Final Exam Applied Econometrics
|
|
- Arron Cross
- 6 years ago
- Views:
Transcription
1 Fal Eam Appled Ecoomercs. 0 Sppose we have he followg regresso resl: Depede Varable: SAT Sample: 437 Iclded observaos: 437 Whe heeroskedasc-cosse sadard errors & covarace Varable Coeffce Sd. Error -Sasc Prob. C HSIE HSIE^ FEMALE BLACK FEMALE*BLACK R-sqared Mea depede var Adjsed R-sqared S.D. depede var S.E. of regresso Akake fo crero.6556 Sm sqared resd Schwar crero Log lkelhood Haa-Q crer..688 F-sasc Drb-Waso sa ProbF-sasc Here, Score s he combed SAT score, Hse s se of he sde s hgh school gradag class, hdreds, Female s a geder dmm varable, ad Black s a race dmm varable. Is here srog evdece ha Hse^ shold be clded he model? Compe he opmal hgh school se. 3 Holdg oher varables fed, wha s he esmaed dfferece SAT score bewee oblack females ad oblack males? How sascall sgfca hs esmaed dfferece? 4 Wha s he esmaed dfferece SAT score bewee black females ad oblack females? Wha wold o eed o do o es wheher he dfferece s sgfca?
2 . 0 We are eresed esmag he hedoc prcg model of hose as follows: logprce α + * Sqrf + γ * Bdrms +, where Prce s he hose prce, Sqrf sqare fooage, ad Bdrms he mber of bedrooms. Depede Varable: LOGPRICE Mehod: Leas Sqares Sample: 88 Iclded observaos: 88 Varable Coeffce Sd. Error -Sasc Prob. C SQRFT E BDRMS R-sqared Mea depede var Adjsed R-sqared S.D. depede var S.E. of regresso Akake fo crero Sm sqared resd Schwar crero Log lkelhood F-sasc Drb-Waso sa ProbF-sasc How well does he model epla he acal daa of hose prce? Evalae he regresso erms of dvdal ad overall sgfcace. How ca we mprove he relevac of he model? Predc he perceage chage prce whe a 50-sqare-foo bedroom s added o a hose. 3 Le θ 50 + γ deoe he perceage chage prce whe a 50-sqare-foo bedroom s added o a hose. Show ha or model ca be wre as follows: logpr ce α + * Sqrf 50* Bdrms + θ * Bdrms +. 4 Usg he followg able, provde a 95% cofdece erval of θ. Depede Varable: LOGPRICE Mehod: Leas Sqares Sample: 88 Iclded observaos: 88 Varable Coeffce Sd. Error -Sasc Prob. C SQRFT-50*BDRMS E
3 BDRMS R-sqared Mea depede var Adjsed R-sqared S.D. depede var S.E. of regresso Akake fo crero Sm sqared resd Schwar crero Log lkelhood F-sasc Drb-Waso sa ProbF-sasc We wa o compare wo models. Model U ses he cosa,, ad as regressors whle Model R s based o he cosa ad. Model U γ α,,...,, Sppose we have he followg: 00, 00, 00, 90, 0, 0, ad. Esmae he parameers ad γ from Model U. H: ] [ ] [ Calclae he resdal sm of sqares e RSS. H: e γ γ 3 Calclae he R-sqared coeffce R. Sppose we om he varable ad esmae Model R. Model R + + α,,...,,
4 4 Esmae he parameer from Model R. How s dffere from he former? How come do we have hs resl? H: e 5 Calclae he resdal sm of sqares? H: e RSS. How s dffere from he former 6 Calclae he R-sqared coeffce R r. How s dffere from he former 3? Epla wha cases hs dfferece. 7 Calclae he F-sasc ad es he ll hpohess H 0 : γ 0 a he 5% se. RSSr RSS / q H: F RSS / r 8 Show ha he same resl comes from he followg: R Rr / q F. R / r r 4. 0 Le UNRATE deoe he emplome rae ad le GRATE deoe he perceage chage gross domesc prodc. Cosder a damc relaoshp bewee he growh rae ad he emplome rae: UNRATE + α + * UNRATE + γ * GRATE + δ * GRATE where UNRATE ad GRATE are he lagged varables. Depede Varable: UNRATE Sample: 960Q 999Q4 Iclded observaos: 60 Varable Coeffce Sd. Error -Sasc Prob. C UNRATE GRATE GRATE R-sqared Mea depede var Adjsed R-sqared S.D. depede var
5 S.E. of regresso Akake fo crero Sm sqared resd Schwar crero Log lkelhood F-sasc Drb-Waso sa.5390 ProbF-sasc The shor-r margal effec of he growh rae o he emplome rae s defed as γ. From he Table above, es he sgfcace of he esmae of he shor-r margal effec. How does he emplome rae respod o he chage growh rae he shor r? γ + δ 3 The log-r margal effec s defed as. Compe he log-r margal effec. 4 We wa o es f here s o dfferece he shor-r ad he log-r margal effecs. Se p he ll hpohess ad epla he procedre of hpohess esg To es he effecveess of a job rag program o he sbseqe wages of workers, we specf he model log Wage α + Tra + γedc + δeper +, where Tra s a bar varable eqal o f a worker parcpaed he program. Sppose he error erm coas observed worker abl. Assme less able workers have a greaer chace of beg seleced for he program. Wha do o epec abo he basedess of he OLS esmaor of? Wha ca o sa abo he lkel bas he OLS esmaor of? 6. 0 Cosder he hosehold cosmpo fco: α + + where, deoes he cosmpo ad come of hosehold, respecvel. Sppose ε, where E ε 0, Var ε, ad ε s depede of. Show ha E 0, so ha eogee holds. Oba Var. Does he error sasf he whe ose error codo? 3 Epla he coseqeces of he OLS esmaor erms of basedess ad effcec. 4 Epla he bes opo ad he secod-bes opo o deal wh.
