Y, where. 1 Estimate St.error
|
|
- Annabel Simmons
- 5 years ago
- Views:
Transcription
1 1 HG Feb 2014 ECON 5101 Exercises III - 24 Feb 2014 Exercise 1 In lecure noes 3 (LN3 page 11) we esimaed an ARMA(1,2) for daa) for he period, 1978q2-2013q2 Le Y ln BNP ln BNP (Norwegian Model: Y Y, where (1) ~ WN(0, ) Table 1 Esimaes (mle): Esimae Serror A The mle esimaes deermine a causal saionary soluion for Y Why? B The causal saionary soluion is Y (2) ,,,,, (i) Deermine formulas for 1 2 (ii) (iii) Deermine he formula for he dynamic muliplier Deermine he formula for he long run effec of a uni change in leaving he oher errors erms unchanged Esimae he long run effec from he daa Y C Make a plo of he dynamic mulipliers along he lines of Seminar I (appendix) using he esimaes in able 1 If you use Saa, use Saa 13
2 2 D (Poenial rap in Hamilon) An alernaive mehod of finding he dynamic mulipliers, is using Hamilon sec 12 (see page 13), who finds explici formulas for he dynamic mulipliers Y w In our case w, so muliplier we really wan (ie, (i) (ii) Explain why Y w and Y Y ) Y w is hardly he dynamic are no he same hing in our case Y How would you use Hamilons s soluion o derive he relevan dm s? Exercise 2 The Norwegian GDP daa, discussed in he lecures, have been pu on he course webpage in a zip-file The file conains a Saa da-file (wih sse daa) The daa are also given as an excel-file for hose ha use anoher sofware The daa are quarerly observaions of ln(gdp) in he period 1978q1 2013q2 In his exercise we wan o calculae ou-of-sample forecass for he period 2013q3 2015q4, based on he Arima(1,1,2) model esimaed in he lecure noes 3 (LN3) In addiion we wan o evaluae he uncerainy of he forecas using Saa s simulaion procedure A (i) Describe formulas for he 4 firs forecass ( Y ˆ, for 1,2,3,4 and q ) (see, eg, LN4 page 9) (ii) How would you predic he necessary ' s involved? s B Ou-of-sample forecas of ln GDP Download he GDP daa in Daa for seminar III Noe ha he daa in he Saa da-file already are declared a ime series (by he sse command) The variable ln BNP is called y in he daa se, while Y in model (1) is acually symbols ) y (sorry abou slighly clumsy use of As a check you may make plos sline y for ln BNP and sline Dy for ln BNP
3 3 Esimae he arima(1,1,2) for y by arima y,arima(1,1,2) Considering he small esimaed error sandard deviaion, i may be a good idea (wih regard o he simulaions below) o blow up he observaions, eg, by a facor 100, yy 100* y, gen yy=100*y arima yy,arima(1,1,2) Which esimaes in he oupu are affeced by he blow up and which are no? We need o define our forecas horizon (10 quarers ahead) sappend, add(10) Look a he effec in he daa base Following he descripions in he firs forecas secion in he pdf-manual (read he example of he Klein model here), we need o sore he esimaes of he arima (his will be used by he forecas commands) esimaes sore us (us is he name of he esimaes se) esimaes dir (for checking) forecas creae mod1, replace (iniiaion of he model called mod1) forecas esimaes us, names(us) (he forecass are now ready o calculaed) forecas solve, log(off) Look a he resul in he daa base Plo he ime series 100 ln BNP including he forecass from 2005q1 woway (sline f_us if >=q(2005q1)), line(2013q2) Now, ransfer hese forecass o he original series he resul from 2005q1 ln BNP (in he original scale) and plo [Hin: Noe ha if we have daa, x0, x1, x2,, x, he we ge x x x x x (called inegraion of he series) ] x
4 4 C Uncerainy by simulaion (In Saa13 read pdf-manual ime series > forecas solve -> example 2 and 3 In paricular read he inroducion before example 2 on predicion uncerainy simulaion) We will concenrae on he yy series (100 ln BNP ) Given a model here are wo componens in he predicion uncerainy, (1) uncerainy due o uncerain esimaes in he model, and (2) uncerainy due o variaions of he error erm (Noe Of course, here are oher sources of uncerainy as well, for example, model specificaion uncerainy, he risk of srucural breaks in he fuure of he series, ec These ouer sources of uncerainy are ignored in he presen calculaions ) For he following simulaions, i is a good idea o se he seed for he random number generaor before each simulaion experimen: (1) Tha makes i possible o reproduce exacly he experimen, and (2) i is always good o compare he resuls from several repeiions of he experimen for sabiliy Firs simulae he esimaion uncerainy se seed (or choose a seed (number) of your own) forecas