How to find the optimal p for AR model

4 vues (au cours des 30 derniers jours)
xiaoli su
xiaoli su le 23 Juil 2015
Commenté : xiaoli su le 24 Juil 2015
Hi everyone,
I now have a nitrate time series and I have decomposed it with an additive model, which is Y=T+S+R. I hope to find the best-fit AR(p) model for my residuals. I read from some papers that Mann-Wald process could be used. It would be really great if someone could provide a code on how to find the p. Below is my nitrate residuals. Thanks!
0.160157983184890 2.17307720054057 1.52955989565626 1.45006890417194 2.45078566444762 1.82790551974331 2.69034445775899 4.42816184597468 2.34540928553036 2.68279049780604 1.62871903928173 0.508296837837410 -1.62288362596995 1.00584965715162 -0.543055573966803 0.444003915314771 1.52210606035635 2.59531316141792 2.23891089519949 3.05301642518107 3.10129331550264 0.429833321544216 -0.483079342214210 0.722657253107364 -2.30055733593411 -1.25196701104664 -1.31637009539918 -0.531355848351714 0.193111664455751 1.34662635028322 1.17338288183068 1.38414720557814 0.521082891665609 -0.939218305526926 0.469027827480539 3.37592322056800 2.33001561329242 3.25061121894578 1.20307030535913 1.16590066017249 1.22124884474584 0.251845091339198 1.67106041465255 1.28918353716591 1.18477801701926 0.0406356165926184 -0.0849594546340268 -1.09190526778067 -2.24995407529036 -2.44871656287112 -1.47128179369187 -0.438992641112626 0.582514336226620 1.16346937958587 1.01304349966511 0.941625416944357 1.37737869456360 -0.790604913097152 -0.566041187557907 -0.880828206938661 -2.85271821768246 -2.62150501849733 -2.32172834455219 -1.72158039620705 -1.13491462210192 2.23399921602322 -0.185267868131646 0.230972844913491 0.841384917298627 -1.66993989159624 -2.26121737129110 -2.65584559490597 -3.72157680988388 -3.18420481493285 -1.57576934422182 -1.59696260111080 -0.875138030239769 -1.32156539734874 -0.119673685737717 0.327725823073310 0.0342966932243356 -0.746369320904637 -2.41098800583361 -3.41195743268258 -3.19052985189460 -3.45549906417769 -2.47420479670077 -2.27343925682385 -1.84095589118694 -0.720724461530018 -0.385173953153102 -0.0366156505761843 -0.184385984659268 -1.31839320302235 -0.627853090185433 -1.23616372126852 0.594922655285354 0.629612240768162 0.680365300010968 0.0264896376537763 -1.57986819994342 -1.29947797852061 -0.541768674377802 -0.0945515750349948 0.341836886647812 -0.419011535949380 -0.965312627346572 -1.65496446266376 -2.05971929034400 -1.59887091009531 -2.04045905108661 -1.84717592067791 -0.914874961509212 -0.226825944320514 1.11411769358818 0.0414180536968818 0.403965307145579 -1.19572431668572 -1.37686661331702 -2.79685965286833 -2.17045568678267 -2.08344851076809 -1.18137785499350 0.230564069181090 0.881023826115679 0.0227316420702668 -0.530741462255145 -0.357706770380556 0.0349992798340315 -0.698531551231380 -1.05651505209679 -4.02404929588220 -2.89478653203066 -2.49912055925018 -2.47739111070970 -1.41929038776922 -0.575671836068746 -0.598305226348267 0.251380466092211 1.14757395173269 -0.00256120028683160 -1.54793323558635 -1.95875793968587 -4.12723338970539 -2.33071182708796 -1.53588705954159 -0.795498819235224 -0.171239299528854 1.98353804593751 0.109563456423885 -0.767092058369746 0.577760226036623 0.131283870782992 -0.430929367750639 -0.290095279084269 0.705388069662101 1.21076842304542 -0.961248012642317 -1.03140097157006 -0.115282656097797 -1.94334651886554 -0.838662314613278 -1.00965903164102 -0.513147954468758 -0.0424655139564996 -1.40851995472424 -1.22502706929198 0.586615073220280 2.16315422436950 1.62729658344764 0.212002419285794 1.55057952852394 -0.763825535477906 0.377017466540245 0.0813795432783944 0.639249419216544 -0.193509344505306 2.11279500549285 2.01474669169100 -1.00665237203085 0.375245576884251 0.344746729728291 0.435311359332332 -0.988452728663628 0.0468009991004125 0.0158027948844535 1.18002366838849 0.519252337092534 0.898752374136574 0.100515518900615 -0.281874001135344 0.713085731908695 -1.09165752441031 -2.17499757480038 -2.10387414943045 -1.77597944666052 -0.335066915130585 -0.670506325580654 0.393873344689276 -0.213639190840792 -0.828080362030862 -1.39885841850093 -1.82138914777100 -2.41957061596107 1.72474492348582 0.637563669861638 0.321345894997459 -0.755900606466719 -2.14292928117090 -2.80640989885508 -1.73387142881925 -1.20402517258343 -0.536207547007612 -1.73462680671179 -2.56629873921597 -3.79432141264014 -2.94124707742737 -1.04946953428566 -0.898328514383944 0.774783777917767 3.60581389697948 2.61549207606119 2.76878934386290 -0.744105601135384 0.0338708192063266 1.60881035226804 1.63889721452975 1.09333333887146 1.23066646885013 0.306902806757731 0.514602623425335 0.356773715492936 -1.77063737567946 -1.12520039783186 -2.26704433826426 -2.17208048749665 -0.219945266389052 0.984453062438551 4.21439872146615 10.6309936395738 9.58348556531831 4.84058069899181 3.25023931042530 2.76976919525879 1.30981690685228 1.62141267746578 1.80372752879927 1.38985018133276 2.03514418820625 1.38900131579975 -0.0668942244067584 0.495559489466734 -0.124789789022819 0.229564143416563 0.229581547615947 0.273870225215330 1.82887673657471 1.85863130295410 1.18370495705348 0.587686401352863 1.44833920699225 0.397255127351630 0.528218379911014 3.31003088855040 3.85584040582673 3.65225313103201 2.73822933399728 1.88907681036256 1.00744211748783 2.54305547663310 1.16348792049838 0.774428165563654 -0.157960233031073 -0.634385520905799 -0.874163468580525 -0.621292162175251 -0.0626238501330239 0.257947670838141 -0.280817329430694 -0.193811057299531 -0.206486957408366 0.0863852015027977 -0.881223561866039 -0.774924524034874 0.828845879136291 1.08787938602746 -1.24653976888138 -1.39630966371022 -2.43148255990210 -1.19245223716504 -0.186158441667991 2.20900662222907 1.73238951688612 0.953620476563174 2.39637050596023 1.36212834255728 1.19055753649434 1.99474984415139 0.524489482008449 -0.385121619054498 0.361164285519512 -0.610946599977543 -0.0421940147145987 -0.0471701500516536 -0.456328459628708 -0.816938703185762 -0.231329876022817 0.558086756340128 0.347174743043072 4.07052584846602 2.45642428408896 2.17897197679191 0.563416677131806 1.06146458540064 -0.415624030570522 -0.318141367141684 -0.259940878952849 1.61980766225599 0.778375292184820 -0.00854928268634358 0.530197499782492 -0.00279259602867288 0.940064630360165 3.74897112182900 3.04957461793479 2.30278132196951 1.40255150376424 1.38919295895897 0.0974522399136985 0.133559588888423 0.612486007583148 -1.31357977552212 0.332425808712600 -0.741205491332674 1.26801053182205 2.64687581605678 1.38863810792846 1.73500360772908 0.906932585289691 0.0380328362503098 0.0930509169709258 0.471917055711543 0.832802270172159 0.327095283832775 0.200359660833392 -0.307012841445991 -0.207738021525375 2.98568605947524 2.52960714711281 2.30613144267932 1.43821921600583 4.66417826273233 2.41865513621884 1.17408007572535 -0.968475910048146 -1.06672410162164 0.435699070144867 -0.679114644368625 1.52121897631788 1.66340185208439 1.10648173548785 0.756164826820247 1.72841139591265 2.79052923840504 1.06346490465744 -0.0409513640701629 -0.386648549077766 -1.06953794088537 -0.520855978352972 1.45028910589943 1.25068152235183 -0.195976806115778 -1.33073812694643 -0.110896239848139 0.792509125010148 0.900885761268438 0.0725802262867249 -0.498077241674988 -1.13091563691670 -1.05444623295841 -0.307505475660124 -1.27820159064184 -1.73985038542355 -0.0304499151252560 -5.12133439419001 -4.62070827832584 -3.96203870770166 -2.67526216267748 -1.57287548189330 -2.88789757708912 -1.62167717956495 -2.11834897984077 -1.28414942177659 -1.70968674699241 4.62332326199177 -1.62001747194405 1.76553879575708 0.946698274387148 0.0514212307772164 -1.44598453843271 -3.22087248488264 -2.62551236631258 -1.79483316802251 -2.78364617353244 -3.25328781670237 -2.00766635015230 -3.04849754640223 -4.02067948957216 2.85603558089486 3.67835385529082 3.55423560744678 -0.964816690997261 -0.0707405176813029 -1.95922160334534 -1.69738360428939 -1.88503782003342 -1.86352066543747 -0.426740398121506 -0.926412799605548 -1.16743594600959 4.46043791522332 0.513914985385175 -0.609044463692976 0.635867352628874 -1.54670300128928 -1.04902528518743 -1.43602850036557 0.0674760866562765 0.630152029018126 -0.636908900900024 0.0995774943818254 0.0697131407436751 -1.49125420025752 0.793381664670220 