Four equations describe the dynamic solution to RBC model. Consumption-leisure efficiency condition. Consumption-investment efficiency condition
|
|
- Zoe Briggs
- 5 years ago
- Views:
Transcription
1 LINEARIZING AND APPROXIMATING THE RBC MODEL SEPTEMBER 7, 200 For f( x, y, z ), mulivariable Taylor liear expasio aroud ( x, yz, ) f ( x, y, z) f( x, y, z) + f ( x, y, z)( x x) + f ( x, y, z)( y y) + f ( x, y, z)( z z) Four equaios describe he dyamic soluio o RBC model Cosumpio-leisure efficiecy codiio u( c, ) = zm ( k, ) uc( c, ) Cosumpio-ivesme efficiecy codiio Aggregae resource cosrai x y z c + k+ ( δ ) k = zm ( k, ) Law of moio for TFP z l z = ( ρ )l z + ρ l z + ε ( δ ) u ( c, ) = βe u ( c, ) + z m ( k, c c k z z + Sepember 7, The Basics
2 The Basics STEADY STATE Deermiisic seady sae he aural local poi of approximaio Shu dow all shocks ad se exogeous variables a heir meas The Idea: Le ecoomy ru for may (ifiie) periods Time eveually does maer ay more Drop all ime idices u( c, ) = zm ( k, ) u ( c, ) c uc( c, ) = βuc( c, ) mk( k, ) + δ c + δ k = zf( k, ) l z = ( ρ )l z + ρ l z z = z (a parameer of he model) z z Give fucios ad parameer values (ex ), solve for (c,, k) The seady-sae of he model Taylor expasio aroud his poi Sepember 7, LINEARIZATION ALGORITHMS Schmi-Grohe ad Uribe (2004 JEDC) A perurbaio algorihm A class of mehods used o fid a approximae soluio o a problem ha cao be solved exacly, by sarig from he exac soluio of a relaed problem. Applicable if he problem ca be formulaed by addig a small erm o he descripio of he exacly-solvable problem. Malab code available hrough Duke ecoomics web sie Uhlig (999, chaper i Compuaioal Mehods for he Sudy of Dyamic Ecoomies) Uses a geeralized eige-decomposiio Typically implemeed wih Schur decomposiio (Sims algorihm) Malab code available a hp://www2.wiwi.hu-berli.de/isiue/wpol/hml/oolki.hm The Basics Sepember 7,
3 SGU Deails Defie co-sae vecor ad sae vecor y c x k z Order model s dyamic equaios i a vecor Cosumpio-leisure efficiecy codiio Cosumpio-ivesme efficiecy codiio Aggregae resource cosrai Law of moio for TFP Sepember 7, f( y, y, x, x ) + + Need four marices of derivaives. Differeiae f ( y, y, x, x ) wih respec o (elemes of) y SGU Deails Cosumpio-leisure efficiecy codiio Cosumpio-ivesme efficiecy codiio Aggregae resource cosrai Law of moio for TFP Firs derivaives wih respec o: c + + = f y + Sepember 7,
4 SGU Deails Need four marices of derivaives 2. Differeiae f ( y, y, x, x ) wih respec o (elemes of) y + + Firs derivaives wih respec o: Cosumpio-leisure efficiecy codiio Cosumpio-ivesme efficiecy codiio Aggregae resource cosrai Law of moio for TFP c = f y Sepember 7, Need four marices of derivaives 3. Differeiae f ( y, y, x, x ) wih respec o (elemes of) x SGU Deails Cosumpio-leisure efficiecy codiio Cosumpio-ivesme efficiecy codiio Aggregae resource cosrai Law of moio for TFP Firs derivaives wih respec o: k + z + = f x + Sepember 7,
5 SGU Deails Need four marices of derivaives 4. Differeiae f ( y, y, x, x ) wih respec o (elemes of) x + + Firs derivaives wih respec o: Cosumpio-leisure efficiecy codiio Cosumpio-ivesme efficiecy codiio Aggregae resource cosrai Law of moio for TFP k z = f x Sepember 7, SGU Deails The model s dyamic expecaioal equaios f ( y, y, x, x ) + + Cosumpio-leisure efficiecy codiio 2 f ( y, y, x, x) E[ f( y, y, x, x) ] E + + Cosumpio-ivesme efficiecy codiio + + = f 3 ( y, y, x, x ) + + Aggregae resource cosrai 4 f ( y+, y, x+, x) Law of moio for TFP Cojecure equilibrium decisio rules Subsiue decisio rules io dyamic equaios y = g( x, ) x = h( x, ) + ηε + + Perurbaio parameer : govers size of shocks Marix of sadard deviaios of sae variables Sepember 7,
6 SGU Deails The model s dyamic expecaioal equaios [ ( +,, +, )] = E[ f( gx ( +, ), gx (, ), hx (, ) + ηε+, x] = E [ f( g( h( x, ), ), g( x, ), h( x, ) + ηε, x ] E f y y x x + F( x, ) F ( x, ) F ( x, ) x Sepember 7, 200 The model s dyamic expecaioal equaios E[ f( y+, y, x+, x) ] = E[ f( gx ( +, ), gx (, ), hx (, ) + ηε+, x] = E f( g( h( x, ), ), g( x, ), h( x, ) + ηε+, x F( x, ) Usig chai rule ad suppressig argumes [ ] Fx ( x, ) f f = y g x hx + y g x + fx h x + f + + x Holds, i paricular, a he deermiisic seady sae ( x,0) F ( x,0) = f g h + f g + f h + f x y+ x x y x x+ x x Seig shus dow shocks Sepember 7, SGU Deails Each erm is evaluaed a he seady sae jus as Taylor heorem requires 6
