VECTOR AUTOREGRESSIVE MOVING AVERAGE MODEL
|
|
- Martha Henderson
- 5 years ago
- Views:
Transcription
1 VECOR AUOREGRESSIVE MOVING AVERAGE MODEL Ran Kumar Paul Indan Agrculural Sac Reearch Inue Lbrar Avenue, New Delh Inroducon In man area of Agrculure relaed feld forecang roducon ver much eenal for roer lannng. Value oberved over a erod of me can be reaed a a me ere can be uded o ee he rend, ner-relaon, erodc ec. Aar from h, me ere model can be ued o foreca fuure value wh more recon. A model ung whch we are able o calculae he robabl ha a fuure value of he ere lng beween wo ecfed lm ermed a a ochac model. In h cone, a me ere condered a a ochac roce a amle or conecuve value of he me ere condered a a realaon of he ochac roce ha generaed he me ere. An moran cla of ochac model ha are wdel ued o rereen me ere he aonar model whch aume ha he ochac roce ha generae he me ere reman n eulbrum abou a conan mean level. A non-aonar roce doe no have an naural mean. A ochac roce ad o be rcl aonar f roere are unaffeced b change of me orgn. A euence of rom varable {a } called a whe noe ere f hee are rom drawng from a fxed drbuon wh ero mean conan varance. he nd of ochac model condered for me ere anal are baed on he conce ha a roce n whch ucceve value are hghl deenden can be regarded a generaed from a ere of ndeenden hoc, a, whch are whe noe. If a whe noe roce {a } ranformed no a ochac roce { } b a weghed um of a value o ha a a a, hen called a lnear flerng. Ung he bac hf oeraor B noaon, B oerae on he me ere { a } uch ha B a a, for value of,, we can wre he above euaon a a Ba B a ( B B a ( B a ( B B B whch a olnomal n he bac hf oeraor B nown a he ranfer funcon of he fler. If he euence of wegh n he ranfer funcon are fne or nfne convergen hen he fler ad o be able he generaed ochac roce { } ad o be aonar. When he euence of wegh { } nfne no convergng hen he roce { } non-aonar. For a aonar roce he arameer wll be he mean abou whch he roce vare for a non-aonar roce onl a reference on for he level of he roce. he mo oular cla of aonar e of ochac model ued for me ere modellng he Auoregreve Inegraed Movng Average (ARIMA model nroduced
2 M III: : Vecor Auoregreve Movng Average Model b Box e al (7. h cla nclude, auoregreve model, movng average model, rom wal model, auoregreve movng average model, negraed model eaonal model. In auoregreve model, he curren value of a roce exreed a a lnear aggregae of a value of he ere along wh a rom hoc. If b { }, we mean a aonar roce o rereen a me ere euence { } he roce whch he devaon from a cenral value, whch he mean of he roce, hen an auoregreve model of order, denoed b AR(, ae he form. Here { } a euence of rom hoc whch are aumed o be ndeendenl dencall drbued wh execaon ero conan varance. Parameer of h model are (,,,,,. Ung he bac hf oeraor noaon we can wre he AR( model a B B B ( B B B ha ( B ( B B B B a olnomal of degree n he bac hf oeraor B. B conderng he lnear fler rereenaon of he above AR( model he condon for aonar of he roce can be derved n erm of he roo of he characerc euaon ( B. h condon ha all he roo of he h degree olnomal euaon ( B are more han one n abolue erm. Anoher oular rereenaon of a me ere ung movng average model. In h model he devaon of are rereened b a lnear combnaon of a fne number of revou rom hoc. he exreon for a movng average model of order, MA( model, a a a a ( B a ( B B B B a h degree olnomal n he bachf oeraor B. Snce he lnear fler forma of h model he ame conan onl fne number of erm, a movng average model alwa aonar. Ung he nvere funcon ( B ( B, he lnear roce ( B a can have he nfne auoregreve rereenaon ( B a ( B B. h roce nverble when he euence of wegh converge. he reured condon for h ha he roo of he characerc olnomal euaon ( B le ou de he un crcle he condon for nverbl of a MA( model. he model ha combne he above wo nd of model he mxed auoregreve movng average (ARMA model. he auoregreve movng average model wh auoregreve erm movng average erm rereened b ARMA(, mahemacal exreon, a a a a whch can be wren a ( B ( B a M III-3
3 M III: : Vecor Auoregreve Movng Average Model ( B B B B a olnomal n he bac hf oeraor B of degree ( B B B B a olnomal n B of degree. he condon for aonar of he model ame a ha of an AR( model he condon for nverbl of he model he ame of an MA( model. Hence hee condon are ha roo of he wo olnomal ( B ( B le ou de he un crcle. In mo of he raccal uaon, he oberved me ere how non-aonar behavour do no var abou a conan mean. In uch uaon ma be oble o rereen he ere b an ARMA model wh a generaled AR oeraor olnomal, a ( B for whch one or more roo are un o ha can be facored a ( B ( B( B d ( B a olnomal n B wh all roo ou de he un crcle. Box e al (7 nroduced a cla of non-aonar model b negrang ARMA model wh rovon for he rereenaon for un roo non-aonar. h model erm volaon of he aonar condon b allowng ome roo o le on he un crcle. hee model are ermed a auoregreve negraed movng average (ARIMA model. If here are d roo ha fall on he un crcle, hen he model rereenaon d ( B ( B ( B a h euvalen o ranformng he orgnal ere no anoher ere b uccevel dfferencng d me hen rereenng he ulmae dfferenced ere b an ARMA(, model. he arameer d ermed a he order of dfferencng are reecvel he order of auoregreve movng average erm n he model. he general eaonal ARIMA model wh AR order, MA order, order of dfferencng d, eaonal, eaonal AR order P, eaonal MA order Q order of eaonal dfferencng D rereened b ARIMA(,d,(P,D,Q exreon d D ( B ( B ( B ( B a ( B, ( B, ( B B B P ( B B B, ( B B B ( B B B P Q Q whch are olnomal n B. he condon for aonar nverbl of he dfferenced ere d D ha he roo of all hee olnomal le ou de he un crcle. Mulvarae me ere model he maor wo reaon for analng modellng of more han one me ere euence ogeher, nown a mulle me ere or vecor me ere, are ( o under he dnamc relaonh among he dfferen me ere comonen ( o mrove he accurac of foreca of one ere b ulng he nformaon abou ha ere conaned n all oher me ere. Suoe here are me ere comonen { Z },{ Z},,{ Z} for,,, a euall aced me nerval. We can rereen hee comonen b a vecor Z Z, Z,, Z ( M III-33
