.803376279 | -0.238924735502361 | 1.04230101450236 | 220 | 0.635350876 | -0.180350785431070 | 0.81570166143107 | 221 | -1.935088352 | -0.111310956468545 | -1.82377739553146 | 222 | -1.364117462 | -0.227795063668494 | -1.13632239833151 | 223 | -1.288076636 | -0.0279741545522181 | -1.26010248144778 | 224 | 2.763030557 | -0.149321368884440 | 2.91235192588444 | 225 | -1.850948021 | -0.244301818353031 | -1.60664620264697 | 226 | -0.426026229 | -0.356257304087534 | -0.069768924912466 | 227 | 1.416557529 | -0.277027867347278 | 1.69358539634728 | 228 | 1.054518042 | -0.478187586490981 | 1.53270562849098 | 229 | 0.100735979 | -0.17144009379556 | 0.27217607279556 | 230 | 3.01821026 | -0.119804057670367 | 3.13801431767037 | 231 | -0.582956862 | -0.225751302152730 | -0.357205559847270 | 232 | -0.465121784 | -0.359723061555671 | -0.105398722444329 | 233 | -1.278449674 | -0.344594971231727 | -0.933854702768273 | 234 | 3.852168919 | -0.248902423491996 | 4.101071342492 | 235 | 1.516127833 | -0.193563042415204 | 1.70969087541520 | 236 | 0.679014194 | -0.209886372189059 | 0.88890056618906 | 237 | -1.187703382 | -0.47489905100813 | -0.71280433099187 | 238 | 1.025403734 | -0.207541644979638 | 1.23294537897964 | 239 | 0.921134292 | -0.264954631917025 | 1.18608892391702 | 240 | 0.473279558 | -0.149383744180362 | 0.622663302180362 | 241 | 1.218773055 | -0.238110620645865 | 1.45688367564586 | 242 | -1.967712268 | -0.153487456761475 | -1.81422481123852 | 243 | -0.284285615 | -0.264541632553229 | -0.0197439824467711 | 244 | -0.739335622 | -0.231235486763745 | -0.508100135236255 | 245 | 0.177297096 | -0.276504413513169 | 0.453801509513169 | 246 | 0.617738343 | -0.316412391633285 | 0.934150734633285 | 247 | 1.963704879 | -0.229686538998157 | 2.19339141799816 | 248 | 1.770795897 | -0.251565292651371 | 2.02236118965137 | 249 | 0.862954668 | -0.0803991381079238 | 0.943353806107924 | 250 | 0.736632082 | 0.0263104720813079 | 0.710321609918692 | 251 | -1.57670801 | -0.191850265823228 | -1.38485774417677 | 252 | -0.757226618 | -0.0692308721614748 | -0.687995745838525 | 253 | 0.459526319 | -0.194020200046615 | 0.653546519046615 | 254 | -2.740544672 | -0.262546590545842 | -2.47799808145416 | 255 | 0.642873603 | -0.338414378381516 | 0.981287981381516 | 256 | -1.942390074 | -0.345378648709025 | -1.59701142529098 | 257 | 0.969546937 | -0.265922559747983 | 1.23546949674798 | 258 | 2.916260999 | -0.153427547089684 | 3.06968854608968 | 259 | 2.804886635 | -0.180081804592917 | 2.98496843959292 | 260 | -1.177267984 | -0.197431552820850 | -0.97983643117915 | 261 | 1.442031421 | -0.336237158212813 | 1.77826857921281 | 262 | -1.008279712 | 0.105911177052674 | -1.11419088905267 | 263 | 1.685048733 | -0.153422238466851 | 1.83847097146685 | 264 | -3.575122053 | -0.268127569118684 | -3.30699448388132 | 265 | -3.36169991 | -0.0997224100542042 | -3.26197749994580 | 266 | 0.544584549 | -0.264213203402493 | 0.808797752402493 | 267 | -3.812968258 | -0.382722716107807 | -3.43024554189219 | 268 | -0.255478375 | -0.272951361698743 | 0.0174729866987429 | 269 | 2.36107515 | -0.124826067823381 | 2.48590121782338 | 270 | 0.231398527 | -0.0993962210951547 | 0.330794748095155 | 271 | 0.961587017 | -0.213197051891556 | 1.17478406889156 | 272 | -2.766406177 | 0.0198432885618289 | -2.78624946556183 | 273 | -1.889330686 | -0.257240587594175 | -1.63209009840582 | 274 | -1.722863291 | -0.108404245932190 | -1.61445904506781 | 275 | -0.492311851 | -0.280393642173762 | -0.211918208826238 | 276 | -0.378296018 | 0.110756873144533 | -0.489052891144533 | 277 | -0.661148328 | -0.128832452916209 | -0.532315875083791 | 278 | -0.391163065 | -0.174573053107709 | -0.216590011892291 | 279 | -0.147328808 | -0.252728450571735 | 0.105399642571735 | 280 | -0.155178326 | -0.00799634884162423 | -0.147181977158376 | 281 | -1.410346167 | -0.251894438693516 | -1.15845172830648 | 282 | -1.656752775 | -0.214310165102364 | -1.44244260989764 | 283 | -0.498316342 | -0.177742888861706 | -0.320573453138294 | 284 | 0.635940123 | -0.314781364898141 | 0.95072148789814 | 285 | -2.533052084 | -0.0456667686883443 | -2.48738531531166 | 286 | 0.066688919 | -0.217181466266833 | 0.283870385266833 | 287 | 0.010332456 | -0.0578719407341868 | 0.0682043967341868 | 288 | 0.100325395 | -0.147060462671756 | 0.247385857671756 | 289 | -0.159212102 | -0.0564843684392878 | -0.102727733560712 | 290 | -1.118768301 | -0.430122409869871 | -0.688645891130129 | 291 | -0.360506506 | -0.312317240764100 | -0.0481892652358996 | 292 | -0.794671339 | -0.282443369184673 | -0.512227969815327 | 293 | -1.82637031 | -0.227521505291809 | -1.59884880470819 | 294 | 0.020284413 | -0.317039220420793 | 0.337323633420793 | 295 | 2.059692348 | -0.325096187663962 | 2.38478853566396 | 296 | 1.01468765 | -0.0743835472830106 | 1.08907119728301 | 297 | 3.219689867 | -0.220220843210082 | 3.43991071021008 | 298 | -2.433588329 | -0.0266781413572552 | -2.40691018764274 | 299 | -2.372306799 | -0.421795916655193 | -1.95051088234481 | 300 | -0.37056341 | -0.413340883346428 | 0.0427774733464281 | 301 | 1.266050442 | -0.163432530567125 | 1.42948297256713 | 302 | 0.824592148 | -0.167007211266900 | 0.9915993592669 | 303 | -1.852999286 | -0.0828740515764494 | -1.77012523442355 | 304 | -3.161642025 | -0.436243824252871 | -2.72539820074713 | 305 | 0.442927211 | -0.339479616500899 | 0.7824068275009 | 306 | -0.428942112 | -0.156591757376555 | -0.272350354623445 | 307 | -1.310961025 | -0.533886234986776 | -0.777074790013224 | 308 | 1.014820082 | -0.347178352616690 | 1.36199843461669 | 309 | -0.305111502 | -0.241286089173921 | -0.0638254128260788 | 310 | 0.018050147 | -0.215938934235474 | 0.233989081235474 | 311 | -2.696506438 | -0.240049705220849 | -2.45645673277915 | 312 | -1.10764417 | -0.35425947044047 | -0.75338469955953 | 313 | 1.141463332 | -0.127621872062304 | 1.26908520406230 | 314 | 0.159323081 | -0.129178890080856 | 0.288501971080856 | 315 | -2.055277082 | -0.228291471653339 | -1.82698561034666 | 316 | 0.202725995 | -0.177851828661742 | 0.380577823661742 | 317 | -0.3671714 | -0.330514775643549 | -0.0366566243564509 | 318 | -0.056145558 | -0.0961548900895674 | 0.0400093320895674 | 319 | -0.897022272 | -0.305465082047359 | -0.591557189952641 | 320 | -0.261083776 | -0.259664242409809 | -0.00141953359019098 | 321 | -0.651847454 | -0.359323548205966 | -0.292523905794034 | 322 | -0.339317343 | -0.129591061415679 | -0.209726281584321 | 323 | 3.954885606 | -0.275093975416345 | 4.22997958141634 | 324 | -2.972657905 | -0.183819286667942 | -2.78883861833206 | 325 | 0.291575514 | -0.233099850842574 | 0.524675364842574 | 326 | 0.387896939 | -0.00150984897614997 | 0.38940678797615 | 327 | 0.061920658 | -0.274053525423912 | 0.335974183423912 | 328 | -4.019270705 | -0.388038209443618 | -3.63123249555638 | 329 | 2.799936975 | -0.0212905800926642 | 2.82122755509266 | 330 | -0.961852566 | -0.290316746975832 | -0.671535819024168 | 331 | -0.689420887 | -0.398658712860098 | -0.290762174139902 | 332 | -0.528085855 | -0.177483319865674 | -0.350602535134326 | 333 | 0.088821319 | 0.208152191727730 | -0.119330872727730 | 334 | -3.596177355 | -0.264721436833030 | -3.33145591816697 | 335 | -1.464790191 | 0.218117068617400 | -1.6829072596174 | 336 | 0.759085451 | -0.372638115947205 | 1.13172356694720 | 337 | -3.392063692 | -0.559746817773922 | -2.83231687422608 | 338 | 2.383755557 | 0.0763047088556159 | 2.30745084814438 | 339 | 3.773197159 | 0.0249627455497148 | 3.74823441345029 | 340 | 0.17271416 | -0.123899685655277 | 0.296613845655277 | 341 | -1.373664483 | -0.355909711946207 | -1.01775477105379 | 342 | -0.554515625 | -0.282171648757471 | -0.272343976242529 | 343 | -0.616708159 | 0.364256423722298 | -0.980964582722298 | 344 | 0.338243237 | -0.654268818388669 | 0.992512055388669 | 345 | -4.969501265 | -0.269328666955519 | -4.70017259804448 | 346 | -1.459226067 | -0.0619383550558825 | -1.39728771194412 | 347 | -3.458393406 | -0.0179489255453813 | -3.44044448045462 | 348 | -0.708571911 | -0.0099092115068391 | -0.698662699493161 | 349 | -0.967100647 | 0.327538223302014 | -1.29463887030201 | 350 | -2.764883729 | -0.0195568418616956 | -2.74532688713830 | 351 | -6.358399349 | 0.379945690994377 | -6.73834503999438 | 352 | -1.216802997 | -0.0478186426367044 | -1.16898435436330 | 353 | -7.378604269 | -0.810998811997552 | -6.56760545700245 | 354 | 6.326253049 | -0.383179636940433 | 6.70943268594043 | 355 | 2.244553469 | 0.316747877572778 | 1.92780559142722 | 356 | 1.064937768 | -0.370346025544574 | 1.43528379354457 | 357 | -5.865037234 | -0.181412615452120 | -5.68362461854788 | 358 | -2.548316402 | -0.681602550025991 | -1.86671385197401 | 359 | 1.936100071 | -0.123254992406544 | 2.05935506340654 | 360 | -0.678052689 | 0.0632473218555839 | -0.741300010855584 | 361 | -0.197814711 | -0.182230281027475 | -0.0155844299725250 | 362 | -2.763460164 | -0.0615452479627985 | -2.7019149160372 | 363 | -0.426954939 | -0.091974604839854 | -0.334980334160146 | 364 | -6.333456722 | -0.705224395898218 | -5.62823232610178 | 365 | 2.598460922 | -0.203950756838813 | 2.80241167883881 | 366 | 0.687911291 | -0.504124696571570 | 1.19203598757157 | 367 | 5.047098676 | -0.245669139916480 | 5.29276781591648 | 368 | -1.221070779 | -0.383133352385105 | -0.837937426614895 | 369 | -1.439614033 | -0.425870889140594 | -1.01374314385941 | 370 | 8.787590818 | 0.146703487800293 | 8.6408873301997 | 371 | -2.386724461 | 0.0849156543337668 | -2.47164011533377 | 372 | 0.539774275 | -0.286922336726217 | 0.826696611726217 | 373 | 0.369474032 | -0.151602926537226 | 0.521076958537226 | 374 | -2.149167875 | -0.208255274008339 | -1.94091260099166 | 375 | 0.01933409 | 0.163534601823699 | -0.144200511823699 | 376 | 1.016870414 | -0.552739376396694 | 1.56960979039669 | 377 | -5.485951805 | 0.0727729832751374 | -5.55872478827514 | 378 | 0.506223899 | -0.0491019590867218 | 0.555325858086722 | 379 | -1.934614759 | -0.177535182317883 | -1.75707957668212 | 380 | -0.521110536 | 0.17098926033505 | -0.69209979633505 | 381 | 0.055716282 | 0.259471156594316 | -0.203754874594316 | 382 | -3.494650335 | -0.441514346567494 | -3.05313598843251 | 383 | -0.275822553 | -0.517160207934401 | 0.241337654934401 | 384 | 2.610696262 | -0.302861194669371 | 2.91355745666937 | 385 | -0.486156759 | -0.4974303971868 | 0.0112736381868001 | 386 | -0.247611741 | -0.283809022331534 | 0.0361972813315337 | 387 | 1.077841547 | -0.434742000287576 | 1.51258354728758 | 388 | 1.091540085 | 0.321788560550414 | 0.769751524449586 | 389 | -3.133113963 | -0.264870322738918 | -2.86824364026108 | 390 | -0.451612277 | -0.333329006376219 | -0.118283270623781 | 391 | 0.819605096 | -0.0785268527112921 | 0.898131948711292 | 392 | -0.476518511 | -0.416782749756588 | -0.0597357612434117 | 393 | -0.344000554 | -0.389360021467852 | 0.0453594674678522 | 394 | 2.175325119 | -0.155257476716021 | 2.33058259571602 | 395 | 0.390570472 | -0.155730042686548 | 0.546300514686548 |
Goldfeld-Quandt test for Heteroskedasticity | p-values | Alternative Hypothesis | breakpoint index | greater | 2-sided | less | 5 | 0.0430808211260248 | 0.0861616422520497 | 0.956919178873975 | 6 | 0.0178664874246579 | 0.0357329748493157 | 0.982133512575342 | 7 | 0.00606145752914795 | 0.0121229150582959 | 0.993938542470852 | 8 | 0.00177719846174280 | 0.00355439692348560 | 0.998222801538257 | 9 | 0.000456656705553601 | 0.000913313411107201 | 0.999543343294446 | 10 | 0.033838527692423 | 0.067677055384846 | 0.966161472307577 | 11 | 0.018471091023589 | 0.036942182047178 | 0.981528908976411 | 12 | 0.0111201246426410 | 0.0222402492852819 | 0.98887987535736 | 13 | 0.0173689819798073 | 0.0347379639596146 | 0.982631018020193 | 14 | 0.00898347448881147 | 0.0179669489776229 | 0.991016525511188 | 15 | 0.00466997597775706 | 0.00933995195551412 | 0.995330024022243 | 16 | 0.00372251530247259 | 0.00744503060494517 | 0.996277484697527 | 17 | 0.00190265800564895 | 0.00380531601129791 | 0.998097341994351 | 18 | 0.00090806427630055 | 0.0018161285526011 | 0.9990919357237 | 19 | 0.000427562395161539 | 0.000855124790323077 | 0.999572437604838 | 20 | 0.000353217814176534 | 0.000706435628353068 | 0.999646782185823 | 21 | 0.000263887684378987 | 0.000527775368757975 | 0.999736112315621 | 22 | 0.000126536081834145 | 0.00025307216366829 | 0.999873463918166 | 23 | 0.000166596334878555 | 0.000333192669757111 | 0.999833403665122 | 24 | 0.000159795617832874 | 0.000319591235665749 | 0.999840204382167 | 25 | 9.31756713361482e-05 | 0.000186351342672296 | 0.999906824328664 | 26 | 6.73045643786576e-05 | 0.000134609128757315 | 0.999932695435621 | 27 | 3.37607349002401e-05 | 6.75214698004802e-05 | 0.9999662392651 | 28 | 1.63248260386758e-05 | 3.26496520773517e-05 | 0.999983675173961 | 29 | 7.46268936412754e-06 | 1.49253787282551e-05 | 0.999992537310636 | 30 | 3.38861360896887e-06 | 6.77722721793773e-06 | 0.99999661138639 | 31 | 1.52901562223316e-06 | 3.05803124446632e-06 | 0.999998470984378 | 32 | 4.59040938912062e-06 | 9.18081877824124e-06 | 0.99999540959061 | 33 | 1.52839894296956e-05 | 3.05679788593913e-05 | 0.99998471601057 | 34 | 9.12466178426482e-06 | 1.82493235685296e-05 | 0.999990875338216 | 35 | 1.34555634282212e-05 | 2.69111268564424e-05 | 0.999986544436572 | 36 | 6.77709669151952e-06 | 1.35541933830390e-05 | 0.999993222903308 | 37 | 3.6537453782747e-06 | 7.3074907565494e-06 | 0.999996346254622 | 38 | 2.88102960717452e-06 | 5.76205921434905e-06 | 0.999997118970393 | 39 | 1.52314461846888e-06 | 3.04628923693776e-06 | 0.999998476855382 | 40 | 7.42479995593218e-07 | 1.48495999118644e-06 | 0.999999257520004 | 41 | 4.54214494293897e-07 | 9.08428988587795e-07 | 0.999999545785506 | 42 | 3.27096003337356e-07 | 6.54192006674712e-07 | 0.999999672903997 | 43 | 4.10575018992179e-07 | 8.21150037984359e-07 | 0.99999958942498 | 44 | 1.70006854946878e-06 | 3.40013709893756e-06 | 0.99999829993145 | 45 | 8.72507738016027e-07 | 1.74501547603205e-06 | 0.999999127492262 | 46 | 5.60486436560903e-07 | 1.12097287312181e-06 | 0.999999439513563 | 47 | 3.05347445957154e-07 | 6.10694891914307e-07 | 0.999999694652554 | 48 | 2.88245663792034e-06 | 5.76491327584068e-06 | 0.999997117543362 | 49 | 2.18852409985808e-06 | 4.37704819971617e-06 | 0.9999978114759 | 50 | 2.8971654540567e-06 | 5.7943309081134e-06 | 0.999997102834546 | 51 | 3.55925268023907e-06 | 7.11850536047814e-06 | 0.99999644074732 | 52 | 2.19579569960614e-06 | 4.39159139921227e-06 | 0.9999978042043 | 53 | 1.26453855792846e-06 | 2.52907711585692e-06 | 0.999998735461442 | 54 | 2.12628082620708e-06 | 4.25256165241416e-06 | 0.999997873719174 | 55 | 9.83813003855478e-06 | 1.96762600771096e-05 | 0.999990161869961 | 56 | 6.59272463088855e-06 | 1.31854492617771e-05 | 0.99999340727537 | 57 | 4.59081918425323e-06 | 9.18163836850646e-06 | 0.999995409180816 | 58 | 6.71391737368569e-06 | 1.34278347473714e-05 | 0.999993286082626 | 59 | 4.00738949643688e-06 | 8.01477899287376e-06 | 0.999995992610504 | 60 | 0.00230562851030234 | 0.00461125702060468 | 0.997694371489698 | 61 | 0.00184642763864905 | 0.00369285527729811 | 0.998153572361351 | 62 | 0.00343149200055287 | 0.00686298400110574 | 0.996568507999447 | 63 | 0.00287504463844176 | 0.00575008927688353 | 0.997124955361558 | 64 | 0.00478254838107982 | 0.00956509676215964 | 0.99521745161892 | 65 | 0.00352093506509201 | 0.00704187013018402 | 0.996479064934908 | 66 | 0.00267281928553581 | 0.00534563857107162 | 0.997327180714464 | 67 | 0.00272676592747980 | 0.00545353185495959 | 0.99727323407252 | 68 | 0.00584469233853394 | 0.0116893846770679 | 0.994155307661466 | 69 | 0.00442290527227599 | 0.00884581054455199 | 0.995577094727724 | 70 | 0.0084723801159986 | 0.0169447602319972 | 0.991527619884001 | 71 | 0.00651107251249193 | 0.0130221450249839 | 0.993488927487508 | 72 | 0.00494316218060769 | 0.00988632436121537 | 0.995056837819392 | 73 | 0.00395014176799797 | 0.00790028353599594 | 0.996049858232002 | 74 | 0.00306248623157402 | 0.00612497246314805 | 0.996937513768426 | 75 | 0.00307258271324805 | 0.00614516542649609 | 0.996927417286752 | 76 | 0.00453832245531525 | 0.0090766449106305 | 0.995461677544685 | 77 | 0.00356911973171773 | 0.00713823946343547 | 0.996430880268282 | 78 | 0.00266582164473577 | 