6
7
Practice Final Exam (corrected formulas, 12/10 11AM)
Ecoomc Meze. Ch Fall Socal Scece 78 Uvery of Wco-Mado Pracce Fal Eam (correced formula, / AM) Awer all queo he (hree) bluebook provded. Make cera you wre your ame, your ude I umber, ad your TA ame o all
More information3/3/2014. CDS M Phil Econometrics. Heteroskedasticity is a problem where the error terms do not have a constant variance.
3/3/4 a Plla N OS Volao of Assmpos Assmpo of Sphercal Dsrbaces Var T T I Var O Cov, j, j,..., Therefore he reqreme for sphercal dsrbaces s ad j I O homoskedascy No aocorrelao Heeroskedascy: Defo Heeroscedascy
More informationChapter 8. Simple Linear Regression
Chaper 8. Smple Lear Regresso Regresso aalyss: regresso aalyss s a sascal mehodology o esmae he relaoshp of a respose varable o a se of predcor varable. whe here s jus oe predcor varable, we wll use smple
More information(1) Cov(, ) E[( E( ))( E( ))]
Impac of Auocorrelao o OLS Esmaes ECON 3033/Evas Cosder a smple bvarae me-seres model of he form: y 0 x The four key assumpos abou ε hs model are ) E(ε ) = E[ε x ]=0 ) Var(ε ) =Var(ε x ) = ) Cov(ε, ε )
More informationFundamentals of Regression Analysis
Fdametals of Regresso Aalyss Regresso aalyss s cocered wth the stdy of the depedece of oe varable, the depedet varable, o oe or more other varables, the explaatory varables, wth a vew of estmatg ad/or
More informationMidterm Exam. Tuesday, September hour, 15 minutes
Ecoomcs of Growh, ECON560 Sa Fracsco Sae Uvers Mchael Bar Fall 203 Mderm Exam Tuesda, Sepember 24 hour, 5 mues Name: Isrucos. Ths s closed boo, closed oes exam. 2. No calculaors of a d are allowed. 3.
More informationNUMERICAL SOLUTIONS TO ORDINARY DIFFERENTIAL EQUATIONS
NUMERICAL SOLUTIONS TO ORDINARY DIFFERENTIAL EQUATIONS If e eqao coas dervaves of a - order s sad o be a - order dffereal eqao. For eample a secod-order eqao descrbg e oscllao of a weg aced po b a sprg
More informationSolution set Stat 471/Spring 06. Homework 2
oluo se a 47/prg 06 Homework a Whe he upper ragular elemes are suppressed due o smmer b Le Y Y Y Y A weep o he frs colum o oba: A ˆ b chagg he oao eg ad ec YY weep o he secod colum o oba: Aˆ YY weep o
More informationθ = θ Π Π Parametric counting process models θ θ θ Log-likelihood: Consider counting processes: Score functions:
Paramerc coug process models Cosder coug processes: N,,..., ha cou he occurreces of a eve of eres for dvduals Iesy processes: Lelhood λ ( ;,,..., N { } λ < Log-lelhood: l( log L( Score fucos: U ( l( log
More informationLeast Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters
Leas Squares Fg LSQF wh a complcaed fuco Theeampleswehavelookedasofarhavebeelearheparameers ha we have bee rg o deerme e.g. slope, ercep. For he case where he fuco s lear he parameers we ca fd a aalc soluo
More informationFundamentals of Speech Recognition Suggested Project The Hidden Markov Model
. Projec Iroduco Fudameals of Speech Recogo Suggesed Projec The Hdde Markov Model For hs projec, s proposed ha you desg ad mpleme a hdde Markov model (HMM) ha opmally maches he behavor of a se of rag sequeces
More informationLecture 2: The Simple Regression Model
Lectre Notes o Advaced coometrcs Lectre : The Smple Regresso Model Takash Yamao Fall Semester 5 I ths lectre we revew the smple bvarate lear regresso model. We focs o statstcal assmptos to obta based estmators.