4 M III: : Vecor Auoregreve Movng Average Model whch we call a a vecor of me ere. A vecor me ere roce Z rcl aonar f he robabl drbuon of he rom vecor Z, Z,, Z Z, Z,, Z n l l nl,,, n are he ame for arbrar me, all lag l,,, all n. ha he robabl drbuon of obervaon from a aonar roce nvaran wh reec o hf n me. For all uch ere E( Z for all (,,, he mean vecor for he ere E Z Z ( l l nown a he cro covarance marx of lag l for onl on he lag l when he ere aonar. ( ( ( ( ( ( ( ( ( ( If V dag (, (,, ( ( he varance of he h comonen ere {Z }, hen l,, ( wll deend / ( l V ( V ( ( ( ( he cro correlaon marx a lag l. For a aonar vecor roce, he rucure of he cro-covarance cro correlaon marce rovde a ueful ummar of nformaon on aec of dnamc ner relaon among he comonen ere of he roce. Samle emae of elemen of he lag l cro correlaon marx baed on a amle of e comued a ˆ ( l l ( Z Z l Z Z Z Z ( Z ( Z for,,, ; l,,,, he amle mean of he h comonen ere. he amle cro correlaon can be ued o denf low order vecor movng average model. For large amle e, under whe noe aumon, ( l are execed o be drbued a normal devae wh mean ero aroxmae varance h roer ued o e he gnfcance of ndvdual amle cro correlaon. Under M III-34
5 M III: : Vecor Auoregreve Movng Average Model whe noe aumon o e he combned gnfcance of he elemen of amle crocorrelaon marx ( for dfferen lag l, he ac defned a l r - - ( ( ( ( ( wh degree of freedom. Vecor AuoRegreve Model A unvarae auoregreon a ngle-euaon, ngle-varable lnear model n whch he curren value of a varable exlaned b own lagged value. Mulvarae model loo le he unvarae model wh he leer re-nerreed a vecor marce. One uch model he vecor auoregreon model. he vecor auoregreon (VAR model one of he mo ucceful, flexble, ea o ue model for he anal of mulvarae me ere. I a naural exenon of he unvarae auoregreve model o dnamc mulvarae me ere. hu hee model caure he lnear nerdeendence among mulle me ere. he VAR model ha roven o be eecall ueful for decrbng he dnamc behavor of economc fnancal me ere for forecang. I ofen rovde ueror foreca o hoe from unvarae me ere model elaborae heor-baed mulaneou euaon model. Foreca from VAR model are ue flexble becaue he can be made condonal on he oenal fuure ah of ecfed varable n he model. In addon o daa decron forecang, he VAR model alo ued for rucural nference olc anal. In rucural anal, ceran aumon abou he caual rucure of he daa under nvegaon are moed, he reulng caual mac of unexeced hoc or nnovaon o ecfed varable on he varable n he model are ummared. hee caual mac are uuall ummared wh mule reone funcon foreca error varance decomoon. A dadvanage here ha a large number of arameer mgh be needed for adeuae decron of daa. A good accoun of leraure on VAR model are avalable, o ce a few, Hamlon (994, Hacer Haem (8, Helmu (5, Runle (987, Soc Waon (, a ( Waler (3. A aonar vecor me ere { Z } wh comonen can be modeled b a vecor auoregreve model of order denoed b VAR(, exreon h can be wren a Where ( B. ( B I B B a marx olnomal n he bac hf oeraor B, vecor of he ere,,,, (,, are Z arameer marce,, he mean are ndeendenl dencall drbued rom nnovaon vecor havng ero mean conan covarance marx. M III-35
6 M III: : Vecor Auoregreve Movng Average Model Examle: he VAR( model wh hree me ere euence ( ( ( ( ( 3, ( ( ( ( ( 3, ( ( ( ( ( , 3 3 he correondng ndvdual unvarae model are ( ( ( ( ( ( 3 ( 3 ( 33,, 3, 3 (,, 3 3,,, 3 3, ( ( ( ( ( (,, 3 3,,, 3 3, ( ( ( ( ( ( 3 3 3, 3, 33 3, 3, 3, 33 3, 3 hu n each model, lagged erm, u o lag, of all he hree me ere euence are ncluded. he condon reured for aonar of a VAR( model can be brough ou b conderng an euvalen VAR( rereenaon. B reeaed ubuon for,,, n he VAR( model, he model can be rerucured no he form ( I Hence he roce vecor Z, Z,, Z are unuel deermned b Z he nal value of he roce he euence of nnovaon vecor. If all egen value of have modulu le han un, hen he roce Z a well defned ochac roce Z can be exreed a Z for,,, (. h condon euvalen o de( x for. Hence he condon for abl/aonar of a VAR( roce ha he roo of he deermnenal euaon de( x have all roo ou de he un crcle or euvalenl all he egen value of are le han one n abolue value. he general VAR( model Z Z Z can be brough o an euvalen VAR( model of a dmenonal roce a Y Y Y ( Z, Z,, Z, (,,,, (,,, whch are column vecor of lengh a uare marx gven b M III-36
7 M III: : Vecor Auoregreve Movng Average Model Baed on h rereenaon of VAR( model he condon for aonar of he model ha all egen value of he above marx have module value le han un. Euvalenl de( x for. Snce de( x de( x x he condon for aonar of he VAR( model ha he deermnanal olnomal de( x x have all roo ou de he un crcle. In erm of he bac hf oeraor B, he VAR( model can be wren a ( B Z ( B B B. Now conder a funcon ( B ( B o ha ( B ( B ( B B Oerang h funcon on he VAR( model gve, ( B ( B Z ( B ( B whch reduce o Z ( B ( B. Bu. ( B B ( becaue B If we wre Z. hen we can wre he VAR( model a whch can ex onl when he euence converge whch he condon for nverbl of he roce. he relaon beween hee coeffcen marce arameer M III-37
8 M III: : Vecor Auoregreve Movng Average Model marce can be obaned b exng ( B ( B hen euang coeffcen of ower of B, a wh I for. Emaon he arameer marce of VAR( model are emaed b generaled lea uare mehod. he VAR( model ( Z ( Z can be exreed a = ( Z X ( X (( Z,,( Z (,,. ( An euvalen form of he model Z Z = wh = ( he model can be rewren a Z X X (, Z,, Z (,,,. If he amle e n = -, hen defne n marce Z ( Z,, Z (,,. Le X be a marx of order n (+ wh h row a Z (, Z,, Z for,,., hen we have he relaon Y XB whch n he general form of a mulvarae lnear model can be olved for B a ˆ B ( X X X Y. M III-38