7 SGU Deails A quadraic equaio i he elemes of g x ad h x F ( x,0) = f g h + f g + f h + f x y+ x x y x x+ x x Solve umerically for he elemes of g x ad h x (use fsolve i Malab) Recall cojecured equilibrium decisio rules y = g( x, ) x = h( x, ) + ηε + + Sepember 7, SGU Deails A quadraic equaio i he elemes of g x ad h x F ( x,0) = f g h + f g + f h + f x y+ x x y x x+ x x Solve umerically for he elemes of g x ad h x (use fsolve i Malab) Recall cojecured equilibrium decisio rules y = g( x, ) x = h( x, ) + ηε + + Firs-order approximaio is y = g( x, ) g( x,0) + g ( x,0)( x x) + g ( x,0) x + x x = h( x, ) h( x,0) + h ( x,0)( x x) + h ( x,0) SGU Theorem : g ad h DONE!!! Now coduc impulse resposes, ru simulaios, abulae momes, wrie paper Sepember 7,
8 Priciples CERTAINTY EQUIVALENCE Displayed by a model if decisio rules do o deped o he sadard deviaio (i.e., he size ) of he exogeous shocks For sochasic problems wih quadraic objecive fucio ad liear cosrais, he decisio rules are ideical o hose of he osochasic problem (Ljugqvis ad Sarge (2004, p. 3)) Sepember 7, CERTAINTY EQUIVALENCE Displayed by a model if decisio rules do o deped o he sadard deviaio (i.e., he size ) of he exogeous shocks Priciples For sochasic problems wih quadraic objecive fucio ad liear cosrais, he decisio rules are ideical o hose of he osochasic problem (Ljugqvis ad Sarge (2004, p. 3)) Here, we have y = g( x, ) g( x,0) + gx( x,0)( x x) + g ( x,0) x = h( x, ) h( x,0) + h ( x,0)( x x) + h ( x,0) + x SGU Theorem : g ad h Firs-order approximaed decisio rules do o deped o he size of he shocks, which is govered by No quie he same hig as CE, bu we ll loosely refer o i as CE Sepember 7,
9 SGU Deails SECOND-ORDER APPROXIMATION Use compued g x ad h x o cosruc liear sysem of equaios eeded o solve for coefficies of secod-order approximaio (SGU p ) SGU Theorem : g x ad h x ; bu ceraiy equivalece does o hold because g 0, h 0 Sepember 7, LINEARIZING THE RBC MODEL Assume uc (, ) = lc ψ l ad mk (, ) = k cosumpio-leisure efficiecy codiio is (Parial) Example c k Le f ( y+, y, x+, x) = ( ) zk (ad recall y ) x z = ( ) zk Sepember 7,
10 (Parial) Example LINEARIZING THE RBC MODEL Assume uc (, ) = lc ψ l ad mk (, ) = k cosumpio-leisure efficiecy codiio is c k Le f ( y+, y, x+, x) = ( ) zk (ad recall y ) x z = ( ) zk Compue firs row of marix f y+ c + + Cosumpio-leisure efficiecy codiio Cosumpio-ivesme efficiecy codiio Aggregae resource cosrai Law of moio for TFP 0 0 Sepember 7, LINEARIZING THE RBC MODEL Assume uc (, ) = lc ψ l ad mk (, ) = k cosumpio-leisure efficiecy codiio is (Parial) Example c k Le f ( y+, y, x+, x) = ( ) zk (ad recall y ) x z = ( ) zk Compue firs row of marix f y Cosumpio-leisure efficiecy codiio Cosumpio-ivesme efficiecy codiio Aggregae resource cosrai Law of moio for TFP c ψ + ( ) z k 2 Sepember 7,
11 (Parial) Example LINEARIZING THE RBC MODEL Assume uc (, ) = lc ψ l ad mk (, ) = k cosumpio-leisure efficiecy codiio is c k Le f ( y+, y, x+, x) = ( ) zk (ad recall y ) x z = ( ) zk Compue firs row of marix f x+ k + z + Cosumpio-leisure efficiecy codiio Cosumpio-ivesme efficiecy codiio Aggregae resource cosrai Law of moio for TFP 0 0 Sepember 7, LINEARIZING THE RBC MODEL Assume uc (, ) = lc ψ l ad mk (, ) = k cosumpio-leisure efficiecy codiio is (Parial) Example c k Le f ( y+, y, x+, x) = ( ) zk (ad recall y ) x z = ( ) zk Compue firs row of marix f x k z Cosumpio-leisure efficiecy codiio Cosumpio-ivesme efficiecy codiio Aggregae resource cosrai Law of moio for TFP k k ( ) z ( ) + Sepember 7,
12 (Parial) Example LINEARIZING THE RBC MODEL I deermiisic seady sae, he firs rows of f y+, f y, f x+, f x are f y+ 0 0 f y f x+ ψ + ( ) zk k ( ) z ( ) k f x + How o compue derivaives? By had (feasible for small models) Schmi-Grohe ad Uribe Malab aalyical rouies Your ow Maple or Mahemaica programs Dyare package Sepember 7,
LINEAR APPROXIMATION OF THE BASELINE RBC MODEL SEPTEMBER 17, 2013
LINEAR APPROXIMATION OF THE BASELINE RBC MODEL SEPTEMBER 7, 203 Iroducio LINEARIZATION OF THE RBC MODEL For f( xyz,, ) = 0, mulivariable Taylor liear expasio aroud f( xyz,, ) f( xyz,, ) + f( xyz,, )( x
More informationLINEAR APPROXIMATION OF THE BASELINE RBC MODEL JANUARY 29, 2013
LINEAR APPROXIMATION OF THE BASELINE RBC MODEL JANUARY 29, 203 Iroducio LINEARIZATION OF THE RBC MODEL For f( x, y, z ) = 0, mulivariable Taylor liear expasio aroud f( x, y, z) f( x, y, z) + f ( x, y,
More informationFour equations describe the dynamic solution to RBC model. Consumption-leisure efficiency condition. Consumption-investment efficiency condition