solve, prefix(d1_) begin(q(2013q3)) simulae(beas, saisic(sddev, prefix(sd1_)) reps(100)) Noe Trying ou several seeds (remember o drop he forecas-variables in he daa base before each aemp), you will probably discover a source of un-sabiliy of he simulaions (no discussed in he Saa manual as far as I know) Someimes you will even ge quie wild (!) resuls Quesion: Can you hink of any reason (speculaion here no knowing exacly how Saa operaes is simulaions in a dynamic seing) why his phenomenon migh occur in his paricular siuaion? Afer having made up your mind of your seed, calculae confidence limis (check he daa base for he resuls) gen d1_us_up = d1_us + invnormal(0975)*sd1_us gen d1_us_dn = d1_us + invnormal(0025)*sd1_us woway (sline d1_us if >=q(2008q1)) (sline d1_us_dn if >=q(2008q1)) (sline d1_us_up if >=q(2008q1)), line(2013q2) Second Simulae he oal (esimaion plus error) predicion uncerainy se seed 567 (or a seed of your own) forecas solve, prefix(d2_) begin(q(2013q3)) simulae(beas errors, saisic(sddev,prefix(sd2_)) reps(100)) log(off)
5 5 gen d2_us_up = d2_us + invnormal(0975)*sd2_us gen d2_us_dn = d2_us + invnormal(0025)*sd2_us woway (sline d2_us if >=q(2008q1)) (sline d2_us_dn if >=q(2008q1)) (sline d2_us_up if >=q(2008q1)), line(2013q2) Exercise 3 (Some elemenary facs abou eigenvalues) I is imporan o know abou eigenvalues in ime series analysis They are also imporan in mos branches of mulivariae analysis Read firs he review of eigenvalues in LN4 page 14 (i) Explain why a k k marix A has a mos k eigenvalues [Hin Consider he meaning of he deerminan, A I ] (ii) Show ha he eigenvalues of a diagonal marix are is diagonal elemens Noe ha a diagonal marix A a11a 22 akk a a22 0 A 0 0 akk has deerminan (iii) Le he eigenvalues of he k k marix A be 1, 2,, k Show ha he deerminan is A 1 2 k (Noe (1) ha his is rue also if some of he 's are equal, which you do no have o prove here Noe (2) ha his implies ha A is nonsingular if and only if all eigenvalues are 0 Noe (3) I may also be menioned (ha you do no have o prove) ha he rank of a square marix( relevan in coinegraion analysis) is equal o he number of non-zero eigenvalues) [Hin Use propery (19) in LN4 page 15, ogeher wih he general marix propery C D C D for square marices of same dimension ]
6 6 (iv) Show ha all eigenvalues of a symmeric square marix (ie, A A ) mus be real numbers (Hence, eigenvalues of covariance marices are always real) [Hin Show firs ha he sandard represenaion of a complex number, z a ib, is unique, (where a is called he real par of z and he real number b he imaginary par of z) (Hin: Suppose z a ib aibare wo represenaions of z To show ha a aand b b, look a he absolue value (modulus) of he difference beween he wo represenaions ) Now, fill in he deails in he following skech of an argumen You will need he following general marix rule for aking he ranspose of an arbirary produc of marices, CD DC where C ~ pq and D ~ q p Proof Le b, be any eigenvecor and corresponding eigenvalue for A (ie, such ha Ab b ) Wrie b and in sandard form, b x iy and c id, where xy, are real vecors and cd, real scalars We mus prove ha d 0 : We ge Ab Ax iay b ( c id)( x iy) ec Equaing he real and imaginary pars of he resul, we mus have Ax cx dy, Ay dx cy Then muliply hese wo equaions by Explain why y and x respecively ( giving yax and x Ay ) yax and x Ay mus be equal Then subracing, 0 yax xay ec shows ha d mus be 0 (End of proof) ] A lile bi abou riangular marices ha are very common (and useful) in mulivariae analysis (v) Explain why he deerminan of an upper (or a lower) riangular marix mus be he produc of is main diagonal elemens Hin Evaluae, eg, he deerminan along he firs column ec (An upper riangular marix is A a a a k 0 a 0 0 a 22 2 k a kk (ie, all elemens below main diagonal are 0)
7 7 In a lower riangular marix all elemens are above he main diagonal are 0 (vi) Explain why he eigenvalues of an upper (or lower) riangular marix are he elemens on he main diagonal Exercize 4 (residuals) Le Y be any saionary series ha admis a linear (causal) represenaion Y where ~ WN(0, ), and where all parameers are known (as esimaes or or in oher ways) Show ha (*) ˆ Y Y 1 where Yˆ 1 is he one-sep-ahead predicor of Y based on Y 1, Y 2, Y 3, Noe Using (*) we have a naural way o predic he error erms (hus deermining residuals) in causal saionary processes (including ARMA(p,q)), based on he he one-sep-ahead forecass of he series
Y 0.4Y 0.45Y Y to a proper ARMA specification.