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 informationTo Estimate or to Predict
Raer Schwabe o Esmae or o Predc Implcaos o he esg or Lear Mxed Models o Esmae or o Predc - Implcaos o he esg or Lear Mxed Models Raer Schwabe, Marya Prus raer.schwabe@ovgu.de suppored by SKAVOE Germa ederal
More informationInternational Journal Of Engineering And Computer Science ISSN: Volume 5 Issue 12 Dec. 2016, Page No.
www.jecs. Ieraoal Joural Of Egeerg Ad Compuer Scece ISSN: 19-74 Volume 5 Issue 1 Dec. 16, Page No. 196-1974 Sofware Relably Model whe mulple errors occur a a me cludg a faul correco process K. Harshchadra
More informationFALL HOMEWORK NO. 6 - SOLUTION Problem 1.: Use the Storage-Indication Method to route the Input hydrograph tabulated below.
Jorge A. Ramírez HOMEWORK NO. 6 - SOLUTION Problem 1.: Use he Sorage-Idcao Mehod o roue he Ipu hydrograph abulaed below. Tme (h) Ipu Hydrograph (m 3 /s) Tme (h) Ipu Hydrograph (m 3 /s) 0 0 90 450 6 50
More informationStationarity and Unit Root tests
Saioari ad Ui Roo ess Saioari ad Ui Roo ess. Saioar ad Nosaioar Series. Sprios Regressio 3. Ui Roo ad Nosaioari 4. Ui Roo ess Dicke-Fller es Agmeed Dicke-Fller es KPSS es Phillips-Perro Tes 5. Resolvig
More information( ) ( ) Weibull Distribution: k ti. u u. Suppose t 1, t 2, t n are times to failure of a group of n mechanisms. The likelihood function is
Webll Dsbo: Des Bce Dep of Mechacal & Idsal Egeeg The Uvesy of Iowa pdf: f () exp Sppose, 2, ae mes o fale of a gop of mechasms. The lelhood fco s L ( ;, ) exp exp MLE: Webll 3//2002 page MLE: Webll 3//2002
More informationLecture 3 Topic 2: Distributions, hypothesis testing, and sample size determination
Lecure 3 Topc : Drbuo, hypohe eg, ad ample ze deermao The Sude - drbuo Coder a repeaed drawg of ample of ze from a ormal drbuo of mea. For each ample, compue,,, ad aoher ac,, where: The ac he devao of
More informationInterval Estimation. Consider a random variable X with a mean of X. Let X be distributed as X X
ECON 37: Ecoomercs Hypohess Tesg Iervl Esmo Wh we hve doe so fr s o udersd how we c ob esmors of ecoomcs reloshp we wsh o sudy. The queso s how comforble re we wh our esmors? We frs exme how o produce
More informationThe Linear Regression Of Weighted Segments
The Lear Regresso Of Weghed Segmes George Dael Maeescu Absrac. We proposed a regresso model where he depede varable s made o up of pos bu segmes. Ths suao correspods o he markes hroughou he da are observed
More informationSuppose we have observed values t 1, t 2, t n of a random variable T.
Sppose we have obseved vales, 2, of a adom vaable T. The dsbo of T s ow o belog o a cea ype (e.g., expoeal, omal, ec.) b he veco θ ( θ, θ2, θp ) of ow paamees assocaed wh s ow (whee p s he mbe of ow paamees).
More informationHYPOTHESIS TESTING. four steps
Irodcio o Saisics i Psychology PSY 20 Professor Greg Fracis Lecre 24 Correlaios ad proporios Ca yo read my mid? Par II HYPOTHESIS TESTING for seps. Sae he hypohesis. 2. Se he crierio for rejecig H 0. 3.