9 he emae of nnovaon deron marx obaned a Sm ( n S m ˆ Z ˆ ˆ Z. M III: : Vecor Auoregreve Movng Average Model For large amle e under aonar Gauan aumon he aroxmae large amle drbuon of whch he emae of vec( ( N, ˆ X X h roer ued o comue he ard error of he emae for furher eng. For he elecon of he order arameer of vecor auoregreve model dfferen order elecon crera are ued. If ( he maxmum lelhood emaor of he nnovaon deron marx obaned b fng a VAR( model o he daa, hen he Fnal Predcon Error creron ( FPE creron gven b FPE de ( (. ( he nformaon creron nroduced b Ae defned b log( maxmed lelhood AIC r r he amle e r he number of arameer emaed for he model aroxmaed a r AICr log( r c r he maxmum lelhood emae of he nnovaon deron marx c a conan. Snce here are ( arameer n he cae of a general VAR( model he mnmum AIC creron calculaed b AIC( ln (. ( Oher wo crera ued are he Baean nformaon creron BIC uggeed b Schwar (alo nown a SC creron he HQ creron rooed b Hannan Qunn. hee are gven defned a r log( BICr log( r r log(log( HQr log( r. In he cae of a general VAR( model hee creron are calculaed ung he formula ln ln ( HQ( ln ( M III-39
10 ln ( SC( ln. ( M III: : Vecor Auoregreve Movng Average Model he order ha eld mnmum value for hee creron are eleced a he uable order for he model. A lelhood rao e for eng he null hohe H of he VAR( model M III-4 : agan H : gven ha he e ac ( ln ln LR ( denoe he maxmum lelhood emae of when a VAR( model wa fed o he vecor me ere of lengh. h e ac ha an amoc drbuon wh degree of freedom. Examle A vecor auoregreve model of order wh he followng arameer marce gven below. Examne wheher he model aonar or no. Wre he ndvdual model. Model: Z Z Z ,, he arameer marx correondng o VAR( rereenaon of a VAR( model o ha we ge for a VAR( model I Egen value of he above marx are he module value of he egen value are Snce he module value of all he egen value are le han un he model aonar he ndvdual model of he VAR( model are Z.5Z,.6Z,.5Z,. 3Z,.3Z.Z.7Z. Z Z,,,, Examle: For he amle bvarae me ere (Samle daa - on US fxed nvemen change n bune nvenore he VAR( model can be emaed a
11 M III: : Vecor Auoregreve Movng Average Model Paral Cro Correlaon For a vecor me ere euence baed on elemen, he aral cro correlaon beween he vecor m gven he n beween vecor,, m he cro correlaon marx beween he elemen of vecor m, afer adumen of boh for her deendence on he elemen of nervenng vecor,, m denoed b P m. For a aonar vecor roce, he aral cro correlaon marx P m can be aroxmaed a follow. Conder he error vecor U m reulng from aroxmang he roce b a VAR model of order (m-, gven b m Um ( m m Y m ( (,( ( m ( ( m, ( m,, ( m( m Y( m.( (,, m. Smlarl b conderng a bacward VAR model of order (m- a m U ( m m m, m we can oban he bacward AR coeffcen marce from he e of euaon m ( ( ( m for l,,( m he bacward error vecor U m, m can be obaned a m m, m m ( m m U m ( m Ym, Paral cro correlaon marx a lag m hen defned a P Corr U, U V ( m E( U U V( m m ( m, m m, m, m m, V ( m dag (, V m dag (, m ( m ( m ( m( m,, a a oluon m m ( U m, ( ( Cov, ( m m m ( U m, m ( ( m Cov ( m ( m E ( Um, mu m, ( m ( m ( m ( m ( m ( m ( ( (,, ( m m ( ( m,, ( ( m. M III-4
12 M III: : Vecor Auoregreve Movng Average Model Under VAR( model aumon, he elemen of amle aral cro correlaon marx Pˆ m are aroxmael normall drbued wh ero mean varance for m h roer can be ued o e her gnfcance. Examle: For he amle daa - he followng are he lag 3 amle cro correlaon marx aral cro correlaon marx. Cro correlaon marx of lag Paral cro correlaon marx of lag VARMA Model he rereenaon of a vecor me ere b a Vecor Auoregreve model of order are ndeendenl dencall drbued rom vecor wh E( = E( = for all, whch are oherwe nown a whe noe ere. Inead of he whe noe ere, f we aume ha he error erm are auocorrelaed u o a ceran lag a, hen he whe noe erm can be relaced b an auo correlaed erm a a a he a are ndeendenl dencall drbued whe noe ere,, are marce of order. B mang h ubuon for, we ge he correondng model a a a a h model nown a Vecor Auoregreve Movng Average model wh order (, denoed b VARMA(,. h can be rewren a ( B ( B a B I B B (, B B I B B (, B I an den marx of order B he bac hf oeraor uch ha B. A vecor me ere can be modelled b a VARMA model whch a mulvarae analog of he unvarae ARMA model. he condon reured for aonar nverbl of a M III-4
13 M III: : Vecor Auoregreve Movng Average Model VARMA(, roce ha he ero of he deermnenal olnomal (B (B n B le oude he un crcle. A general VARMA(, model can have a VAR( rereenaon ung whch oble o e he aonar condon n erm of egen value of a characerc marx he form of he AR coeffcen marx for uch a rereenaon n erm of he arameer marce a marx of order ( ( he comonen marce are gven b I I a marx of order I a marx of order a marx of order wh all elemen ero I I I a marx of order,, If all he egen value of marx are le han one n abolue value hen he VARMA(, model aonar. In arcular for a VARMA(, model wh he exreon he condon for aonar ha all egen value of he coeffcen marx are le han one n abolue value he condon for nverbl of he model ha all egen value of he coeffcen marx are le han one n abolue value. Selecon of he order arameer are made b ung order elecon crera le AIC, BIC HQ. he Aae nformaon creron aroxmaed b r AICr log( r c, he Baean nformaon creron gven b Schwar (978 M III-43