LINEAR APPROXIMATION OF THE BASELINE RBC MODEL FEBRUARY, 202 Iroducio For f(, y, z ), mulivariable Taylor liear epasio aroud (, yz, ) f (, y, z) f(, y, z) + f (, y, z)( ) + f (, y, z)( y y) + f (, y, z)(
More informationThe Solution of the One Species Lotka-Volterra Equation Using Variational Iteration Method ABSTRACT INTRODUCTION
Malaysia Joural of Mahemaical Scieces 2(2): 55-6 (28) The Soluio of he Oe Species Loka-Volerra Equaio Usig Variaioal Ieraio Mehod B. Baiha, M.S.M. Noorai, I. Hashim School of Mahemaical Scieces, Uiversii
More informationApproximating Solutions for Ginzburg Landau Equation by HPM and ADM
Available a hp://pvamu.edu/aam Appl. Appl. Mah. ISSN: 193-9466 Vol. 5, No. Issue (December 1), pp. 575 584 (Previously, Vol. 5, Issue 1, pp. 167 1681) Applicaios ad Applied Mahemaics: A Ieraioal Joural
More information1. Solve by the method of undetermined coefficients and by the method of variation of parameters. (4)
7 Differeial equaios Review Solve by he mehod of udeermied coefficies ad by he mehod of variaio of parameers (4) y y = si Soluio; we firs solve he homogeeous equaio (4) y y = 4 The correspodig characerisic
More informationIdeal Amplifier/Attenuator. Memoryless. where k is some real constant. Integrator. System with memory
Liear Time-Ivaria Sysems (LTI Sysems) Oulie Basic Sysem Properies Memoryless ad sysems wih memory (saic or dyamic) Causal ad o-causal sysems (Causaliy) Liear ad o-liear sysems (Lieariy) Sable ad o-sable
More informationThe Central Limit Theorem
The Ceral Limi Theorem The ceral i heorem is oe of he mos impora heorems i probabiliy heory. While here a variey of forms of he ceral i heorem, he mos geeral form saes ha give a sufficiely large umber,
More informationPaper 3A3 The Equations of Fluid Flow and Their Numerical Solution Handout 1
Paper 3A3 The Equaios of Fluid Flow ad Their Numerical Soluio Hadou Iroducio A grea ma fluid flow problems are ow solved b use of Compuaioal Fluid Damics (CFD) packages. Oe of he major obsacles o he good
More informationA Note on Prediction with Misspecified Models
ITB J. Sci., Vol. 44 A, No. 3,, 7-9 7 A Noe o Predicio wih Misspecified Models Khresha Syuhada Saisics Research Divisio, Faculy of Mahemaics ad Naural Scieces, Isiu Tekologi Badug, Jala Gaesa Badug, Jawa
More informationAvailable online at J. Math. Comput. Sci. 4 (2014), No. 4, ISSN:
Available olie a hp://sci.org J. Mah. Compu. Sci. 4 (2014), No. 4, 716-727 ISSN: 1927-5307 ON ITERATIVE TECHNIQUES FOR NUMERICAL SOLUTIONS OF LINEAR AND NONLINEAR DIFFERENTIAL EQUATIONS S.O. EDEKI *, A.A.
More informationElectrical Engineering Department Network Lab.
Par:- Elecrical Egieerig Deparme Nework Lab. Deermiaio of differe parameers of -por eworks ad verificaio of heir ierrelaio ships. Objecive: - To deermie Y, ad ABD parameers of sigle ad cascaded wo Por
More informationA Novel Approach for Solving Burger s Equation
Available a hp://pvamu.edu/aam Appl. Appl. Mah. ISSN: 93-9466 Vol. 9, Issue (December 4), pp. 54-55 Applicaios ad Applied Mahemaics: A Ieraioal Joural (AAM) A Novel Approach for Solvig Burger s Equaio
More informationSupplement for SADAGRAD: Strongly Adaptive Stochastic Gradient Methods"
Suppleme for SADAGRAD: Srogly Adapive Sochasic Gradie Mehods" Zaiyi Che * 1 Yi Xu * Ehog Che 1 iabao Yag 1. Proof of Proposiio 1 Proposiio 1. Le ɛ > 0 be fixed, H 0 γi, γ g, EF (w 1 ) F (w ) ɛ 0 ad ieraio
More informationBEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS
BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS Opimal ear Forecasig Alhough we have o meioed hem explicily so far i he course, here are geeral saisical priciples for derivig he bes liear forecas, ad
More informationSection 8 Convolution and Deconvolution
APPLICATIONS IN SIGNAL PROCESSING Secio 8 Covoluio ad Decovoluio This docume illusraes several echiques for carryig ou covoluio ad decovoluio i Mahcad. There are several operaors available for hese fucios:
More informationProblems and Solutions for Section 3.2 (3.15 through 3.25)
3-7 Problems ad Soluios for Secio 3 35 hrough 35 35 Calculae he respose of a overdamped sigle-degree-of-freedom sysem o a arbirary o-periodic exciaio Soluio: From Equaio 3: x = # F! h "! d! For a overdamped
More information14.02 Principles of Macroeconomics Fall 2005
14.02 Priciples of Macroecoomics Fall 2005 Quiz 2 Tuesday, November 8, 2005 7:30 PM 9 PM Please, aswer he followig quesios. Wrie your aswers direcly o he quiz. You ca achieve a oal of 100 pois. There are
More informationF D D D D F. smoothed value of the data including Y t the most recent data.