More informationRATIO ESTIMATORS USING CHARACTERISTICS OF POISSON DISTRIBUTION WITH APPLICATION TO EARTHQUAKE DATA
The 7 h Ieraoal as of Sascs ad Ecoomcs Prague Sepember 9-0 Absrac RATIO ESTIMATORS USING HARATERISTIS OF POISSON ISTRIBUTION WITH APPLIATION TO EARTHQUAKE ATA Gamze Özel Naural pulaos bolog geecs educao
More informationLinear Regression Linear Regression with Shrinkage
Lear Regresso Lear Regresso h Shrkage Iroduco Regresso meas predcg a couous (usuall scalar oupu from a vecor of couous pus (feaures x. Example: Predcg vehcle fuel effcec (mpg from 8 arbues: Lear Regresso
More informationThe Poisson Process Properties of the Poisson Process
Posso Processes Summary The Posso Process Properes of he Posso Process Ierarrval mes Memoryless propery ad he resdual lfeme paradox Superposo of Posso processes Radom seleco of Posso Pos Bulk Arrvals ad
More informationLeast squares and motion. Nuno Vasconcelos ECE Department, UCSD
Leas squares ad moo uo Vascocelos ECE Deparme UCSD Pla for oda oda we wll dscuss moo esmao hs s eresg wo was moo s ver useful as a cue for recogo segmeao compresso ec. s a grea eample of leas squares problem
More informationAs evident from the full-sample-model, we continue to assume that individual errors are identically and
Maxmum Lkelhood smao Greee Ch.4; App. R scrp modsa, modsb If we feel safe makg assumpos o he sascal dsrbuo of he error erm, Maxmum Lkelhood smao (ML) s a aracve alerave o Leas Squares for lear regresso
More informationAn Efficient Dual to Ratio and Product Estimator of Population Variance in Sample Surveys
"cece as True Here" Joural of Mahemacs ad ascal cece, Volume 06, 78-88 cece gpos Publshg A Effce Dual o Rao ad Produc Esmaor of Populao Varace ample urves ubhash Kumar Yadav Deparme of Mahemacs ad ascs
More informationDISTURBANCE TERMS. is a scalar and x i
DISTURBANCE TERMS I a feld of research desg, we ofte have the qesto abot whether there s a relatoshp betwee a observed varable (sa, ) ad the other observed varables (sa, x ). To aswer the qesto, we ma
More informationi 2 σ ) i = 1,2,...,n , and = 3.01 = 4.01
ECO 745, Homework 6 Le Cabrera. Assume that the followg data come from the lear model: ε ε ~ N, σ,,..., -6. -.5 7. 6.9 -. -. -.9. -..6.4.. -.6 -.7.7 Fd the mamum lkelhood estmates of,, ad σ ε s.6. 4. ε
More informationColumbia University. Columbia University Biostatistics Technical Report Series. A Note on the Censoring Problem in Empirical Case-Outcome Studies
olmba versy olmba versy Bosascs echcal epor eres Year 2006 aper A Noe o he esorg roblem Emprcal ase-ocome des Mchael O. kelse Brce Lev a W. McKeage We-Ya sa olmba versy, Brce.Lev@olmba.ed hs workg paper
More informationComparison of the Bayesian and Maximum Likelihood Estimation for Weibull Distribution
Joural of Mahemacs ad Sascs 6 (2): 1-14, 21 ISSN 1549-3644 21 Scece Publcaos Comarso of he Bayesa ad Maxmum Lkelhood Esmao for Webull Dsrbuo Al Omar Mohammed Ahmed, Hadeel Salm Al-Kuub ad Noor Akma Ibrahm
More informationDetermination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction
refeed Soluos for R&D o Desg Deermao of oe Equao arameers Soluos for R&D o Desg December 4, 0 refeed orporao Yosho Kumagae refeed Iroduco hyscal propery daa s exremely mpora for performg process desg ad
More informationQR factorization. Let P 1, P 2, P n-1, be matrices such that Pn 1Pn 2... PPA
QR facorzao Ay x real marx ca be wre as AQR, where Q s orhogoal ad R s upper ragular. To oba Q ad R, we use he Householder rasformao as follows: Le P, P, P -, be marces such ha P P... PPA ( R s upper ragular.
More informationUse of Non-Conventional Measures of Dispersion for Improved Estimation of Population Mean
Amerca Joural of Operaoal esearch 06 6(: 69-75 DOI: 0.59/.aor.06060.0 Use of o-coveoal Measures of Dsperso for Improve Esmao of Populao Mea ubhash Kumar aav.. Mshra * Alok Kumar hukla hak Kumar am agar
More informationCOMPARISON OF ESTIMATORS OF PARAMETERS FOR THE RAYLEIGH DISTRIBUTION
COMPARISON OF ESTIMATORS OF PARAMETERS FOR THE RAYLEIGH DISTRIBUTION Eldesoky E. Affy. Faculy of Eg. Shbee El kom Meoufa Uv. Key word : Raylegh dsrbuo, leas squares mehod, relave leas squares, leas absolue
More informationAxiomatic Definition of Probability. Problems: Relative Frequency. Event. Sample Space Examples
Rado Sgals robabl & Rado Varables: Revew M. Sa Fadal roessor o lecrcal geerg Uvers o evada Reo Soe phscal sgals ose cao be epressed as a eplc aheacal orla. These sgals s be descrbed probablsc ers. ose
More informationMoments of Order Statistics from Nonidentically Distributed Three Parameters Beta typei and Erlang Truncated Exponential Variables
Joural of Mahemacs ad Sascs 6 (4): 442-448, 200 SSN 549-3644 200 Scece Publcaos Momes of Order Sascs from Nodecally Dsrbued Three Parameers Bea ype ad Erlag Trucaed Expoeal Varables A.A. Jamoom ad Z.A.