14 M III: : Vecor Auoregreve Movng Average Model r log( BICr log( r, he creron rooed b Hannan Qunn (979 r log(log( HQr log( r r he maxmum lelhood emae of he nnovaon deron marx, r he number of arameer emaed, he amle e c a conan. he order ha eld mnmum value for hee creron eleced a he reured order for he model. For a VARMA(, model he number of arameer o be emaed ( o ha we ge hee creron for elecon of order of a VARMA(, model a AIC ( (, log(, BIC ( log( (, log( HQ ( log(log( (, log( Emaon of Model Parameer An emaon algorhm for vecor auoregreve movng average model arameer baed on he mehod gven b Sld (983 a followed. Conder he model ( B ( B whch aonar nverble Le Here ( B I B ( B I B n B B,, be a amle of vecor realaon of he ere. Defne marce ( for,, n;,, whch an n marx of obervaon, n ( for,, n;,, whch an n marx of redual, n Y ( B, B,, B whch a lagged daa marx of order n, A ( B, B,, B whch a lagged redual marx of order n, U ( A, Y whch a marx of order n (. B he marx wh order n. Now defne he marx,,,,, ( of arameer whch of order h (, elemen, M III-44 for,, n;,, of (. Ung hee marce he VARMA(, model can be wren for he amle daa marx a U. Fr mae he nal emae of redual vecor b fng a hgher order vecor auoregreve model. For h conruc a marx W ( B, B,, B
15 M III: : Vecor Auoregreve Movng Average Model he hgher order choen for he auoregreon. hen he nal redual marx ˆ ( obaned a ˆ ( W( WW W. Ung h redual marx conruc marce Â( Uˆ ( comue he fr emae of a ˆ( from lnear regreon a, ˆ ( [ U ( U(] U ( h nal value ˆ( aumed a ero. For he eraon o emae ˆ (, we oban he emae from he regreon U ( U( ˆ( U( a ˆ ( [ U( U( ] U ( we comue he new e of redual ung he recuron ˆ ( ˆ ( ˆ ( ˆ (. Ung he new emae of redual arameer marce he eraon connued ll convergence acheved o a afacor level of accurac. Reference Box, G. E. P., Jenn, G. M. Renel, G. C. (7. me-sere Anal: Forecang Conrol. 3 rd edon. Pearon educaon, Inda. Hamlon, Jame D. (994. me Sere Anal. Prnceon Unver Pre. Hacer, R. S. Haem-J, A. (8. Omal lag-lengh choce n able unable VAR model under uaon of homocedac ARCH, "Journal of Aled Sac", 35(6, Helmu L. (5. New Inroducon o Mulle me Sere Anal. Srnger. Runle, D. E. (987. Vecor Auoregreon Real. Journal of Bune Economc Sac, 5 (4, Soc, J.H. M.W. Waon (. Vecor Auoregreon. Journal of Economc Perecve, 5, -5. a, R. (. Anal of Fnancal me Sere. John Wle & Son. New Yor. Waler E. (3. Aled Economerc me Sere, nd Edon, John Wle & Son M III-45
Advanced time-series analysis (University of Lund, Economic History Department)
Advanced me-seres analss (Unvers of Lund, Economc Hsor Dearmen) 3 Jan-3 Februar and 6-3 March Lecure 4 Economerc echnues for saonar seres : Unvarae sochasc models wh Box- Jenns mehodolog, smle forecasng
More informationTesting of Markov Assumptions Based on the Dynamic Specification Test
Aca olechnca Hungarca Vol. 8 o. 3 0 Teng of Markov Aumon Baed on he Dnamc Secfcaon Te Jana Lenčuchová Dearmen of Mahemac Facul of Cvl Engneerng Slovak Unver of Technolog Bralava Radkého 83 68 Bralava Slovaka
More informationLecture 18: The Laplace Transform (See Sections and 14.7 in Boas)
Lecure 8: The Lalace Transform (See Secons 88- and 47 n Boas) Recall ha our bg-cure goal s he analyss of he dfferenal equaon, ax bx cx F, where we emloy varous exansons for he drvng funcon F deendng on
More informationNPTEL Project. Econometric Modelling. Module23: Granger Causality Test. Lecture35: Granger Causality Test. Vinod Gupta School of Management
P age NPTEL Proec Economerc Modellng Vnod Gua School of Managemen Module23: Granger Causaly Tes Lecure35: Granger Causaly Tes Rudra P. Pradhan Vnod Gua School of Managemen Indan Insue of Technology Kharagur,
More information(,,, ) (,,, ). In addition, there are three other consumers, -2, -1, and 0. Consumer -2 has the utility function
MACROECONOMIC THEORY T J KEHOE ECON 87 SPRING 5 PROBLEM SET # Conder an overlappng generaon economy le ha n queon 5 on problem e n whch conumer lve for perod The uly funcon of he conumer born n perod,
More informationJohn Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany
Herarchcal Markov Normal Mxure models wh Applcaons o Fnancal Asse Reurns Appendx: Proofs of Theorems and Condonal Poseror Dsrbuons John Geweke a and Gann Amsano b a Deparmens of Economcs and Sascs, Unversy
More informationA Demand System for Input Factors when there are Technological Changes in Production
A Demand Syem for Inpu Facor when here are Technologcal Change n Producon Movaon Due o (e.g.) echnologcal change here mgh no be a aonary relaonhp for he co hare of each npu facor. When emang demand yem
More informationA Random Walk through Seasonal Adjustment: Noninvertible Moving Averages and Unit Root Tests
CONFERENCE ON SEASONAIY, SEASONA ADJUSMEN AND HEIR IMPICAIONS FOR SHOR-ERM ANAYSIS AND FORECASING 0- MAY 006 A Random al hrogh Seaonal Admen: Nonnverble Movng Average and Un Roo e oma del Barro Caro Dene
More informationEP2200 Queuing theory and teletraffic systems. 3rd lecture Markov chains Birth-death process - Poisson process. Viktoria Fodor KTH EES
EP Queung heory and eleraffc sysems 3rd lecure Marov chans Brh-deah rocess - Posson rocess Vora Fodor KTH EES Oulne for oday Marov rocesses Connuous-me Marov-chans Grah and marx reresenaon Transen and
More informationTHEORETICAL AUTOCORRELATIONS. ) if often denoted by γ. Note that
THEORETICAL AUTOCORRELATIONS Cov( y, y ) E( y E( y))( y E( y)) ρ = = Var( y) E( y E( y)) =,, L ρ = and Cov( y, y ) s ofen denoed by whle Var( y ) f ofen denoed by γ. Noe ha γ = γ and ρ = ρ and because
More informationSolution in semi infinite diffusion couples (error function analysis)
Soluon n sem nfne dffuson couples (error funcon analyss) Le us consder now he sem nfne dffuson couple of wo blocks wh concenraon of and I means ha, n a A- bnary sysem, s bondng beween wo blocks made of
More informationNotes on the stability of dynamic systems and the use of Eigen Values.