0.00533164328947153 | 0.997334178355264 | 79 | 0.00231458384138903 | 0.00462916768277807 | 0.99768541615861 | 80 | 0.00207960537787171 | 0.00415921075574341 | 0.997920394622128 | 81 | 0.00156675854893319 | 0.00313351709786638 | 0.998433241451067 | 82 | 0.0032818226434679 | 0.0065636452869358 | 0.996718177356532 | 83 | 0.00707653041365835 | 0.0141530608273167 | 0.992923469586342 | 84 | 0.00782802307340447 | 0.0156560461468089 | 0.992171976926596 | 85 | 0.00682086120468361 | 0.0136417224093672 | 0.993179138795316 | 86 | 0.00549597117861522 | 0.0109919423572304 | 0.994504028821385 | 87 | 0.00533265123067563 | 0.0106653024613513 | 0.994667348769324 | 88 | 0.00409479091875039 | 0.00818958183750077 | 0.99590520908125 | 89 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0.00291000168464222 | 0.00582000336928443 | 0.997089998315358 | 276 | 0.00231655296543579 | 0.00463310593087157 | 0.997683447034564 | 277 | 0.00183937897544565 | 0.0036787579508913 | 0.998160621024554 | 278 | 0.00144019710359795 | 0.00288039420719589 | 0.998559802896402 | 279 | 0.00112604562287692 | 0.00225209124575383 | 0.998873954377123 | 280 | 0.000869754518627857 | 0.00173950903725571 | 0.999130245481372 | 281 | 0.000715452003039537 | 0.00143090400607907 | 0.99928454799696 | 282 | 0.000611314861584307 | 0.00122262972316861 | 0.999388685138416 | 283 | 0.000467414665318006 | 0.000934829330636013 | 0.999532585334682 | 284 | 0.000383693056343343 | 0.000767386112686686 | 0.999616306943657 | 285 | 0.000417355731161344 | 0.000834711462322688 | 0.999582644268839 | 286 | 0.000319007972801815 | 0.00063801594560363 | 0.999680992027198 | 287 | 0.000239783023233044 | 0.000479566046466087 | 0.999760216976767 | 288 | 0.000180606592609211 | 0.000361213185218423 | 0.99981939340739 | 289 | 0.000133802903239002 | 0.000267605806478005 | 0.999866197096761 | 290 | 0.000101506615465807 | 0.000203013230931615 | 0.999898493384534 | 291 | 7.47026964464708e-05 | 0.000149405392892942 | 0.999925297303554 | 292 | 5.51148472360121e-05 | 0.000110229694472024 | 0.999944885152764 | 293 | 4.68445353886544e-05 | 9.36890707773088e-05 | 0.999953155464611 | 294 | 3.44811727389332e-05 | 6.89623454778664e-05 | 0.999965518827261 | 295 | 4.03193843921282e-05 | 8.06387687842564e-05 | 0.999959680615608 | 296 | 3.24036087765276e-05 | 6.48072175530552e-05 | 0.999967596391223 | 297 | 6.16341974512062e-05 | 0.000123268394902412 | 0.99993836580255 | 298 | 6.4744641098384e-05 | 0.000129489282196768 | 0.999935255358902 | 299 | 5.92441725428841e-05 | 0.000118488345085768 | 0.999940755827457 | 300 | 4.32059200014340e-05 | 8.64118400028679e-05 | 0.999956794079998 | 301 | 3.74855592882253e-05 | 7.49711185764506e-05 | 0.999962514440712 | 302 | 2.96806333439712e-05 | 5.93612666879424e-05 | 0.999970319366656 | 303 | 2.56941249058151e-05 | 5.13882498116301e-05 | 0.999974305875094 | 304 | 2.98537710681181e-05 | 5.97075421362363e-05 | 0.999970146228932 | 305 | 2.28597836444572e-05 | 4.57195672889143e-05 | 0.999977140216356 | 306 | 1.61064684629359e-05 | 3.22129369258718e-05 | 0.999983893531537 | 307 | 1.16663901916600e-05 | 2.33327803833201e-05 | 0.999988333609808 | 308 | 9.94712652717927e-06 | 1.98942530543585e-05 | 0.999990052873473 | 309 | 6.90606113333691e-06 | 1.38121222666738e-05 | 0.999993093938867 | 310 | 4.82374558344985e-06 | 9.6474911668997e-06 | 0.999995176254417 | 311 | 5.05032427563909e-06 | 1.01006485512782e-05 | 0.999994949675724 | 312 | 3.54354576893734e-06 | 7.08709153787468e-06 | 0.999996456454231 | 313 | 2.85807399425023e-06 | 5.71614798850045e-06 | 0.999997141926006 | 314 | 1.96000630206962e-06 | 3.92001260413924e-06 | 0.999998039993698 | 315 | 1.65322117136105e-06 | 3.30644234272211e-06 | 0.999998346778829 | 316 | 1.13203878661417e-06 | 2.26407757322835e-06 | 0.999998867961213 | 317 | 7.53613223025137e-07 | 1.50722644605027e-06 | 0.999999246386777 | 318 | 4.95459189786813e-07 | 9.90918379573626e-07 | 0.99999950454081 | 319 | 3.27477421127092e-07 | 6.54954842254183e-07 | 0.999999672522579 | 320 | 2.12692392854092e-07 | 4.25384785708183e-07 | 0.999999787307607 | 321 | 1.36937812836988e-07 | 2.73875625673976e-07 | 0.999999863062187 | 322 | 8.68648962106798e-08 | 1.73729792421360e-07 | 0.999999913135104 | 323 | 3.67758453576811e-07 | 7.35516907153622e-07 | 0.999999632241546 | 324 | 4.32333735417595e-07 | 8.64667470835191e-07 | 0.999999567666265 | 325 | 2.94023002120853e-07 | 5.88046004241706e-07 | 0.999999705976998 | 326 | 1.92652437690896e-07 | 3.85304875381792e-07 | 0.999999807347562 | 327 | 1.26621181776661e-07 | 2.53242363553321e-07 | 0.999999873378818 | 328 | 2.3102725947794e-07 | 4.6205451895588e-07 | 0.99999976897274 | 329 | 3.42843899749566e-07 | 6.85687799499131e-07 | 0.9999996571561 | 330 | 2.20823730355024e-07 | 4.41647460710049e-07 | 0.99999977917627 | 331 | 1.39345424204713e-07 | 2.78690848409427e-07 | 0.999999860654576 | 332 | 8.67330708102428e-08 | 1.73466141620486e-07 | 0.99999991326693 | 333 | 5.3191182128907e-08 | 1.06382364257814e-07 | 0.999999946808818 | 334 | 8.21841332267654e-08 | 1.64368266453531e-07 | 0.999999917815867 | 335 | 6.38234570104709e-08 | 1.27646914020942e-07 | 0.999999936176543 | 336 | 4.75948437453452e-08 | 9.51896874906905e-08 | 0.999999952405156 | 337 | 5.39207944783158e-08 | 1.07841588956632e-07 | 0.999999946079206 | 338 | 5.94725749278075e-08 | 1.18945149855615e-07 | 0.999999940527425 | 339 | 1.69696067302023e-07 | 3.39392134604046e-07 | 0.999999830303933 | 340 | 1.07051169092111e-07 | 2.14102338184223e-07 | 0.999999892948831 | 341 | 6.795538075475e-08 | 1.359107615095e-07 | 0.99999993204462 | 342 | 4.06526439859466e-08 | 8.13052879718932e-08 | 0.999999959347356 | 343 | 2.54308270095322e-08 | 5.08616540190645e-08 | 0.999999974569173 | 344 | 1.82987951065709e-08 | 3.65975902131418e-08 | 0.999999981701205 | 345 | 7.62465043784381e-08 | 1.52493008756876e-07 | 0.999999923753496 | 346 | 5.1545754883396e-08 | 1.03091509766792e-07 | 0.999999948454245 | 347 | 8.7729169271511e-08 | 1.75458338543022e-07 | 0.99999991227083 | 348 | 5.28458050700227e-08 | 1.05691610140045e-07 | 0.999999947154195 | 349 | 3.62858849432874e-08 | 7.25717698865748e-08 | 0.999999963714115 | 350 | 4.31322538744686e-08 | 8.62645077489372e-08 | 0.999999956867746 | 351 | 4.12814314032766e-06 | 8.25628628065533e-06 | 0.99999587185686 | 352 | 3.04789440162822e-06 | 6.09578880325643e-06 | 0.999996952105598 | 353 | 4.19460971542541e-05 | 8.38921943085082e-05 | 0.999958053902846 | 354 | 0.00113415382098235 | 0.00226830764196470 | 0.998865846179018 | 355 | 0.000907242000902476 | 0.00181448400180495 | 0.999092757999098 | 356 | 0.000769157321072033 | 0.00153831464214407 | 0.999230842678928 | 357 | 0.00528733417958442 | 0.0105746683591688 | 0.994712665820416 | 358 | 0.00429799426160002 | 0.00859598852320005 | 0.9957020057384 | 359 | 0.00394437084213848 | 0.00788874168427696 | 0.996055629157861 | 360 | 0.00299078149180862 | 0.00598156298361723 | 0.997009218508191 | 361 | 0.00206593214875519 | 0.00413186429751037 | 0.997934067851245 | 362 | 0.00268001930618963 | 0.00536003861237927 | 0.99731998069381 | 363 | 0.00187956062702614 | 0.00375912125405229 | 0.998120439372974 | 364 | 0.0105265988832623 | 0.0210531977665246 | 0.989473401116738 | 365 | 0.0116380248835253 | 0.0232760497670505 | 0.988361975116475 | 366 | 0.00893141065356292 | 0.0178628213071258 | 0.991068589346437 | 367 | 0.0375521171408491 | 0.0751042342816981 | 0.96244788285915 | 368 | 0.0287339717585537 | 0.0574679435171075 | 0.971266028241446 | 369 | 0.0221433716549427 | 0.0442867433098854 | 0.977856628345057 | 370 | 0.672696308705148 | 0.654607382589704 | 0.327303691294852 | 371 | 0.673062125045612 | 0.653875749908776 | 0.326937874954388 | 372 | 0.625469413676097 | 0.749061172647806 | 0.374530586323903 | 373 | 0.571150991107537 | 0.857698017784925 | 0.428849008892463 | 374 | 0.558138388701031 | 0.883723222597938 | 0.441861611298969 | 375 | 0.492525940343851 | 0.985051880687701 | 0.507474059656149 | 376 | 0.456575012022886 | 0.913150024045772 | 0.543424987977114 | 377 | 0.887044924803601 | 0.225910150392798 | 0.112955075196399 | 378 | 0.846598708959867 | 0.306802582080265 | 0.153401291040133 | 379 | 0.849271576892888 | 0.301456846214224 | 0.150728423107112 | 380 | 0.817440122736242 | 0.365119754527516 | 0.182559877263758 | 381 | 0.778066505686481 | 0.443866988627038 | 0.221933494313519 | 382 | 0.899476993538195 | 0.201046012923610 | 0.100523006461805 | 383 | 0.848173088143432 | 0.303653823713136 | 0.151826911856568 | 384 | 0.914815096344222 | 0.170369807311555 | 0.0851849036557776 | 385 | 0.861367926949045 | 0.27726414610191 | 0.138632073050955 | 386 | 0.785825212973388 | 0.428349574053224 | 0.214174787026612 | 387 | 0.77058828553707 | 0.458823428925861 | 0.229411714462931 | 388 | 0.691924090167278 | 0.616151819665445 | 0.308075909832722 | 389 | 0.96710211458508 | 0.065795770829842 | 0.032897885414921 | 390 | 0.914571486939556 | 0.170857026120888 | 0.0854285130604439 |
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity | Description | # significant tests | % significant tests | OK/NOK | 1% type I error level | 256 | 0.663212435233161 | NOK | 5% type I error level | 339 | 0.878238341968912 | NOK | 10% type I error level | 366 | 0.94818652849741 | NOK |
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| | Parameters (Session): | par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; | | Parameters (R input): | par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; | | R code (references can be found in the software module): | library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE)
a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
| |
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