More informationRegression Approach to Parameter Estimation of an Exponential Software Reliability Model
Amerca Joural of Theorecal ad Appled Sascs 06; 5(3): 80-86 hp://www.scecepublshggroup.com/j/ajas do: 0.648/j.ajas.060503. ISSN: 36-8999 (Pr); ISSN: 36-9006 (Ole) Regresso Approach o Parameer Esmao of a
More informationSurvival Prediction Based on Compound Covariate under Cox Proportional Hazard Models
Ieraoal Bomerc Coferece 22/8/3, Kobe JAPAN Survval Predco Based o Compoud Covarae uder Co Proporoal Hazard Models PLoS ONE 7. do:.37/oural.poe.47627. hp://d.plos.org/.37/oural.poe.47627 Takesh Emura Graduae
More informationThe ray paths and travel times for multiple layers can be computed using ray-tracing, as demonstrated in Lab 3.
C. Trael me cures for mulple reflecors The ray pahs ad rael mes for mulple layers ca be compued usg ray-racg, as demosraed Lab. MATLAB scrp reflec_layers_.m performs smple ray racg. (m) ref(ms) ref(ms)
More informationIMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS
Vol.7 No.4 (200) p73-78 Joural of Maageme Scece & Sascal Decso IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS TIANXIANG YAO AND ZAIWU GONG College of Ecoomcs &
More informationFall 2009 Social Sciences 7418 University of Wisconsin-Madison. Problem Set 2 Answers (4) (6) di = D (10)
Publc Affars 974 Menze D. Chnn Fall 2009 Socal Scences 7418 Unversy of Wsconsn-Madson Problem Se 2 Answers Due n lecure on Thursday, November 12. " Box n" your answers o he algebrac quesons. 1. Consder
More informationPartial Molar Properties of solutions
Paral Molar Properes of soluos A soluo s a homogeeous mxure; ha s, a soluo s a oephase sysem wh more ha oe compoe. A homogeeous mxures of wo or more compoes he gas, lqud or sold phase The properes of a
More informationMachine Learning. Introduction to Regression. Lecture 3, September 19, Reading: Chap. 3, CB
ache Learg 0-70/5 70/5-78 78 all 006 Iroduco o Regresso Erc g Lecure 3 Sepember 9 006 Readg: Chap. 3 C Iferece wh he Jo Compue Codoals 0.4 0. P lu eadhead P lu eadhead P eadhead 0.7 0. 0.05 0.05 0.05 0.05
More informationMiscellanea Miscellanea
Miscellanea Miscellanea Miscellanea Miscellanea Miscellanea CENRAL EUROPEAN REVIEW OF ECONOMICS & FINANCE Vol., No. (4) pp. -6 bigniew Śleszński USING BORDERED MARICES FOR DURBIN WASON D SAISIC EVALUAION
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 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 informationNUMERICAL EVALUATION of DYNAMIC RESPONSE
NUMERICAL EVALUATION of YNAMIC RESPONSE Aalycal solo of he eqao of oo for a sgle degree of freedo syse s sally o ossble f he excao aled force or grod accelerao ü g -vares arbrarly h e or f he syse s olear.
More information8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall
8. Queueg sysems lec8. S-38.45 - Iroduco o Teleraffc Theory - Fall 8. Queueg sysems Coes Refresher: Smle eleraffc model M/M/ server wag laces M/M/ servers wag laces 8. Queueg sysems Smle eleraffc model
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 informationMidterm Exam 1, section 1 (Solution) Thursday, February hour, 15 minutes
coometrcs, CON Sa Fracsco State Uversty Mchael Bar Sprg 5 Mdterm am, secto Soluto Thursday, February 6 hour, 5 mutes Name: Istructos. Ths s closed book, closed otes eam.. No calculators of ay kd are allowed..
More informationDensity estimation III. Linear regression.
Lecure 6 Mlos Hauskrec mlos@cs.p.eu 539 Seo Square Des esmao III. Lear regresso. Daa: Des esmao D { D D.. D} D a vecor of arbue values Obecve: r o esmae e uerlg rue probabl srbuo over varables X px usg
More informationCyclone. Anti-cyclone
Adveco Cycloe A-cycloe Lorez (963) Low dmesoal aracors. Uclear f hey are a good aalogy o he rue clmae sysem, bu hey have some appealg characerscs. Dscusso Is he al codo balaced? Is here a al adjusme
More informationPricing of CDO s Based on the Multivariate Wang Transform*
Prcg of DO s Based o he Mulvarae Wag Trasform* ASTIN 2009 olloquum @ Helsk 02 Jue 2009 Masaak Kma Tokyo Meropola versy/ Kyoo versy Emal: kma@mu.ac.p hp://www.comp.mu.ac.p/kmam * Jo Work wh Sh-ch Moomya
More informationImputation Based on Local Linear Regression for Nonmonotone Nonrespondents in Longitudinal Surveys
Ope Joural of Sascs, 6, 6, 38-54 p://www.scrp.org/joural/ojs SSN Ole: 6-798 SSN Pr: 6-78X mpuao Based o Local Lear Regresso for Nomoooe Norespodes Logudal Surves Sara Pee, Carles K. Sego, Leo Odogo, George
More informationQuantum Mechanics II Lecture 11 Time-dependent perturbation theory. Time-dependent perturbation theory (degenerate or non-degenerate starting state)
Pro. O. B. Wrgh, Auum Quaum Mechacs II Lecure Tme-depede perurbao heory Tme-depede perurbao heory (degeerae or o-degeerae sarg sae) Cosder a sgle parcle whch, s uperurbed codo wh Hamloa H, ca exs a superposo
More informationSolution. The straightforward approach is surprisingly difficult because one has to be careful about the limits.