Noes on he sabl of dnamc ssems and he use of Egen Values. Source: Macro II course noes, Dr. Davd Bessler s Tme Seres course noes, zarads (999) Ineremporal Macroeconomcs chaper 4 & Techncal ppend, and Hamlon
More informationSeasonal Time Series and Transfer Function Modelling for Natural Rubber Forecasting in India
Inernaonal Journal of Comuer Trend and Technology (IJCTT) - volume4 Iue5 May 03 Seaonal Tme Sere and Tranfer Funcon Modellng for Naural Ruer Forecang n Inda P. Arumugam #, V. Anhakumar * # Reader Dearmen
More informationJanuary Examinations 2012
Page of 5 EC79 January Examnaons No. of Pages: 5 No. of Quesons: 8 Subjec ECONOMICS (POSTGRADUATE) Tle of Paper EC79 QUANTITATIVE METHODS FOR BUSINESS AND FINANCE Tme Allowed Two Hours ( hours) Insrucons
More informationANALYSIS AND MODELING OF HYDROLOGIC TIME SERIES. Wasserhaushalt Time Series Analysis and Stochastic Modelling Spring Semester
ANALYSIS AND MODELING OF HYDROLOGIC TIME SERIES Waerhauhal Tme Sere Analy and Sochac Modellng Sprng Semeer 8 ANALYSIS AND MODELING OF HYDROLOGIC TIME SERIES Defnon Wha a me ere? Leraure: Sala, J.D. 99,
More informationCS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4
CS434a/54a: Paern Recognon Prof. Olga Veksler Lecure 4 Oulne Normal Random Varable Properes Dscrmnan funcons Why Normal Random Varables? Analycally racable Works well when observaon comes form a corruped
More informationThruster Modulation for Unsymmetric Flexible Spacecraft with Consideration of Torque Arm Perturbation
hruer Modulaon for Unymmerc Flexble Sacecraf wh onderaon of orue rm Perurbaon a Shgemune anwak Shnchro chkawa a Yohak hkam b a Naonal Sace evelomen gency of Jaan 2-- Sengen ukuba-h barak b eo Unvery 3--
More informationRobustness Experiments with Two Variance Components
Naonal Insue of Sandards and Technology (NIST) Informaon Technology Laboraory (ITL) Sascal Engneerng Dvson (SED) Robusness Expermens wh Two Varance Componens by Ana Ivelsse Avlés avles@ns.gov Conference
More informationPHYS 705: Classical Mechanics. Canonical Transformation
PHYS 705: Classcal Mechancs Canoncal Transformaon Canoncal Varables and Hamlonan Formalsm As we have seen, n he Hamlonan Formulaon of Mechancs,, are ndeenden varables n hase sace on eual foong The Hamlon
More informationFI 3103 Quantum Physics
/9/4 FI 33 Quanum Physcs Aleander A. Iskandar Physcs of Magnesm and Phooncs Research Grou Insu Teknolog Bandung Basc Conces n Quanum Physcs Probably and Eecaon Value Hesenberg Uncerany Prncle Wave Funcon
More informationEcon107 Applied Econometrics Topic 5: Specification: Choosing Independent Variables (Studenmund, Chapter 6)
Econ7 Appled Economercs Topc 5: Specfcaon: Choosng Independen Varables (Sudenmund, Chaper 6 Specfcaon errors ha we wll deal wh: wrong ndependen varable; wrong funconal form. Ths lecure deals wh wrong ndependen
More informationApplication of ARIMA Model for River Discharges Analysis
Alcaon of ARIMA Model for Rver Dscharges Analyss Bhola NS Ghmre Journal of Neal Physcal Socey Volume 4, Issue 1, February 17 ISSN: 39-473X Edors: Dr. Go Chandra Kahle Dr. Devendra Adhkar Mr. Deeendra Parajul
More information( ) () we define the interaction representation by the unitary transformation () = ()
Hgher Order Perurbaon Theory Mchael Fowler 3/7/6 The neracon Represenaon Recall ha n he frs par of hs course sequence, we dscussed he chrödnger and Hesenberg represenaons of quanum mechancs here n he chrödnger
More information( t) Outline of program: BGC1: Survival and event history analysis Oslo, March-May Recapitulation. The additive regression model
BGC1: Survval and even hsory analyss Oslo, March-May 212 Monday May 7h and Tuesday May 8h The addve regresson model Ørnulf Borgan Deparmen of Mahemacs Unversy of Oslo Oulne of program: Recapulaon Counng
More informationLecture 6: Learning for Control (Generalised Linear Regression)
Lecure 6: Learnng for Conrol (Generalsed Lnear Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure 6: RLSC - Prof. Sehu Vjayakumar Lnear Regresson
More informationCHAPTER 5: MULTIVARIATE METHODS
CHAPER 5: MULIVARIAE MEHODS Mulvarae Daa 3 Mulple measuremens (sensors) npus/feaures/arbues: -varae N nsances/observaons/eamples Each row s an eample Each column represens a feaure X a b correspons o he
More informationCooling of a hot metal forging. , dt dt
Tranen Conducon Uneady Analy - Lumped Thermal Capacy Model Performed when; Hea ranfer whn a yem produced a unform emperaure drbuon n he yem (mall emperaure graden). The emperaure change whn he yem condered
More informationDesign of Recursive Digital Filters IIR
Degn of Recurve Dgtal Flter IIR The outut from a recurve dgtal flter deend on one or more revou outut value, a well a on nut t nvolve feedbac. A recurve flter ha an nfnte mule reone (IIR). The mulve reone
More informationIn the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!") i+1,q - [(!
ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL The frs hng o es n wo-way ANOVA: Is here neracon? "No neracon" means: The man effecs model would f. Ths n urn means: In he neracon plo (wh A on he horzonal
More informationLecture VI Regression
Lecure VI Regresson (Lnear Mehods for Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure VI: MLSC - Dr. Sehu Vjayakumar Lnear Regresson Model M
More informationControl Systems. Mathematical Modeling of Control Systems.
Conrol Syem Mahemacal Modelng of Conrol Syem chbum@eoulech.ac.kr Oulne Mahemacal model and model ype. Tranfer funcon model Syem pole and zero Chbum Lee -Seoulech Conrol Syem Mahemacal Model Model are key
More informationEndogeneity. Is the term given to the situation when one or more of the regressors in the model are correlated with the error term such that
s row Endogeney Is he erm gven o he suaon when one or more of he regressors n he model are correlaed wh he error erm such ha E( u 0 The 3 man causes of endogeney are: Measuremen error n he rgh hand sde
More informationV.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS
R&RATA # Vol.) 8, March FURTHER AALYSIS OF COFIDECE ITERVALS FOR LARGE CLIET/SERVER COMPUTER ETWORKS Vyacheslav Abramov School of Mahemacal Scences, Monash Unversy, Buldng 8, Level 4, Clayon Campus, Wellngon
More information( ) [ ] MAP Decision Rule
Announcemens Bayes Decson Theory wh Normal Dsrbuons HW0 due oday HW o be assgned soon Proec descrpon posed Bomercs CSE 90 Lecure 4 CSE90, Sprng 04 CSE90, Sprng 04 Key Probables 4 ω class label X feaure
More informationBOX-JENKINS MODEL NOTATION. The Box-Jenkins ARMA(p,q) model is denoted by the equation. pwhile the moving average (MA) part of the model is θ1at
BOX-JENKINS MODEL NOAION he Box-Jenkins ARMA(,q) model is denoed b he equaion + + L+ + a θ a L θ a 0 q q. () he auoregressive (AR) ar of he model is + L+ while he moving average (MA) ar of he model is
More informationChapter 6: AC Circuits
Chaper 6: AC Crcus Chaper 6: Oulne Phasors and he AC Seady Sae AC Crcus A sable, lnear crcu operang n he seady sae wh snusodal excaon (.e., snusodal seady sae. Complee response forced response naural response.