0.451581008357960 -3.26734837355430 0.556240070293440 0.611776578161180 -0.970767841251080 0.0538955415366611 -0.587269719335599 -0.663171856487859 0.0461733355598827 -1.80923222131238 -2.01404076954768 0.436753893145947 -0.153887963400423 -0.146658553546792 -0.966911317933161 -0.326916009299530 -0.841601633945901 -1.88577945139227 -0.590785914498639 1.08947074111499 1.08327472192862 -1.99427203217775 0.702078213352839 0.161031670812359 -0.305451395968119 -0.0870631873485989 -1.05615714796908 -0.165003055569557 -0.773529877450035 -2.27154890113051 -1.13039657147099 -0.903981119091473 -1.86901833951195 1.79459369914757 2.51410274444404 1.16521499766946 0.926890728654869 0.286437729040281 -0.598197438814308 -2.16248454264890 -0.796552569763486 -1.39341280067807 -1.80610167425266 -2.10682742810725 -2.98740584776184 -3.87424976333643 0.251032824725939 -0.286696126282760 2.52313840046854 -0.832855802380152 -1.04293216946885 -1.20446048253755 -1.24396971188625 -2.56137115303494 -2.79960122284364 -2.93416817993234 -2.09188780882104 4.93404181737026 6.02886845419852 1.95429829895571 0.760291621472907 0.411885251390097 0.264538642067290 0.607469126764484 0.604118691181675 -0.442123951201132 -0.900795234243940 4.35409660743325 4.04753577531044 0.174624200267637 -0.994390369138215 0.962198273384867 -0.532649610332050 0.390373785351033 -0.889085000205883 0.621704283257200 -0.382187354559718 -0.182271203176635 -0.624783684453552 0.571266947989530 0.0875649126326145 -1.81088786764430 0.836256360715734 0.439003797004706 0.755314711053681 0.484496895502654 -0.453803086288373 0.753144987940602 -0.524587859110426 -1.03181290496145 -0.819866594472480 -0.122657165263506 0.528099596145467 -1.38249438836556 -0.704191361239631 -0.470285127184769 -2.24286389536990 -0.0924744411550401 0.920384369819825 -0.961508756185312 -0.978082804470449 -0.264149055555584 0.188956052699280 -1.13267572632586 -0.310760169150993 -1.35019535689613 -1.99073353500431 -2.53566850718355 -1.48704000260280 -0.860540224622045 -0.981522618881289 -0.0122569541205351 -0.437672201639781 2.75959194904097 -0.798315758938272 5.35121126480248 3.64428561574324 0.276009223763989 -4.23121581557830 -0.717146338991657 -0.867359035645010 -0.709700457898366 -0.819524061391721 -1.57909959586508 -1.10335605361843 1.53743933782821 -1.46168201238514 -2.03099619687850 -2.43676305417185 4.07611935261479 -2.98386917296161 -2.14885749960907 1.67622748050346 0.415044854015996 2.85138005228853 -0.722036694418932 0.0648656475936020 -1.11772421519386 1.62089454035867 -1.06929811336879 1.87109382810374 0.750135030656278 0.113073237845765 -1.77166313103581 1.54871954584262 -0.484093911878955 1.32618970415947 0.273931763217895 2.47184567199632 0.745561821974748 -0.436697879706828 1.03975741733160 0.582856264570025 0.328056260888449 1.15215326084383 1.16185347172814 1.68511716037246 0.213760721416778 -0.0100950887789047 0.728805765045413 -0.746974307410273 3.81753391533404 0.311652508418360 2.93081470622268 0.713524247226995 2.98988303431131

Réponse acceptée

Roger Wohlwend
Roger Wohlwend le 24 Juil 2015
You could use the Akaike or the Bayesian Information criterion (Matlab function aicbic). Also consult the page "Choosing ARMA lags using BIC" in the Matlab documentation.
A less sophisticated way is to try different values for p, estimate the model in each case and choose the p where the model's residual are free of autocorrelation.
The Matlab function parcorr suggests that the optimal value for p is 1 in your case. Indeed, for p = 1 the model seem to be quite good. The residuals are free of autocorrelation, the R-square is 0.35 and the coefficient is significant.
  1 commentaire
xiaoli su
xiaoli su le 24 Juil 2015
Thanks Roger, I have tried the BIC before and it showed the best model ARMA model. However, when I was using autocorr, I got a ACF graph which has a quite significant value at lag 12. I wonder if this is related to the p of AR model or what it indicates.

Connectez-vous pour commenter.

Plus de réponses (0)

Catégories

En savoir plus sur Conditional Mean Models dans Help Center et File Exchange

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by