ose ad Varably Homewor # (8), aswers Q: Power spera of some smple oses A Posso ose A Posso ose () s a sequee of dela-fuo pulses, eah ourrg depedely, a some rae r (More formally, s a sum of pulses of wdh
More informationChapter 11 Autocorrelation
Chaper Aocorrelaio Oe of he basic assmpio i liear regressio model is ha he radom error compoes or disrbaces are ideically ad idepedely disribed So i he model y = Xβ +, i is assmed ha σ if s = E (, s) =
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 informationQuantitative Portfolio Theory & Performance Analysis
550.447 Quaave Porfolo heory & Performace Aalyss Week February 4 203 Coceps. Assgme For February 4 (hs Week) ead: A&L Chaper Iroduco & Chaper (PF Maageme Evrome) Chaper 2 ( Coceps) Seco (Basc eur Calculaos)
More informationMathematical Statistics. Chapter VIII Sampling Distributions and the Central Limit Theorem
Mahmacal ascs 8 Chapr VIII amplg Dsrbos ad h Cral Lm Thorm Fcos of radom arabls ar sall of rs sascal applcao Cosdr a s of obsrabl radom arabls L For ampl sppos h arabls ar a radom sampl of s from a poplao
More informationStatistics. Correlational. Dr. Ayman Eldeib. Simple Linear Regression and Correlation. SBE 304: Linear Regression & Correlation 1/3/2018
/3/08 Sstems & Bomedcal Egeerg Departmet SBE 304: Bo-Statstcs Smple Lear Regresso ad Correlato Dr. Ama Eldeb Fall 07 Descrptve Orgasg, summarsg & descrbg data Statstcs Correlatoal Relatoshps Iferetal Geeralsg
More informationAssessing Normality. Assessing Normality. Assessing Normality. Assessing Normality. Normal Probability Plot for Normal Distribution.
Assessg Normaly No All Couous Radom Varables are Normally Dsrbued I s Impora o Evaluae how Well he Daa Se Seems o be Adequaely Approxmaed by a Normal Dsrbuo Cosruc Chars Assessg Normaly For small- or moderae-szed
More informationSome Probability Inequalities for Quadratic Forms of Negatively Dependent Subgaussian Random Variables
Joural of Sceces Islamc epublc of Ira 6(: 63-67 (005 Uvers of ehra ISSN 06-04 hp://scecesuacr Some Probabl Iequales for Quadrac Forms of Negavel Depede Subgaussa adom Varables M Am A ozorga ad H Zare 3
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted
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 informationFault Tolerant Computing. Fault Tolerant Computing CS 530 Probabilistic methods: overview
Probably 1/19/ CS 53 Probablsc mehods: overvew Yashwa K. Malaya Colorado Sae Uversy 1 Probablsc Mehods: Overvew Cocree umbers presece of uceray Probably Dsjo eves Sascal depedece Radom varables ad dsrbuos
More informationOptimal Eye Movement Strategies in Visual Search (Supplement)
Opmal Eye Moveme Sraeges Vsual Search (Suppleme) Jr Naemk ad Wlso S. Gesler Ceer for Percepual Sysems ad Deparme of Psychology, Uversy of exas a Aus, Aus X 787 Here we derve he deal searcher for he case
More informationSupplement Material for Inverse Probability Weighted Estimation of Local Average Treatment Effects: A Higher Order MSE Expansion
Suppleme Maeral for Iverse Probably Weged Esmao of Local Average Treame Effecs: A Hger Order MSE Expaso Sepe G. Doald Deparme of Ecoomcs Uversy of Texas a Aus Yu-C Hsu Isue of Ecoomcs Academa Sca Rober
More informationApplication of the stochastic self-training procedure for the modelling of extreme floods
The Exremes of he Exremes: Exraordary Floods (Proceedgs of a symposum held a Reyjav, Icelad, July 000). IAHS Publ. o. 7, 00. 37 Applcao of he sochasc self-rag procedure for he modellg of exreme floods
More informationSuggested Answers, Problem Set 4 ECON The R 2 for the unrestricted model is by definition u u u u
Da Hgerma Fall 9 Sggested Aswers, Problem Set 4 ECON 333 The F-test s defed as ( SSEr The R for the restrcted model s by defto SSE / ( k ) R ( SSE / SST ) so therefore, SSE SST ( R ) ad lkewse SSEr SST
More informationMechanical Design Technology (Free-form Surface) April 28, /12
Mechacal Desg echolog Free-form Srface Prof. amos Mrakam Assgme #: Free-form Srface Geerao Make a program ha geeraes a bcbc eer srface from 4 4 defg polgo e pos ad dsplas he srface graphcall a a ha allos
More informationMidterm Exam 1, section 2 (Solution) Thursday, February hour, 15 minutes
coometrcs, CON Sa Fracsco State Uverst Mchael Bar Sprg 5 Mdterm xam, secto Soluto Thursda, Februar 6 hour, 5 mutes Name: Istructos. Ths s closed book, closed otes exam.. No calculators of a kd are allowed..