Module 2 Forecasig 1. Wha is forecasig? Forecasig is defied as esimaig he fuure value ha a parameer will ake. Mos scieific forecasig mehods forecas he fuure value usig pas daa. I Operaios Maageme forecasig
More informationEconomics 8723 Macroeconomic Theory Problem Set 3 Sketch of Solutions Professor Sanjay Chugh Spring 2017
Deparme of Ecoomic The Ohio Sae Uiveriy Ecoomic 8723 Macroecoomic Theory Problem Se 3 Skech of Soluio Profeor Sajay Chugh Sprig 27 Taylor Saggered Nomial Price-Seig Model There are wo group of moopoliically-compeiive
More informationEconomics 8723 Macroeconomic Theory Problem Set 2 Professor Sanjay Chugh Spring 2017
Deparme of Ecoomics The Ohio Sae Uiversiy Ecoomics 8723 Macroecoomic Theory Problem Se 2 Professor Sajay Chugh Sprig 207 Labor Icome Taxes, Nash-Bargaied Wages, ad Proporioally-Bargaied Wages. I a ecoomy
More informationDavid Randall. ( )e ikx. k = u x,t. u( x,t)e ikx dx L. x L /2. Recall that the proof of (1) and (2) involves use of the orthogonality condition.
! Revised April 21, 2010 1:27 P! 1 Fourier Series David Radall Assume ha u( x,) is real ad iegrable If he domai is periodic, wih period L, we ca express u( x,) exacly by a Fourier series expasio: ( ) =
More informationSamuel Sindayigaya 1, Nyongesa L. Kennedy 2, Adu A.M. Wasike 3
Ieraioal Joural of Saisics ad Aalysis. ISSN 48-9959 Volume 6, Number (6, pp. -8 Research Idia Publicaios hp://www.ripublicaio.com The Populaio Mea ad is Variace i he Presece of Geocide for a Simple Birh-Deah-
More informationLecture 15 First Properties of the Brownian Motion
Lecure 15: Firs Properies 1 of 8 Course: Theory of Probabiliy II Term: Sprig 2015 Isrucor: Gorda Zikovic Lecure 15 Firs Properies of he Browia Moio This lecure deals wih some of he more immediae properies
More informationSOLVING OF THE FRACTIONAL NON-LINEAR AND LINEAR SCHRÖDINGER EQUATIONS BY HOMOTOPY PERTURBATION METHOD
SOLVING OF THE FRACTIONAL NON-LINEAR AND LINEAR SCHRÖDINGER EQUATIONS BY HOMOTOPY PERTURBATION METHOD DUMITRU BALEANU, ALIREZA K. GOLMANKHANEH,3, ALI K. GOLMANKHANEH 3 Deparme of Mahemaics ad Compuer Sciece,
More informationHomotopy Analysis Method for Solving Fractional Sturm-Liouville Problems
Ausralia Joural of Basic ad Applied Scieces, 4(1): 518-57, 1 ISSN 1991-8178 Homoopy Aalysis Mehod for Solvig Fracioal Surm-Liouville Problems 1 A Neamay, R Darzi, A Dabbaghia 1 Deparme of Mahemaics, Uiversiy
More informationAffine term structure models
/5/07 Affie erm srucure models A. Iro o Gaussia affie erm srucure models B. Esimaio by miimum chi square (Hamilo ad Wu) C. Esimaio by OLS (Adria, Moech, ad Crump) D. Dyamic Nelso-Siegel model (Chrisese,
More informationMath-303 Chapter 7 Linear systems of ODE November 16, Chapter 7. Systems of 1 st Order Linear Differential Equations.
Mah-33 Chaper 7 Liear sysems of ODE November 6, 7 Chaper 7 Sysems of s Order Liear Differeial Equaios saddle poi λ >, λ < Mah-33 Chaper 7 Liear sysems of ODE November 6, 7 Mah-33 Chaper 7 Liear sysems
More informationComparison between Fourier and Corrected Fourier Series Methods
Malaysia Joural of Mahemaical Scieces 7(): 73-8 (13) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES Joural homepage: hp://eispem.upm.edu.my/oural Compariso bewee Fourier ad Correced Fourier Series Mehods 1
More informationAdditional Tables of Simulation Results
Saisica Siica: Suppleme REGULARIZING LASSO: A CONSISTENT VARIABLE SELECTION METHOD Quefeg Li ad Ju Shao Uiversiy of Wiscosi, Madiso, Eas Chia Normal Uiversiy ad Uiversiy of Wiscosi, Madiso Supplemeary
More informationMean Square Convergent Finite Difference Scheme for Stochastic Parabolic PDEs
America Joural of Compuaioal Mahemaics, 04, 4, 80-88 Published Olie Sepember 04 i SciRes. hp://www.scirp.org/joural/ajcm hp://dx.doi.org/0.436/ajcm.04.4404 Mea Square Coverge Fiie Differece Scheme for
More informationxp (X = x) = P (X = 1) = θ. Hence, the method of moments estimator of θ is
Exercise 7 / page 356 Noe ha X i are ii from Beroulli(θ where 0 θ a Meho of momes: Sice here is oly oe parameer o be esimae we ee oly oe equaio where we equae he rs sample mome wih he rs populaio mome,
More informationClock Skew and Signal Representation
Clock Skew ad Sigal Represeaio Ch. 7 IBM Power 4 Chip 0/7/004 08 frequecy domai Program Iroducio ad moivaio Sequeial circuis, clock imig, Basic ools for frequecy domai aalysis Fourier series sigal represeaio
More informationLinear System Theory
Naioal Tsig Hua Uiversiy Dearme of Power Mechaical Egieerig Mid-Term Eamiaio 3 November 11.5 Hours Liear Sysem Theory (Secio B o Secio E) [11PME 51] This aer coais eigh quesios You may aswer he quesios
More informationThe Moment Approximation of the First Passage Time for the Birth Death Diffusion Process with Immigraton to a Moving Linear Barrier
America Joural of Applied Mahemaics ad Saisics, 015, Vol. 3, No. 5, 184-189 Available olie a hp://pubs.sciepub.com/ajams/3/5/ Sciece ad Educaio Publishig DOI:10.1691/ajams-3-5- The Mome Approximaio of
More informationECE-314 Fall 2012 Review Questions
ECE-34 Fall 0 Review Quesios. A liear ime-ivaria sysem has he ipu-oupu characerisics show i he firs row of he diagram below. Deermie he oupu for he ipu show o he secod row of he diagram. Jusify your aswer.