More informationOnline Appendix for. Strategic safety stocks in supply chains with evolving forecasts
Onlne Appendx for Sraegc safey socs n supply chans wh evolvng forecass Tor Schoenmeyr Sephen C. Graves Opsolar, Inc. 332 Hunwood Avenue Hayward, CA 94544 A. P. Sloan School of Managemen Massachuses Insue
More informationVector autoregression VAR. Case 1
Vecor auoregression VAR So far we have focused mosl on models where deends onl on as. More generall we migh wan o consider oin models ha involve more han one variable. There are wo reasons: Firs, we migh
More informationSeasonal Adjustment Programs. 1. Introduction
Bernhard Hammer 010394 Klau Prener 001418 Seaonal Adumen Program Chaper 4: "The Economerc Anal of Seaonal Tme Sere (Ghel, Oborn, 001) 1. Inroducon A procedure ha fler he eaonal flucuaon from a me ere called
More informationPattern Classification (III) & Pattern Verification
Preare by Prof. Hu Jang CSE638 --4 CSE638 3. Seech & Language Processng o.5 Paern Classfcaon III & Paern Verfcaon Prof. Hu Jang Dearmen of Comuer Scence an Engneerng York Unversy Moel Parameer Esmaon Maxmum
More informationIntroduction. Modeling Data. Approach. Quality of Fit. Likelihood. Probabilistic Approach
Introducton Modelng Data Gven a et of obervaton, we wh to ft a mathematcal model Model deend on adutable arameter traght lne: m + c n Polnomal: a + a + a + L+ a n Choce of model deend uon roblem Aroach
More informationRELATIONSHIP BETWEEN VOLATILITY AND TRADING VOLUME: THE CASE OF HSI STOCK RETURNS DATA
RELATIONSHIP BETWEEN VOLATILITY AND TRADING VOLUME: THE CASE OF HSI STOCK RETURNS DATA Mchaela Chocholaá Unversy of Economcs Braslava, Slovaka Inroducon (1) one of he characersc feaures of sock reurns
More information. The geometric multiplicity is dim[ker( λi. number of linearly independent eigenvectors associated with this eigenvalue.
Lnear Algebra Lecure # Noes We connue wh he dscusson of egenvalues, egenvecors, and dagonalzably of marces We wan o know, n parcular wha condons wll assure ha a marx can be dagonalzed and wha he obsrucons
More informationH = d d q 1 d d q N d d p 1 d d p N exp
8333: Sacal Mechanc I roblem Se # 7 Soluon Fall 3 Canoncal Enemble Non-harmonc Ga: The Hamlonan for a ga of N non neracng parcle n a d dmenonal box ha he form H A p a The paron funcon gven by ZN T d d
More informationJ i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes.
umercal negraon of he dffuson equaon (I) Fne dfference mehod. Spaal screaon. Inernal nodes. R L V For hermal conducon le s dscree he spaal doman no small fne spans, =,,: Balance of parcles for an nernal
More informationTHE PREDICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS
THE PREICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS INTROUCTION The wo dmensonal paral dfferenal equaons of second order can be used for he smulaon of compeve envronmen n busness The arcle presens he
More informationMath 128b Project. Jude Yuen
Mah 8b Proec Jude Yuen . Inroducon Le { Z } be a sequence of observed ndependen vecor varables. If he elemens of Z have a on normal dsrbuon hen { Z } has a mean vecor Z and a varancecovarance marx z. Geomercally
More informationAppendix H: Rarefaction and extrapolation of Hill numbers for incidence data
Anne Chao Ncholas J Goell C seh lzabeh L ander K Ma Rober K Colwell and Aaron M llson 03 Rarefacon and erapolaon wh ll numbers: a framewor for samplng and esmaon n speces dversy sudes cology Monographs
More informationLecture 11: Stereo and Surface Estimation
Lecure : Sereo and Surface Emaon When camera poon have been deermned, ung rucure from moon, we would lke o compue a dene urface model of he cene. In h lecure we wll udy he o called Sereo Problem, where
More informationOpen Chemical Systems and Their Biological Function. Department of Applied Mathematics University of Washington
Oen Chemcal Syem and Ther Bologcal Funcon Hong Qan Dearmen of Aled Mahemac Unvery of Wahngon Dynamc and Thermodynamc of Sochac Nonlnear Meococ Syem Meococ decron of hycal and chemcal yem: Gbb 1870-1890
More informationOrdinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s
Ordnary Dfferenal Equaons n Neuroscence wh Malab eamples. Am - Gan undersandng of how o se up and solve ODE s Am Undersand how o se up an solve a smple eample of he Hebb rule n D Our goal a end of class
More informationImprovement in Estimating Population Mean using Two Auxiliary Variables in Two-Phase Sampling
Improvemen n Esmang Populaon Mean usng Two Auxlar Varables n Two-Phase amplng Rajesh ngh Deparmen of ascs, Banaras Hndu Unvers(U.P.), Inda (rsnghsa@ahoo.com) Pankaj Chauhan and Nrmala awan chool of ascs,
More informationNormal Random Variable and its discriminant functions
Noral Rando Varable and s dscrnan funcons Oulne Noral Rando Varable Properes Dscrnan funcons Why Noral Rando Varables? Analycally racable Works well when observaon coes for a corruped snle prooype 3 The
More informationAdvanced Machine Learning & Perception
Advanced Machne Learnng & Percepon Insrucor: Tony Jebara SVM Feaure & Kernel Selecon SVM Eensons Feaure Selecon (Flerng and Wrappng) SVM Feaure Selecon SVM Kernel Selecon SVM Eensons Classfcaon Feaure/Kernel
More informationF-Tests and Analysis of Variance (ANOVA) in the Simple Linear Regression Model. 1. Introduction
ECOOMICS 35* -- OTE 9 ECO 35* -- OTE 9 F-Tess and Analyss of Varance (AOVA n he Smple Lnear Regresson Model Inroducon The smple lnear regresson model s gven by he followng populaon regresson equaon, or
More informationOP = OO' + Ut + Vn + Wb. Material We Will Cover Today. Computer Vision Lecture 3. Multi-view Geometry I. Amnon Shashua
Comuer Vson 27 Lecure 3 Mul-vew Geomer I Amnon Shashua Maeral We Wll Cover oa he srucure of 3D->2D rojecon mar omograh Marces A rmer on rojecve geomer of he lane Eolar Geomer an Funamenal Mar ebrew Unvers
More informationTSS = SST + SSE An orthogonal partition of the total SS
ANOVA: Topc 4. Orhogonal conrass [ST&D p. 183] H 0 : µ 1 = µ =... = µ H 1 : The mean of a leas one reamen group s dfferen To es hs hypohess, a basc ANOVA allocaes he varaon among reamen means (SST) equally
More informationDepartment of Economics University of Toronto
Deparmen of Economcs Unversy of Torono ECO408F M.A. Economercs Lecure Noes on Heeroskedascy Heeroskedascy o Ths lecure nvolves lookng a modfcaons we need o make o deal wh he regresson model when some of
More informationChapter Lagrangian Interpolation
Chaper 5.4 agrangan Inerpolaon Afer readng hs chaper you should be able o:. dere agrangan mehod of nerpolaon. sole problems usng agrangan mehod of nerpolaon and. use agrangan nerpolans o fnd deraes and
More informationNew M-Estimator Objective Function. in Simultaneous Equations Model. (A Comparative Study)
Inernaonal Mahemacal Forum, Vol. 8, 3, no., 7 - HIKARI Ld, www.m-hkar.com hp://dx.do.org/.988/mf.3.3488 New M-Esmaor Objecve Funcon n Smulaneous Equaons Model (A Comparave Sudy) Ahmed H. Youssef Professor
More informationImprovement in Estimating Population Mean using Two Auxiliary Variables in Two-Phase Sampling
Rajesh ngh Deparmen of ascs, Banaras Hndu Unvers(U.P.), Inda Pankaj Chauhan, Nrmala awan chool of ascs, DAVV, Indore (M.P.), Inda Florenn marandache Deparmen of Mahemacs, Unvers of New Meco, Gallup, UA
More informationVariants of Pegasos. December 11, 2009
Inroducon Varans of Pegasos SooWoong Ryu bshboy@sanford.edu December, 009 Youngsoo Cho yc344@sanford.edu Developng a new SVM algorhm s ongong research opc. Among many exng SVM algorhms, we wll focus on
More information. The geometric multiplicity is dim[ker( λi. A )], i.e. the number of linearly independent eigenvectors associated with this eigenvalue.