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 informationComputer Life (CPL) ISSN: Research on IOWHA Operator Based on Vector Angle Cosine
Copuer Lfe (CPL) ISS: 1819-4818 Delverg Qualy Scece o he World Research o IOWHA Operaor Based o Vecor Agle Cose Megg Xao a, Cheg L b Shagha Uversy of Egeerg Scece, Shagha 0160, Cha a x18065415@163.co,
More informationReal-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF
EDA/DIT6 Real-Tme Sysems, Chalmers/GU, 0/0 ecure # Updaed February, 0 Real-Tme Sysems Specfcao Problem: Assume a sysem wh asks accordg o he fgure below The mg properes of he asks are gve he able Ivesgae
More informationForecasting Stock Prices Using a Hierarchical Bayesian Approach
Joural of Forecasg J. Forecas. 4, 39 59 (005) Publshed ole Wle IerScece (www.erscece.wle.com). DOI: 0.00/for.933 Forecasg Sock Prces Usg a Herarchcal Baesa Approach JUN YING, LYNN KUO * AND GIM S. SEOW
More informationMixed Integral Equation of Contact Problem in Position and Time
Ieraoal Joural of Basc & Appled Sceces IJBAS-IJENS Vol: No: 3 ed Iegral Equao of Coac Problem Poso ad me. A. Abdou S. J. oaquel Deparme of ahemacs Faculy of Educao Aleadra Uversy Egyp Deparme of ahemacs
More informationKey words: Fractional difference equation, oscillatory solutions,
OSCILLATION PROPERTIES OF SOLUTIONS OF FRACTIONAL DIFFERENCE EQUATIONS Musafa BAYRAM * ad Ayd SECER * Deparme of Compuer Egeerg, Isabul Gelsm Uversy Deparme of Mahemacal Egeerg, Yldz Techcal Uversy * Correspodg
More informationSOLUTION OF PARABOLA EQUATION BY USING REGULAR,BOUNDARY AND CORNER FUNCTIONS
SOLUTION OF PAABOLA EQUATION BY USING EGULA,BOUNDAY AND CONE FUNCTIONS Dr. Hayder Jabbar Abood, Dr. Ifchar Mdhar Talb Deparme of Mahemacs, College of Edcao, Babylo Uversy. Absrac:- we solve coverge seqece
More informationJournal of Mathematical Psychology
Joural of Mahemacal sychology 56 (22) 34 355 Coes lss avalable a ScVerse SceceDrec Joural of Mahemacal sychology oural homepage: www.elsever.com/locae/mp Sascal measures for workload capacy aalyss Joseph
More informationEDUCATION COMMITTEE OF THE SOCIETY OF ACTUARIES ADVANCED TOPICS IN GENERAL INSURANCE STUDY NOTE CREDIBILITY WITH SHIFTING RISK PARAMETERS
EDUCATION COMMITTEE OF THE SOCIETY OF ACTUARIES ADVANCED TOPICS IN GENERAL INSURANCE STUDY NOTE CREDIBILITY WITH SHIFTING RISK PARAMETERS Suar Klugma, FSA, CERA, PhD Copyrgh 04 Socey of Acuares The Educao
More informationEE 6885 Statistical Pattern Recognition
EE 6885 Sascal Paer Recogo Fall 005 Prof. Shh-Fu Chag hp://.ee.columba.edu/~sfchag Lecure 8 (/8/05 8- Readg Feaure Dmeso Reduco PCA, ICA, LDA, Chaper 3.8, 0.3 ICA Tuoral: Fal Exam Aapo Hyväre ad Erkk Oja,
More informationChapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1)
Aoucemes Reags o E-reserves Proec roosal ue oay Parameer Esmao Bomercs CSE 9-a Lecure 6 CSE9a Fall 6 CSE9a Fall 6 Paer Classfcao Chaer 3: Mamum-Lelhoo & Bayesa Parameer Esmao ar All maerals hese sles were
More informationThe Mean Residual Lifetime of (n k + 1)-out-of-n Systems in Discrete Setting
Appled Mahemacs 4 5 466-477 Publshed Ole February 4 (hp//wwwscrporg/oural/am hp//dxdoorg/436/am45346 The Mea Resdual Lfeme of ( + -ou-of- Sysems Dscree Seg Maryam Torab Sahboom Deparme of Sascs Scece ad
More information14. Poisson Processes
4. Posso Processes I Lecure 4 we roduced Posso arrvals as he lmg behavor of Bomal radom varables. Refer o Posso approxmao of Bomal radom varables. From he dscusso here see 4-6-4-8 Lecure 4 " arrvals occur
More informationStatistics: Part 1 Parameter Estimation