More informationThe Eigen Function of Linear Systems
1/25/211 The Eige Fucio of Liear Sysems.doc 1/7 The Eige Fucio of Liear Sysems Recall ha ha we ca express (expad) a ime-limied sigal wih a weighed summaio of basis fucios: v ( ) a ψ ( ) = where v ( ) =
More informationIf boundary values are necessary, they are called mixed initial-boundary value problems. Again, the simplest prototypes of these IV problems are:
3. Iiial value problems: umerical soluio Fiie differeces - Trucaio errors, cosisecy, sabiliy ad covergece Crieria for compuaioal sabiliy Explici ad implici ime schemes Table of ime schemes Hyperbolic ad
More informationExtremal graph theory II: K t and K t,t
Exremal graph heory II: K ad K, Lecure Graph Theory 06 EPFL Frak de Zeeuw I his lecure, we geeralize he wo mai heorems from he las lecure, from riagles K 3 o complee graphs K, ad from squares K, o complee
More informationODEs II, Supplement to Lectures 6 & 7: The Jordan Normal Form: Solving Autonomous, Homogeneous Linear Systems. April 2, 2003
ODEs II, Suppleme o Lecures 6 & 7: The Jorda Normal Form: Solvig Auoomous, Homogeeous Liear Sysems April 2, 23 I his oe, we describe he Jorda ormal form of a marix ad use i o solve a geeral homogeeous
More informationSolutions Manual 4.1. nonlinear. 4.2 The Fourier Series is: and the fundamental frequency is ω 2π
Soluios Maual. (a) (b) (c) (d) (e) (f) (g) liear oliear liear liear oliear oliear liear. The Fourier Series is: F () 5si( ) ad he fudameal frequecy is ω f ----- H z.3 Sice V rms V ad f 6Hz, he Fourier
More informationState and Parameter Estimation of The Lorenz System In Existence of Colored Noise
Sae ad Parameer Esimaio of he Lorez Sysem I Eisece of Colored Noise Mozhga Mombeii a Hamid Khaloozadeh b a Elecrical Corol ad Sysem Egieerig Researcher of Isiue for Research i Fudameal Scieces (IPM ehra
More informationL-functions and Class Numbers
L-fucios ad Class Numbers Sude Number Theory Semiar S. M.-C. 4 Sepember 05 We follow Romyar Sharifi s Noes o Iwasawa Theory, wih some help from Neukirch s Algebraic Number Theory. L-fucios of Dirichle
More informationLecture 9: Polynomial Approximations
CS 70: Complexiy Theory /6/009 Lecure 9: Polyomial Approximaios Isrucor: Dieer va Melkebeek Scribe: Phil Rydzewski & Piramaayagam Arumuga Naiar Las ime, we proved ha o cosa deph circui ca evaluae he pariy
More informationBig O Notation for Time Complexity of Algorithms
BRONX COMMUNITY COLLEGE of he Ciy Uiversiy of New York DEPARTMENT OF MATHEMATICS AND COMPUTER SCIENCE CSI 33 Secio E01 Hadou 1 Fall 2014 Sepember 3, 2014 Big O Noaio for Time Complexiy of Algorihms Time
More information1 Notes on Little s Law (l = λw)
Copyrigh c 26 by Karl Sigma Noes o Lile s Law (l λw) We cosider here a famous ad very useful law i queueig heory called Lile s Law, also kow as l λw, which assers ha he ime average umber of cusomers i
More informationTime Dependent Queuing
Time Depede Queuig Mark S. Daski Deparme of IE/MS, Norhweser Uiversiy Evaso, IL 628 Sprig, 26 Oulie Will look a M/M/s sysem Numerically iegraio of Chapma- Kolmogorov equaios Iroducio o Time Depede Queue
More informationUsing Linnik's Identity to Approximate the Prime Counting Function with the Logarithmic Integral
Usig Lii's Ideiy o Approimae he Prime Couig Fucio wih he Logarihmic Iegral Naha McKezie /26/2 aha@icecreambreafas.com Summary:This paper will show ha summig Lii's ideiy from 2 o ad arragig erms i a cerai
More informationA Two-Level Quantum Analysis of ERP Data for Mock-Interrogation Trials. Michael Schillaci Jennifer Vendemia Robert Buzan Eric Green
A Two-Level Quaum Aalysis of ERP Daa for Mock-Ierrogaio Trials Michael Schillaci Jeifer Vedemia Rober Buza Eric Gree Oulie Experimeal Paradigm 4 Low Workload; Sigle Sessio; 39 8 High Workload; Muliple
More informationLet s express the absorption of radiation by dipoles as a dipole correlation function.