Mah E-b Lecure #0 Noes We connue wh he dscusson of egenvalues, egenvecors, and dagonalzably of marces We wan o know, n parcular wha condons wll assure ha a marx can be dagonalzed and wha he obsrucons are
More informationEfficient Estimators for Population Variance using Auxiliary Information
Global Joural of Mahemacal cece: Theor ad Praccal. IN 97-3 Volume 3, Number (), pp. 39-37 Ieraoal Reearch Publcao Houe hp://www.rphoue.com Effce Emaor for Populao Varace ug Aular Iformao ubhah Kumar Yadav
More informationCHAPTER FOUR REPEATED MEASURES IN TOXICITY TESTING
CHAPTER FOUR REPEATED MEASURES IN TOXICITY TESTING 4. Inroducon The repeaed measures sudy s a very commonly used expermenal desgn n oxcy esng because no only allows one o nvesgae he effecs of he oxcans,
More informationChapter 5 Signal-Space Analysis
Chaper 5 Sgnal-Space Analy Sgnal pace analy provde a mahemacally elegan and hghly nghful ool for he udy of daa ranmon. 5. Inroducon o Sacal model for a genec dgal communcaon yem n eage ource: A pror probable
More informationVolatility Interpolation
Volaly Inerpolaon Prelmnary Verson March 00 Jesper Andreasen and Bran Huge Danse Mares, Copenhagen wan.daddy@danseban.com brno@danseban.com Elecronc copy avalable a: hp://ssrn.com/absrac=69497 Inro Local
More informationData Collection Definitions of Variables - Conceptualize vs Operationalize Sample Selection Criteria Source of Data Consistency of Data
Apply Sascs and Economercs n Fnancal Research Obj. of Sudy & Hypoheses Tesng From framework objecves of sudy are needed o clarfy, hen, n research mehodology he hypoheses esng are saed, ncludng esng mehods.
More informationChapter 8 Dynamic Models
Chaper 8 Dnamc odels 8. Inroducon 8. Seral correlaon models 8.3 Cross-seconal correlaons and me-seres crosssecon models 8.4 me-varng coeffcens 8.5 Kalman fler approach 8. Inroducon When s mporan o consder
More informationPanel Data Regression Models
Panel Daa Regresson Models Wha s Panel Daa? () Mulple dmensoned Dmensons, e.g., cross-secon and me node-o-node (c) Pongsa Pornchawseskul, Faculy of Economcs, Chulalongkorn Unversy (c) Pongsa Pornchawseskul,
More informationCHAPTER 10: LINEAR DISCRIMINATION
CHAPER : LINEAR DISCRIMINAION Dscrmnan-based Classfcaon 3 In classfcaon h K classes (C,C,, C k ) We defned dscrmnan funcon g j (), j=,,,k hen gven an es eample, e chose (predced) s class label as C f g
More informationIdentification and estimation of causal factor models of stationary time series
Idencaon and emaon o caual acor model o aonary me ere CRI EATON AND VICTOR OLO Dearmen o Economc, Macquare Unvery and chool o Economc, Unvery o New ouh Wale, E-mal: cheaon@e.mq.edu.au chool o Elecrcal
More informationLecture 11 SVM cont
Lecure SVM con. 0 008 Wha we have done so far We have esalshed ha we wan o fnd a lnear decson oundary whose margn s he larges We know how o measure he margn of a lnear decson oundary Tha s: he mnmum geomerc
More informationOutline. Energy-Efficient Target Coverage in Wireless Sensor Networks. Sensor Node. Introduction. Characteristics of WSN
Ener-Effcen Tare Coverae n Wreless Sensor Newors Presened b M Trà Tá -4-4 Inroducon Bacround Relaed Wor Our Proosal Oulne Maxmum Se Covers (MSC) Problem MSC Problem s NP-Comlee MSC Heursc Concluson Sensor
More informationA. Inventory model. Why are we interested in it? What do we really study in such cases.