Hery Sar ad Joh W. Woods, robably, Sascs, ad Radom ables for geers, h ed., earso ducao Ic., 0. ISBN: 978-0-3-33-6 Chaer 6 Sascs: ar arameer smao Secos 6. Iroduco 30 Ideede, Idecally Dsrbued (..d.) Observaos
More informationOther Topics in Kernel Method Statistical Inference with Reproducing Kernel Hilbert Space
Oher Topcs Kerel Mehod Sascal Iferece wh Reproducg Kerel Hlber Space Kej Fukumzu Isue of Sascal Mahemacs, ROIS Deparme of Sascal Scece, Graduae Uversy for Advaced Sudes Sepember 6, 008 / Sascal Learg Theory
More informationDecompression diagram sampler_src (source files and makefiles) bin (binary files) --- sh (sample shells) --- input (sample input files)
. Iroduco Probblsc oe-moh forecs gudce s mde b 50 esemble members mproved b Model Oupu scs (MO). scl equo s mde b usg hdcs d d observo d. We selec some prmeers for modfg forecs o use mulple regresso formul.
More informationContinuous Time Markov Chains
Couous me Markov chas have seay sae probably soluos f a oly f hey are ergoc, us lke scree me Markov chas. Fg he seay sae probably vecor for a couous me Markov cha s o more ffcul ha s he scree me case,
More informationDESIGN OF TENSION MEMBERS
CHAPTER Srcral Seel Design LRFD Mehod DESIGN OF TENSION MEMBERS Third Ediion A. J. Clark School of Engineering Deparmen of Civil and Environmenal Engineering Par II Srcral Seel Design and Analysis 4 FALL
More informationHYPOTHESIS TESTING. four steps. 1. State the hypothesis and the criterion. 2. Compute the test statistic. 3. Compute the p-value. 4.
Inrodcion o Saisics in Psychology PSY Professor Greg Francis Lecre 24 Hypohesis esing for correlaions Is here a correlaion beween homework and exam grades? for seps. Sae he hypohesis and he crierion 2.
More informationGeneralized Estimators Using Characteristics of Poisson distribution. Prayas Sharma, Hemant K. Verma, Nitesh K. Adichwal and *Rajesh Singh
Geeralzed Esaors Usg Characerscs of osso dsrbuo raas Shara, Hea K. Vera, Nesh K. Adchwal ad *Rajesh Sgh Depare of Sascs, Baaras Hdu Uvers Varaas(U..), Ida-5 * Corresdg auhor rsghsa@gal.co Absrac I hs arcle,
More informationThe Optimal Combination Forecasting Based on ARIMA,VAR and SSM
Advaces Compuer, Sgals ad Sysems (206) : 3-7 Clausus Scefc Press, Caada The Opmal Combao Forecasg Based o ARIMA,VAR ad SSM Bebe Che,a, Mgya Jag,b* School of Iformao Scece ad Egeerg, Shadog Uversy, Ja,
More informationChapter 9 Autocorrelation
Chaper 9 Aocorrelaio Oe of he basic assmpios i liear regressio model is ha he radom error compoes or disrbaces are ideically ad idepedely disribed So i he model y = Xβ +, i is assmed ha σ if s = E (, s)
More informationAPPLICATION OF ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM IN INTEREST RATES EFFECTS ON STOCK RETURNS
Elefheros Govas / Ida Joural of Compuer Scece ad Egeerg (IJCSE) APPLICATION OF ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM IN INTEREST RATES EFFECTS ON STOCK RETURNS Absrac ELEFTHERIOS GIOVANIS * Deparme of
More informationSimple Linear Regression: 1. Finding the equation of the line of best fit
Cocerao Wegh kg mle Lear Regresso:. Fdg he equao of he le of es f Ojecves: To fd he equao of he leas squares regresso le of o. Backgroud ad geeral rcle The am of regresso s o fd he lear relaosh ewee wo
More informationhp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations
HP 30S Statstcs Averages ad Stadard Devatos Average ad Stadard Devato Practce Fdg Averages ad Stadard Devatos HP 30S Statstcs Averages ad Stadard Devatos Average ad stadard devato The HP 30S provdes several
More information