MIT Deparme of Chemisry 5.74, Sprig 004: Iroducory Quaum Mechaics II Isrucor: Prof. Adrei Tokmakoff p. 81 Time-Correlaio Fucio Descripio of Absorpio Lieshape Le s express he absorpio of radiaio by dipoles
More informationMath 6710, Fall 2016 Final Exam Solutions
Mah 67, Fall 6 Fial Exam Soluios. Firs, a sude poied ou a suble hig: if P (X i p >, he X + + X (X + + X / ( evaluaes o / wih probabiliy p >. This is roublesome because a radom variable is supposed o be
More informationOnline Supplement to Reactive Tabu Search in a Team-Learning Problem
Olie Suppleme o Reacive abu Search i a eam-learig Problem Yueli She School of Ieraioal Busiess Admiisraio, Shaghai Uiversiy of Fiace ad Ecoomics, Shaghai 00433, People s Republic of Chia, she.yueli@mail.shufe.edu.c
More informationInference of the Second Order Autoregressive. Model with Unit Roots
Ieraioal Mahemaical Forum Vol. 6 0 o. 5 595-604 Iferece of he Secod Order Auoregressive Model wih Ui Roos Ahmed H. Youssef Professor of Applied Saisics ad Ecoomerics Isiue of Saisical Sudies ad Research
More informationCalculus BC 2015 Scoring Guidelines
AP Calculus BC 5 Scorig Guidelies 5 The College Board. College Board, Advaced Placeme Program, AP, AP Ceral, ad he acor logo are regisered rademarks of he College Board. AP Ceral is he official olie home
More informationA Complex Neural Network Algorithm for Computing the Largest Real Part Eigenvalue and the corresponding Eigenvector of a Real Matrix
4h Ieraioal Coferece o Sesors, Mecharoics ad Auomaio (ICSMA 06) A Complex Neural Newor Algorihm for Compuig he Larges eal Par Eigevalue ad he correspodig Eigevecor of a eal Marix HANG AN, a, XUESONG LIANG,
More information6/10/2014. Definition. Time series Data. Time series Graph. Components of time series. Time series Seasonal. Time series Trend
6//4 Defiiio Time series Daa A ime series Measures he same pheomeo a equal iervals of ime Time series Graph Compoes of ime series 5 5 5-5 7 Q 7 Q 7 Q 3 7 Q 4 8 Q 8 Q 8 Q 3 8 Q 4 9 Q 9 Q 9 Q 3 9 Q 4 Q Q
More informationCSE 241 Algorithms and Data Structures 10/14/2015. Skip Lists
CSE 41 Algorihms ad Daa Srucures 10/14/015 Skip Liss This hadou gives he skip lis mehods ha we discussed i class. A skip lis is a ordered, doublyliked lis wih some exra poiers ha allow us o jump over muliple
More informationParametric Iteration Method for Solving Linear Optimal Control Problems
Applied Mahemaics,, 3, 59-64 hp://dx.doi.org/.436/am..3955 Published Olie Sepember (hp://www.scirp.org/joural/am) Parameric Ieraio Mehod for Solvig Liear Opimal Corol Problems Abdolsaeed Alavi, Aghileh
More informationMODIFIED ADOMIAN DECOMPOSITION METHOD FOR SOLVING RICCATI DIFFERENTIAL EQUATIONS
Review of he Air Force Academy No 3 (3) 15 ODIFIED ADOIAN DECOPOSIION EHOD FOR SOLVING RICCAI DIFFERENIAL EQUAIONS 1. INRODUCION Adomia decomposiio mehod was foud by George Adomia ad has recely become
More informationExcursions of Max-Weight Dynamics
Excursios o Max-Weigh Dyamics Joh N. Tsisiklis (wih Arsala Shariassab ad Jamal Golesai, Shari U. Workshop o The Nex Wave i Neworkig Research i hoor o Jea Walrad Simos Isiue, Berkeley Sepember 27 Sepember
More informationOptimization of Rotating Machines Vibrations Limits by the Spring - Mass System Analysis
Joural of aerials Sciece ad Egieerig B 5 (7-8 (5 - doi: 765/6-6/57-8 D DAVID PUBLISHING Opimizaio of Roaig achies Vibraios Limis by he Sprig - ass Sysem Aalysis BENDJAIA Belacem sila, Algéria Absrac: The
More informationWhen both wages and prices are sticky
Whe boh ages ad rices are sicy Previously, i he basic models, oly roduc rices ere alloed o be sicy. I racice, i is ossible ha oher rices are sicy as ell. I addiio, some rices migh be more or less sicy
More informationS n. = n. Sum of first n terms of an A. P is
PROGREION I his secio we discuss hree impora series amely ) Arihmeic Progressio (A.P), ) Geomeric Progressio (G.P), ad 3) Harmoic Progressio (H.P) Which are very widely used i biological scieces ad humaiies.
More informationNot For Publication. APPENDIX B. Linearization of the Euler equation.
o For Publicaio PPEDI B Liearizaio of he Euler equaio The represeaive firm is assumed o miimize (4) subjec o he accumulaio equaio, (6), where C is defied by (5) The opimaliy codiios for his opimizaio problem
More informationBE.430 Tutorial: Linear Operator Theory and Eigenfunction Expansion
BE.43 Tuorial: Liear Operaor Theory ad Eigefucio Expasio (adaped fro Douglas Lauffeburger) 9//4 Moivaig proble I class, we ecouered parial differeial equaios describig rasie syses wih cheical diffusio.
More informationSupplementary Information for Thermal Noises in an Aqueous Quadrupole Micro- and Nano-Trap
Supplemeary Iformaio for Thermal Noises i a Aqueous Quadrupole Micro- ad Nao-Trap Jae Hyu Park ad Predrag S. Krsić * Physics Divisio, Oak Ridge Naioal Laboraory, Oak Ridge, TN 3783 E-mail: krsicp@orl.gov