HG Jan 04 ECON 50 Exercises II - 0 Feb 04 (wih answers Exercise. Read secion 8 in lecure noes 3 (LN3 on he common facor problem in ARMA-processes. Consider he following process Y 0.4Y 0.45Y 0.5 ( where
More information10. State Space Methods
. Sae Space Mehods. Inroducion Sae space modelling was briefly inroduced in chaper. Here more coverage is provided of sae space mehods before some of heir uses in conrol sysem design are covered in he
More informationt is a basis for the solution space to this system, then the matrix having these solutions as columns, t x 1 t, x 2 t,... x n t x 2 t...
Mah 228- Fri Mar 24 5.6 Marix exponenials and linear sysems: The analogy beween firs order sysems of linear differenial equaions (Chaper 5) and scalar linear differenial equaions (Chaper ) is much sronger
More informationUnit Root Time Series. Univariate random walk
Uni Roo ime Series Univariae random walk Consider he regression y y where ~ iid N 0, he leas squares esimae of is: ˆ yy y y yy Now wha if = If y y hen le y 0 =0 so ha y j j If ~ iid N 0, hen y ~ N 0, he
More informationLet us start with a two dimensional case. We consider a vector ( x,
Roaion marices We consider now roaion marices in wo and hree dimensions. We sar wih wo dimensions since wo dimensions are easier han hree o undersand, and one dimension is a lile oo simple. However, our
More information23.2. Representing Periodic Functions by Fourier Series. Introduction. Prerequisites. Learning Outcomes
Represening Periodic Funcions by Fourier Series 3. Inroducion In his Secion we show how a periodic funcion can be expressed as a series of sines and cosines. We begin by obaining some sandard inegrals
More informationDEPARTMENT OF STATISTICS
A Tes for Mulivariae ARCH Effecs R. Sco Hacker and Abdulnasser Haemi-J 004: DEPARTMENT OF STATISTICS S-0 07 LUND SWEDEN A Tes for Mulivariae ARCH Effecs R. Sco Hacker Jönköping Inernaional Business School
More informationLecture 3: Exponential Smoothing
NATCOR: Forecasing & Predicive Analyics Lecure 3: Exponenial Smoohing John Boylan Lancaser Cenre for Forecasing Deparmen of Managemen Science Mehods and Models Forecasing Mehod A (numerical) procedure
More informationACE 562 Fall Lecture 8: The Simple Linear Regression Model: R 2, Reporting the Results and Prediction. by Professor Scott H.
ACE 56 Fall 5 Lecure 8: The Simple Linear Regression Model: R, Reporing he Resuls and Predicion by Professor Sco H. Irwin Required Readings: Griffihs, Hill and Judge. "Explaining Variaion in he Dependen
More information3.1 More on model selection
3. More on Model selecion 3. Comparing models AIC, BIC, Adjused R squared. 3. Over Fiing problem. 3.3 Sample spliing. 3. More on model selecion crieria Ofen afer model fiing you are lef wih a handful of
More informationSystem of Linear Differential Equations
Sysem of Linear Differenial Equaions In "Ordinary Differenial Equaions" we've learned how o solve a differenial equaion for a variable, such as: y'k5$e K2$x =0 solve DE yx = K 5 2 ek2 x C_C1 2$y''C7$y
More information23.5. Half-Range Series. Introduction. Prerequisites. Learning Outcomes
Half-Range Series 2.5 Inroducion In his Secion we address he following problem: Can we find a Fourier series expansion of a funcion defined over a finie inerval? Of course we recognise ha such a funcion
More informationTwo Coupled Oscillators / Normal Modes
Lecure 3 Phys 3750 Two Coupled Oscillaors / Normal Modes Overview and Moivaion: Today we ake a small, bu significan, sep owards wave moion. We will no ye observe waves, bu his sep is imporan in is own
More informationGMM - Generalized Method of Moments
GMM - Generalized Mehod of Momens Conens GMM esimaion, shor inroducion 2 GMM inuiion: Maching momens 2 3 General overview of GMM esimaion. 3 3. Weighing marix...........................................