Some general yem model.. Inenory model. Why are we nereed n? Wha do we really udy n uch cae. General raegy of machng wo dmlar procee, ay, machng a fa proce wh a low one. We need an nenory or a buffer or
More information10. A.C CIRCUITS. Theoretically current grows to maximum value after infinite time. But practically it grows to maximum after 5τ. Decay of current :
. A. IUITS Synopss : GOWTH OF UNT IN IUIT : d. When swch S s closed a =; = d. A me, curren = e 3. The consan / has dmensons of me and s called he nducve me consan ( τ ) of he crcu. 4. = τ; =.63, n one
More information[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5
TPG460 Reservor Smulaon 08 page of 5 DISCRETIZATIO OF THE FOW EQUATIOS As we already have seen, fne dfference appromaons of he paral dervaves appearng n he flow equaons may be obaned from Taylor seres
More informationMachine Learning 2nd Edition
INTRODUCTION TO Lecure Sldes for Machne Learnng nd Edon ETHEM ALPAYDIN, modfed by Leonardo Bobadlla and some pars from hp://www.cs.au.ac.l/~aparzn/machnelearnng/ The MIT Press, 00 alpaydn@boun.edu.r hp://www.cmpe.boun.edu.r/~ehem/mle
More informationInverse Joint Moments of Multivariate. Random Variables
In J Conem Mah Scences Vol 7 0 no 46 45-5 Inverse Jon Momens of Mulvarae Rom Varables M A Hussan Dearmen of Mahemacal Sascs Insue of Sascal Sudes Research ISSR Caro Unversy Egy Curren address: Kng Saud
More informationSimple fuzzy adaptive control for a class of nonlinear plants
Smle uzz adave conrol or a cla o nonlnear lan SAŠO BLAŽIČ, IGOR ŠKRJANC, DRAGO MAKO Facul o Elecrcal Engneerng Unver o Ljuljana ržaška 5, SI- Ljuljana SLOVENIA Arac: A uzz adave conrol algorhm reened n
More informationGENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS. Youngwoo Ahn and Kitae Kim
Korean J. Mah. 19 (2011), No. 3, pp. 263 272 GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS Youngwoo Ahn and Kae Km Absrac. In he paper [1], an explc correspondence beween ceran
More informationGraduate Macroeconomics 2 Problem set 5. - Solutions
Graduae Macroeconomcs 2 Problem se. - Soluons Queson 1 To answer hs queson we need he frms frs order condons and he equaon ha deermnes he number of frms n equlbrum. The frms frs order condons are: F K
More informationEE241 - Spring 2003 Advanced Digital Integrated Circuits
EE4 EE4 - rn 00 Advanced Dal Ineraed rcus Lecure 9 arry-lookahead Adders B. Nkolc, J. Rabaey arry-lookahead Adders Adder rees» Radx of a ree» Mnmum deh rees» arse rees Loc manulaons» onvenonal vs. Ln»
More informationUNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 2017 EXAMINATION
INTERNATIONAL TRADE T. J. KEHOE UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 27 EXAMINATION Please answer wo of he hree quesons. You can consul class noes, workng papers, and arcles whle you are workng on he
More informationTesting a new idea to solve the P = NP problem with mathematical induction
Tesng a new dea o solve he P = NP problem wh mahemacal nducon Bacground P and NP are wo classes (ses) of languages n Compuer Scence An open problem s wheher P = NP Ths paper ess a new dea o compare he
More informationOn One Analytic Method of. Constructing Program Controls
Appled Mahemacal Scences, Vol. 9, 05, no. 8, 409-407 HIKARI Ld, www.m-hkar.com hp://dx.do.org/0.988/ams.05.54349 On One Analyc Mehod of Consrucng Program Conrols A. N. Kvko, S. V. Chsyakov and Yu. E. Balyna
More informationIs it necessary to seasonally adjust business and consumer surveys. Emmanuelle Guidetti
Is necessar o seasonall adjs bsness and consmer srves Emmanelle Gde Olne 1 BTS feares 2 Smlaon eercse 3 Seasonal ARIMA modellng 4 Conclsons Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01
More informationRobust and Accurate Cancer Classification with Gene Expression Profiling
Robus and Accurae Cancer Classfcaon wh Gene Expresson Proflng (Compuaonal ysems Bology, 2005) Auhor: Hafeng L, Keshu Zhang, ao Jang Oulne Background LDA (lnear dscrmnan analyss) and small sample sze problem
More informationMOLP MOLP MOLP Corresponding author,
h://jnrm.rbau.ac.r پژوهشهای نوین در ریاضی دانشگاه آزاد اسالمی واحد علوم و تحقیقات * Correondng auhor, Emal: j.val@abrzu.ac.r. * تخمین x x x, c x e d.. A x B b.. A x B b, 2 2 2 A d,, e n2 cx, n A 2 mn2
More information8th WSEAS International Conference on SIMULATION, MODELLING and OPTIMIZATION (SMO '08) Santander, Cantabria, Spain, September 23-25, 2008
8h WSEAS Inernaonal Conference on SIMULAION, MODELLING and OPIMIZAION (SMO 08) Sanander, Canabra, San, Seeber 3-5, 008 Obervably of 4h Order Dynacal Sye Jerzy Sefan Reonde Faculy of Auoac Conrol, Elecronc
More informationUsing a Prediction Error Criterion for Model Selection in Forecasting Option Prices
Ung a Predcon Error Creron for Model Selecon n Forecang Opon Prce Savro Degannak and Evdoka Xekalak Deparmen of Sac, Ahen Unvery of Economc and Bune, 76, Paon Sree, 0434 Ahen, Greece echncal Repor no 3,
More informationLecture Notes 4. Univariate Forecasting and the Time Series Properties of Dynamic Economic Models
Tme Seres Seven N. Durlauf Unversy of Wsconsn Lecure Noes 4. Unvarae Forecasng and he Tme Seres Properes of Dynamc Economc Models Ths se of noes presens does hree hngs. Frs, formulas are developed o descrbe
More informationMechanics Physics 151
Mechancs Physcs 5 Lecure 9 Hamlonan Equaons of Moon (Chaper 8) Wha We Dd Las Tme Consruced Hamlonan formalsm H ( q, p, ) = q p L( q, q, ) H p = q H q = p H = L Equvalen o Lagrangan formalsm Smpler, bu
More informationLinear Response Theory: The connection between QFT and experiments
Phys540.nb 39 3 Lnear Response Theory: The connecon beween QFT and expermens 3.1. Basc conceps and deas Q: ow do we measure he conducvy of a meal? A: we frs nroduce a weak elecrc feld E, and hen measure
More informationSOME NOISELESS CODING THEOREMS OF INACCURACY MEASURE OF ORDER α AND TYPE β
SARAJEVO JOURNAL OF MATHEMATICS Vol.3 (15) (2007), 137 143 SOME NOISELESS CODING THEOREMS OF INACCURACY MEASURE OF ORDER α AND TYPE β M. A. K. BAIG AND RAYEES AHMAD DAR Absrac. In hs paper, we propose
More informationOutline. Probabilistic Model Learning. Probabilistic Model Learning. Probabilistic Model for Time-series Data: Hidden Markov Model
Probablsc Model for Tme-seres Daa: Hdden Markov Model Hrosh Mamsuka Bonformacs Cener Kyoo Unversy Oulne Three Problems for probablsc models n machne learnng. Compung lkelhood 2. Learnng 3. Parsng (predcon
More informationGMM parameter estimation. Xiaoye Lu CMPS290c Final Project
GMM paraeer esaon Xaoye Lu M290c Fnal rojec GMM nroducon Gaussan ure Model obnaon of several gaussan coponens Noaon: For each Gaussan dsrbuon:, s he ean and covarance ar. A GMM h ures(coponens): p ( 2π
More informationMechanics Physics 151
Mechancs Physcs 5 Lecure 9 Hamlonan Equaons of Moon (Chaper 8) Wha We Dd Las Tme Consruced Hamlonan formalsm Hqp (,,) = qp Lqq (,,) H p = q H q = p H L = Equvalen o Lagrangan formalsm Smpler, bu wce as
More informationA HIERARCHICAL KALMAN FILTER
A HIERARCHICAL KALMAN FILER Greg aylor aylor Fry Consulng Acuares Level 8, 3 Clarence Sree Sydney NSW Ausrala Professoral Assocae, Cenre for Acuaral Sudes Faculy of Economcs and Commerce Unversy of Melbourne
More information, the. L and the L. x x. max. i n. It is easy to show that these two norms satisfy the following relation: x x n x = (17.3) max
ecure 8 7. Sabiliy Analyi For an n dimenional vecor R n, he and he vecor norm are defined a: = T = i n i (7.) I i eay o how ha hee wo norm aify he following relaion: n (7.) If a vecor i ime-dependen, hen
More information