More informationINVESTMENT PROJECT EFFICIENCY EVALUATION
368 Miljeko Crjac Domiika Crjac INVESTMENT PROJECT EFFICIENCY EVALUATION Miljeko Crjac Professor Faculy of Ecoomics Drsc Domiika Crjac Faculy of Elecrical Egieerig Osijek Summary Fiacial efficiecy of ivesme
More informationVARIOUS phenomena occurring in the applied sciences
roceedigs of he Ieraioal MuliCoferece of Egieers ad Compuer Scieiss 8 Vol I IMECS 8 March -6 8 Hog Kog Exac Soluios ad Numerical Compariso of Mehods for Solvig Fracioal-Order Differeial Sysems Nachapo
More information10.3 Autocorrelation Function of Ergodic RP 10.4 Power Spectral Density of Ergodic RP 10.5 Normal RP (Gaussian RP)
ENGG450 Probabiliy ad Saisics for Egieers Iroducio 3 Probabiliy 4 Probabiliy disribuios 5 Probabiliy Desiies Orgaizaio ad descripio of daa 6 Samplig disribuios 7 Ifereces cocerig a mea 8 Comparig wo reames
More informationEstimating nonlinear DSGE models with moments based methods
Dyare Workig Papers Series hp://www.dyare.org/wp/ simaig oliear DSG models wih momes based mehods Ivashcheko Sergey Workig Paper o. 32 Jauary 204 42, rue du hevalere 7503 Paris Frace hp://www.cepremap.fr
More informationMODERN CONTROL SYSTEMS
MODERN CONTROL SYSTEMS Lecure 9, Sae Space Repreeaio Emam Fahy Deparme of Elecrical ad Corol Egieerig email: emfmz@aa.edu hp://www.aa.edu/cv.php?dip_ui=346&er=6855 Trafer Fucio Limiaio TF = O/P I/P ZIC
More informationDepartment of Mathematical and Statistical Sciences University of Alberta
MATH 4 (R) Wier 008 Iermediae Calculus I Soluios o Problem Se # Due: Friday Jauary 8, 008 Deparme of Mahemaical ad Saisical Scieces Uiversiy of Albera Quesio. [Sec.., #] Fid a formula for he geeral erm
More informationEE363 homework 1 solutions
EE363 Prof. S. Boyd EE363 homework 1 soluions 1. LQR for a riple accumulaor. We consider he sysem x +1 = Ax + Bu, y = Cx, wih 1 1 A = 1 1, B =, C = [ 1 ]. 1 1 This sysem has ransfer funcion H(z) = (z 1)
More informationNumerical Solution of Parabolic Volterra Integro-Differential Equations via Backward-Euler Scheme
America Joural of Compuaioal ad Applied Maemaics, (6): 77-8 DOI:.59/.acam.6. Numerical Soluio of Parabolic Volerra Iegro-Differeial Equaios via Bacward-Euler Sceme Ali Filiz Deparme of Maemaics, Ada Mederes
More informationOnline Appendix to Solution Methods for Models with Rare Disasters
Online Appendix o Soluion Mehods for Models wih Rare Disasers Jesús Fernández-Villaverde and Oren Levinal In his Online Appendix, we presen he Euler condiions of he model, we develop he pricing Calvo block,
More informationFRACTIONAL VARIATIONAL ITERATION METHOD FOR TIME-FRACTIONAL NON-LINEAR FUNCTIONAL PARTIAL DIFFERENTIAL EQUATION HAVING PROPORTIONAL DELAYS
S33 FRACTIONAL VARIATIONAL ITERATION METHOD FOR TIME-FRACTIONAL NON-LINEAR FUNCTIONAL PARTIAL DIFFERENTIAL EQUATION HAVING PROPORTIONAL DELAYS by Derya DOGAN DURGUN ad Ali KONURALP * Deparme of Mahemaics
More informationDevelopment of Kalman Filter and Analogs Schemes to Improve Numerical Weather Predictions
Developme of Kalma Filer ad Aalogs Schemes o Improve Numerical Weaher Predicios Luca Delle Moache *, Aimé Fourier, Yubao Liu, Gregory Roux, ad Thomas Warer (NCAR) Thomas Nipe, ad Rolad Sull (UBC) Wid Eergy
More informationN! AND THE GAMMA FUNCTION
N! AND THE GAMMA FUNCTION Cosider he produc of he firs posiive iegers- 3 4 5 6 (-) =! Oe calls his produc he facorial ad has ha produc of he firs five iegers equals 5!=0. Direcly relaed o he discree! fucio
More informationEnergy Density / Energy Flux / Total Energy in 1D. Key Mathematics: density, flux, and the continuity equation.
ecure Phys 375 Eergy Desiy / Eergy Flu / oal Eergy i D Overview ad Moivaio: Fro your sudy of waves i iroducory physics you should be aware ha waves ca raspor eergy fro oe place o aoher cosider he geeraio
More informationMath Week 14 April 16-20: sections first order systems of linear differential equations; 7.4 mass-spring systems.
Mah 2250-004 Week 4 April 6-20 secions 7.-7.3 firs order sysems of linear differenial equaions; 7.4 mass-spring sysems. Mon Apr 6 7.-7.2 Sysems of differenial equaions (7.), and he vecor Calculus we need
More information5.74 Introductory Quantum Mechanics II
MIT OpeCourseWare hp://ocw.mi.edu 5.74 Iroducory Quaum Mechaics II Sprig 009 For iformaio aou ciig hese maerials or our Terms of Use, visi: hp://ocw.mi.edu/erms. drei Tokmakoff, MIT Deparme of Chemisry,
More informationCLOSED FORM EVALUATION OF RESTRICTED SUMS CONTAINING SQUARES OF FIBONOMIAL COEFFICIENTS
PB Sci Bull, Series A, Vol 78, Iss 4, 2016 ISSN 1223-7027 CLOSED FORM EVALATION OF RESTRICTED SMS CONTAINING SQARES OF FIBONOMIAL COEFFICIENTS Emrah Kılıc 1, Helmu Prodiger 2 We give a sysemaic approach
More informationTAKA KUSANO. laculty of Science Hrosh tlnlersty 1982) (n-l) + + Pn(t)x 0, (n-l) + + Pn(t)Y f(t,y), XR R are continuous functions.