More informationMATH 2050 Assignment 9 Winter Do not need to hand in. 1. Find the determinant by reducing to triangular form for the following matrices.
MATH 2050 Assignmen 9 Winer 206 Do no need o hand in Noe ha he final exam also covers maerial afer HW8, including, for insance, calculaing deerminan by row operaions, eigenvalues and eigenvecors, similariy
More informationMatlab and Python programming: how to get started
Malab and Pyhon programming: how o ge sared Equipping readers he skills o wrie programs o explore complex sysems and discover ineresing paerns from big daa is one of he main goals of his book. In his chaper,
More informationEstimation Uncertainty
Esimaion Uncerainy The sample mean is an esimae of β = E(y +h ) The esimaion error is = + = T h y T b ( ) = = + = + = = = T T h T h e T y T y T b β β β Esimaion Variance Under classical condiions, where
More informationLecture Notes 2. The Hilbert Space Approach to Time Series
Time Series Seven N. Durlauf Universiy of Wisconsin. Basic ideas Lecure Noes. The Hilber Space Approach o Time Series The Hilber space framework provides a very powerful language for discussing he relaionship
More informationDiebold, Chapter 7. Francis X. Diebold, Elements of Forecasting, 4th Edition (Mason, Ohio: Cengage Learning, 2006). Chapter 7. Characterizing Cycles
Diebold, Chaper 7 Francis X. Diebold, Elemens of Forecasing, 4h Ediion (Mason, Ohio: Cengage Learning, 006). Chaper 7. Characerizing Cycles Afer compleing his reading you should be able o: Define covariance
More informationNotes on Kalman Filtering
Noes on Kalman Filering Brian Borchers and Rick Aser November 7, Inroducion Daa Assimilaion is he problem of merging model predicions wih acual measuremens of a sysem o produce an opimal esimae of he curren
More informationSummer Term Albert-Ludwigs-Universität Freiburg Empirische Forschung und Okonometrie. Time Series Analysis
Summer Term 2009 Alber-Ludwigs-Universiä Freiburg Empirische Forschung und Okonomerie Time Series Analysis Classical Time Series Models Time Series Analysis Dr. Sevap Kesel 2 Componens Hourly earnings:
More informationCointegration and Implications for Forecasting
Coinegraion and Implicaions for Forecasing Two examples (A) Y Y 1 1 1 2 (B) Y 0.3 0.9 1 1 2 Example B: Coinegraion Y and coinegraed wih coinegraing vecor [1, 0.9] because Y 0.9 0.3 is a saionary process
More informationTime series Decomposition method
Time series Decomposiion mehod A ime series is described using a mulifacor model such as = f (rend, cyclical, seasonal, error) = f (T, C, S, e) Long- Iner-mediaed Seasonal Irregular erm erm effec, effec,
More information14 Autoregressive Moving Average Models
14 Auoregressive Moving Average Models In his chaper an imporan parameric family of saionary ime series is inroduced, he family of he auoregressive moving average, or ARMA, processes. For a large class
More informationForecasting optimally
I) ile: Forecas Evaluaion II) Conens: Evaluaing forecass, properies of opimal forecass, esing properies of opimal forecass, saisical comparison of forecas accuracy III) Documenaion: - Diebold, Francis
More informationSimulation-Solving Dynamic Models ABE 5646 Week 2, Spring 2010
Simulaion-Solving Dynamic Models ABE 5646 Week 2, Spring 2010 Week Descripion Reading Maerial 2 Compuer Simulaion of Dynamic Models Finie Difference, coninuous saes, discree ime Simple Mehods Euler Trapezoid
More informationAn recursive analytical technique to estimate time dependent physical parameters in the presence of noise processes
WHAT IS A KALMAN FILTER An recursive analyical echnique o esimae ime dependen physical parameers in he presence of noise processes Example of a ime and frequency applicaion: Offse beween wo clocks PREDICTORS,
More informationOBJECTIVES OF TIME SERIES ANALYSIS
OBJECTIVES OF TIME SERIES ANALYSIS Undersanding he dynamic or imedependen srucure of he observaions of a single series (univariae analysis) Forecasing of fuure observaions Asceraining he leading, lagging
More informationMathcad Lecture #8 In-class Worksheet Curve Fitting and Interpolation
Mahcad Lecure #8 In-class Workshee Curve Fiing and Inerpolaion A he end of his lecure, you will be able o: explain he difference beween curve fiing and inerpolaion decide wheher curve fiing or inerpolaion
More informationPhysics 235 Chapter 2. Chapter 2 Newtonian Mechanics Single Particle
Chaper 2 Newonian Mechanics Single Paricle In his Chaper we will review wha Newon s laws of mechanics ell us abou he moion of a single paricle. Newon s laws are only valid in suiable reference frames,
More informationComponents Model. Remember that we said that it was useful to think about the components representation
Componens Model Remember ha we said ha i was useful o hink abou he componens represenaion = T S C Suppose ha C is an AR(p) process Wha model does his impl for? TrendCcle Model For simplici, we sar wih
More informationVectorautoregressive Model and Cointegration Analysis. Time Series Analysis Dr. Sevtap Kestel 1
Vecorauoregressive Model and Coinegraion Analysis Par V Time Series Analysis Dr. Sevap Kesel 1 Vecorauoregression Vecor auoregression (VAR) is an economeric model used o capure he evoluion and he inerdependencies
More informationChallenge Problems. DIS 203 and 210. March 6, (e 2) k. k(k + 2). k=1. f(x) = k(k + 2) = 1 x k
Challenge Problems DIS 03 and 0 March 6, 05 Choose one of he following problems, and work on i in your group. Your goal is o convince me ha your answer is correc. Even if your answer isn compleely correc,
More informationLinear Gaussian State Space Models
Linear Gaussian Sae Space Models Srucural Time Series Models Level and Trend Models Basic Srucural Model (BSM Dynamic Linear Models Sae Space Model Represenaion Level, Trend, and Seasonal Models Time Varying
More informationFinal Spring 2007
.615 Final Spring 7 Overview The purpose of he final exam is o calculae he MHD β limi in a high-bea oroidal okamak agains he dangerous n = 1 exernal ballooning-kink mode. Effecively, his corresponds o
More informationHOMEWORK # 2: MATH 211, SPRING Note: This is the last solution set where I will describe the MATLAB I used to make my pictures.
HOMEWORK # 2: MATH 2, SPRING 25 TJ HITCHMAN Noe: This is he las soluion se where I will describe he MATLAB I used o make my picures.. Exercises from he ex.. Chaper 2.. Problem 6. We are o show ha y() =
More informationForward guidance. Fed funds target during /15/2017
Forward guidance Fed funds arge during 2004 A. A wo-dimensional characerizaion of moneary shocks (Gürkynak, Sack, and Swanson, 2005) B. Odyssean versus Delphic foreign guidance (Campbell e al., 2012) C.