Iera. J. Mah. & Mah. Si. Vol. 6 No. 3 (1983) 559-566 559 ASYMPTOTIC RELATIOHIPS BETWEEN TWO HIGHER ORDER ORDINARY DIFFERENTIAL EQUATIONS TAKA KUSANO laculy of Sciece Hrosh llersy 1982) ABSTRACT. Some asympoic
More informationLecture 15: Three-tank Mixing and Lead Poisoning
Lecure 15: Three-ak Miig ad Lead Poisoig Eigevalues ad eigevecors will be used o fid he soluio of a sysem for ukow fucios ha saisfy differeial equaios The ukow fucios will be wrie as a 1 colum vecor [
More informationφ ( t ) = φ ( t ). The notation denotes a norm that is usually
7h Europea Sigal Processig Coferece (EUSIPCO 9) Glasgo, Scolad, Augus -8, 9 DESIG OF DIGITAL IIR ITEGRATOR USIG RADIAL BASIS FUCTIO ITERPOLATIO METOD Chie-Cheg Tseg ad Su-Lig Lee Depar of Compuer ad Commuicaio
More informationA note on deviation inequalities on {0, 1} n. by Julio Bernués*
A oe o deviaio iequaliies o {0, 1}. by Julio Berués* Deparameo de Maemáicas. Faculad de Ciecias Uiversidad de Zaragoza 50009-Zaragoza (Spai) I. Iroducio. Le f: (Ω, Σ, ) IR be a radom variable. Roughly
More informationSTK4080/9080 Survival and event history analysis
STK48/98 Survival ad eve hisory aalysis Marigales i discree ime Cosider a sochasic process The process M is a marigale if Lecure 3: Marigales ad oher sochasic processes i discree ime (recap) where (formally
More informationA Study On (H, 1)(E, q) Product Summability Of Fourier Series And Its Conjugate Series
Mahemaical Theory ad Modelig ISSN 4-584 (Paper) ISSN 5-5 (Olie) Vol.7, No.5, 7 A Sudy O (H, )(E, q) Produc Summabiliy Of Fourier Series Ad Is Cojugae Series Sheela Verma, Kalpaa Saxea * Research Scholar
More informationFIXED FUZZY POINT THEOREMS IN FUZZY METRIC SPACE
Mohia & Samaa, Vol. 1, No. II, December, 016, pp 34-49. ORIGINAL RESEARCH ARTICLE OPEN ACCESS FIED FUZZY POINT THEOREMS IN FUZZY METRIC SPACE 1 Mohia S. *, Samaa T. K. 1 Deparme of Mahemaics, Sudhir Memorial
More informationMath 2414 Homework Set 7 Solutions 10 Points
Mah Homework Se 7 Soluios 0 Pois #. ( ps) Firs verify ha we ca use he iegral es. The erms are clearly posiive (he epoeial is always posiive ad + is posiive if >, which i is i his case). For decreasig we
More informationMANY-SERVER QUEUES WITH CUSTOMER ABANDONMENT: A SURVEY OF DIFFUSION AND FLUID APPROXIMATIONS
J Sys Sci Sys Eg (Mar 212) 21(1): 1-36 DOI: 1.17/s11518-12-5189-y ISSN: 14-3756 (Paper) 1861-9576 (Olie) CN11-2983/N MANY-SERVER QUEUES WITH CUSTOMER ABANDONMENT: A SURVEY OF DIFFUSION AND FLUID APPROXIMATIONS
More informationCompact Finite Difference Schemes for Solving a Class of Weakly- Singular Partial Integro-differential Equations
Ma. Sci. Le. Vol. No. 53-0 (0 Maemaical Scieces Leers A Ieraioal Joural @ 0 NSP Naural Scieces Publisig Cor. Compac Fiie Differece Scemes for Solvig a Class of Weakly- Sigular Parial Iegro-differeial Equaios
More informationdt = C exp (3 ln t 4 ). t 4 W = C exp ( ln(4 t) 3) = C(4 t) 3.
Mah Rahman Exam Review Soluions () Consider he IVP: ( 4)y 3y + 4y = ; y(3) = 0, y (3) =. (a) Please deermine he longes inerval for which he IVP is guaraneed o have a unique soluion. Soluion: The disconinuiies
More informationCredit portfolio optimization with replacement in defaultable asset
SPECIAL SECION: MAHEMAICAL FINANCE Credi porfolio opimizaio wih replaceme i defaulable asse K. Suresh Kumar* ad Chada Pal Deparme of Mahemaics, Idia Isiue of echology Bombay, Mumbai 4 76, Idia I his aricle,
More informationNEWTON METHOD FOR DETERMINING THE OPTIMAL REPLENISHMENT POLICY FOR EPQ MODEL WITH PRESENT VALUE
Yugoslav Joural of Operaios Research 8 (2008, Number, 53-6 DOI: 02298/YUJOR080053W NEWTON METHOD FOR DETERMINING THE OPTIMAL REPLENISHMENT POLICY FOR EPQ MODEL WITH PRESENT VALUE Jeff Kuo-Jug WU, Hsui-Li
More informationVibration 2-1 MENG331
Vibraio MENG33 Roos of Char. Eq. of DOF m,c,k sysem for λ o he splae λ, ζ ± ζ FIG..5 Dampig raios of commo maerials 3 4 T d T d / si cos B B e d d ζ ˆ ˆ d T N e B e B ζ ζ d T T w w e e e B e B ˆ ˆ ζ ζ
More informationECE 570 Session 7 IC 752-E Computer Aided Engineering for Integrated Circuits. Transient analysis. Discuss time marching methods used in SPICE
ECE 570 Sessio 7 IC 75-E Compuer Aided Egieerig for Iegraed Circuis Trasie aalysis Discuss ime marcig meods used i SPICE. Time marcig meods. Explici ad implici iegraio meods 3. Implici meods used i circui
More informationBAYESIAN ESTIMATION METHOD FOR PARAMETER OF EPIDEMIC SIR REED-FROST MODEL. Puji Kurniawan M
BAYESAN ESTMATON METHOD FOR PARAMETER OF EPDEMC SR REED-FROST MODEL Puji Kuriawa M447 ABSTRACT. fecious diseases is a impora healh problem i he mos of couries, belogig o doesia. Some of ifecious diseases
More informationBasic Results in Functional Analysis
Preared by: F.. ewis Udaed: Suday, Augus 7, 4 Basic Resuls i Fucioal Aalysis f ( ): X Y is coiuous o X if X, (, ) z f( z) f( ) f ( ): X Y is uiformly coiuous o X if i is coiuous ad ( ) does o deed o. f
More informationClosed-Form Solution for the Nontrivial Zeros of the Riemann Zeta Function
Closed-Form Soluio for he Norivial Zeros of he Riema Zea Fucio Frederick Ira Moxley III (Daed: April, 27) I he year 27 i was formally cojecured ha if he Beder-Brody-Müller (BBM) Hamiloia ca be show o be
More information