More information!!"#"$%&#'()!"#&'(*%)+,&',-)./0)1-*23)
"#"$%&#'()"#&'(*%)+,&',-)./)1-*) #$%&'()*+,&',-.%,/)*+,-&1*#$)()5*6$+$%*,7&*-'-&1*(,-&*6&,7.$%$+*&%'(*8$&',-,%'-&1*(,-&*6&,79*(&,%: ;..,*&1$&$.$%&'()*1$$.,'&',-9*(&,%)?%*,('&5
More informationState-Space Models. Initialization, Estimation and Smoothing of the Kalman Filter
Sae-Space Models Iniializaion, Esimaion and Smoohing of he Kalman Filer Iniializaion of he Kalman Filer The Kalman filer shows how o updae pas predicors and he corresponding predicion error variances when
More informationAffine term structure models
Affine erm srucure models A. Inro o Gaussian affine erm srucure models B. Esimaion by minimum chi square (Hamilon and Wu) C. Esimaion by OLS (Adrian, Moench, and Crump) D. Dynamic Nelson-Siegel model (Chrisensen,
More informationModule 2 F c i k c s la l w a s o s f dif di fusi s o i n
Module Fick s laws of diffusion Fick s laws of diffusion and hin film soluion Adolf Fick (1855) proposed: d J α d d d J (mole/m s) flu (m /s) diffusion coefficien and (mole/m 3 ) concenraion of ions, aoms
More informationGDP Advance Estimate, 2016Q4
GDP Advance Esimae, 26Q4 Friday, Jan 27 Real gross domesic produc (GDP) increased a an annual rae of.9 percen in he fourh quarer of 26. The deceleraion in real GDP in he fourh quarer refleced a downurn
More informationHW6: MRI Imaging Pulse Sequences (7 Problems for 100 pts)
HW6: MRI Imaging Pulse Sequences (7 Problems for 100 ps) GOAL The overall goal of HW6 is o beer undersand pulse sequences for MRI image reconsrucion. OBJECTIVES 1) Design a spin echo pulse sequence o image
More informationExercises: Similarity Transformation
Exercises: Similariy Transformaion Problem. Diagonalize he following marix: A [ 2 4 Soluion. Marix A has wo eigenvalues λ 3 and λ 2 2. Since (i) A is a 2 2 marix and (ii) i has 2 disinc eigenvalues, we
More informationKriging Models Predicting Atrazine Concentrations in Surface Water Draining Agricultural Watersheds
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Kriging Models Predicing Arazine Concenraions in Surface Waer Draining Agriculural Waersheds Paul L. Mosquin, Jeremy Aldworh, Wenlin Chen Supplemenal Maerial Number
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 informationSOLUTIONS TO ECE 3084
SOLUTIONS TO ECE 384 PROBLEM 2.. For each sysem below, specify wheher or no i is: (i) memoryless; (ii) causal; (iii) inverible; (iv) linear; (v) ime invarian; Explain your reasoning. If he propery is no
More informationFinancial Econometrics Jeffrey R. Russell Midterm Winter 2009 SOLUTIONS
Name SOLUTIONS Financial Economerics Jeffrey R. Russell Miderm Winer 009 SOLUTIONS You have 80 minues o complee he exam. Use can use a calculaor and noes. Try o fi all your work in he space provided. If
More informationEcon107 Applied Econometrics Topic 7: Multicollinearity (Studenmund, Chapter 8)
I. Definiions and Problems A. Perfec Mulicollineariy Econ7 Applied Economerics Topic 7: Mulicollineariy (Sudenmund, Chaper 8) Definiion: Perfec mulicollineariy exiss in a following K-variable regression
More information1. Joint stationarity and long run effects in a simple ADL(1,1) Suppose Xt, Y, also is stationary?
HG Third lecure - 9. Jan. 04. Join saionariy and long run effecs in a simple ADL(,) Suppose X, Y are wo saionary ime series. Does i follow ha he sum, X Y, also is saionary? The answer is NO in general.
More informationSPH3U: Projectiles. Recorder: Manager: Speaker:
SPH3U: Projeciles Now i s ime o use our new skills o analyze he moion of a golf ball ha was ossed hrough he air. Le s find ou wha is special abou he moion of a projecile. Recorder: Manager: Speaker: 0
More informationThe expectation value of the field operator.
The expecaion value of he field operaor. Dan Solomon Universiy of Illinois Chicago, IL dsolom@uic.edu June, 04 Absrac. Much of he mahemaical developmen of quanum field heory has been in suppor of deermining
More informationQuarterly ice cream sales are high each summer, and the series tends to repeat itself each year, so that the seasonal period is 4.
Seasonal models Many business and economic ime series conain a seasonal componen ha repeas iself afer a regular period of ime. The smalles ime period for his repeiion is called he seasonal period, and
More informationACE 562 Fall Lecture 5: The Simple Linear Regression Model: Sampling Properties of the Least Squares Estimators. by Professor Scott H.
ACE 56 Fall 005 Lecure 5: he Simple Linear Regression Model: Sampling Properies of he Leas Squares Esimaors by Professor Sco H. Irwin Required Reading: Griffihs, Hill and Judge. "Inference in he Simple
More information( ) a system of differential equations with continuous parametrization ( T = R + These look like, respectively:
XIII. DIFFERENCE AND DIFFERENTIAL EQUATIONS Ofen funcions, or a sysem of funcion, are paramerized in erms of some variable, usually denoed as and inerpreed as ime. The variable is wrien as a funcion of
More informationSolutions to Odd Number Exercises in Chapter 6
1 Soluions o Odd Number Exercises in 6.1 R y eˆ 1.7151 y 6.3 From eˆ ( T K) ˆ R 1 1 SST SST SST (1 R ) 55.36(1.7911) we have, ˆ 6.414 T K ( ) 6.5 y ye ye y e 1 1 Consider he erms e and xe b b x e y e b
More informationLab 10: RC, RL, and RLC Circuits
Lab 10: RC, RL, and RLC Circuis In his experimen, we will invesigae he behavior of circuis conaining combinaions of resisors, capaciors, and inducors. We will sudy he way volages and currens change in
More information2.7. Some common engineering functions. Introduction. Prerequisites. Learning Outcomes
Some common engineering funcions 2.7 Inroducion This secion provides a caalogue of some common funcions ofen used in Science and Engineering. These include polynomials, raional funcions, he modulus funcion
More informationBiol. 356 Lab 8. Mortality, Recruitment, and Migration Rates
Biol. 356 Lab 8. Moraliy, Recruimen, and Migraion Raes (modified from Cox, 00, General Ecology Lab Manual, McGraw Hill) Las week we esimaed populaion size hrough several mehods. One assumpion of all hese
More informationPENALIZED LEAST SQUARES AND PENALIZED LIKELIHOOD
PENALIZED LEAST SQUARES AND PENALIZED LIKELIHOOD HAN XIAO 1. Penalized Leas Squares Lasso solves he following opimizaion problem, ˆβ lasso = arg max β R p+1 1 N y i β 0 N x ij β j β j (1.1) for some 0.
More informationInstitute for Mathematical Methods in Economics. University of Technology Vienna. Singapore, May Manfred Deistler
MULTIVARIATE TIME SERIES ANALYSIS AND FORECASTING Manfred Deisler E O S Economerics and Sysems Theory Insiue for Mahemaical Mehods in Economics Universiy of Technology Vienna Singapore, May 2004 Inroducion
More informationProblem Set 5. Graduate Macro II, Spring 2017 The University of Notre Dame Professor Sims
Problem Se 5 Graduae Macro II, Spring 2017 The Universiy of Nore Dame Professor Sims Insrucions: You may consul wih oher members of he class, bu please make sure o urn in your own work. Where applicable,
More informationLecture 33: November 29
36-705: Inermediae Saisics Fall 2017 Lecurer: Siva Balakrishnan Lecure 33: November 29 Today we will coninue discussing he boosrap, and hen ry o undersand why i works in a simple case. In he las lecure
More informationSome Basic Information about M-S-D Systems
Some Basic Informaion abou M-S-D Sysems 1 Inroducion We wan o give some summary of he facs concerning unforced (homogeneous) and forced (non-homogeneous) models for linear oscillaors governed by second-order,
More informationSolutions of Sample Problems for Third In-Class Exam Math 246, Spring 2011, Professor David Levermore
Soluions of Sample Problems for Third In-Class Exam Mah 6, Spring, Professor David Levermore Compue he Laplace ransform of f e from is definiion Soluion The definiion of he Laplace ransform gives L[f]s
More informationACE 562 Fall Lecture 4: Simple Linear Regression Model: Specification and Estimation. by Professor Scott H. Irwin
ACE 56 Fall 005 Lecure 4: Simple Linear Regression Model: Specificaion and Esimaion by Professor Sco H. Irwin Required Reading: Griffihs, Hill and Judge. "Simple Regression: Economic and Saisical Model
More informationTesting for a Single Factor Model in the Multivariate State Space Framework
esing for a Single Facor Model in he Mulivariae Sae Space Framework Chen C.-Y. M. Chiba and M. Kobayashi Inernaional Graduae School of Social Sciences Yokohama Naional Universiy Japan Faculy of Economics
More informationChapter 15: Phenomena. Chapter 15 Chemical Kinetics. Reaction Rates. Reaction Rates R P. Reaction Rates. Rate Laws
Chaper 5: Phenomena Phenomena: The reacion (aq) + B(aq) C(aq) was sudied a wo differen emperaures (98 K and 35 K). For each emperaure he reacion was sared by puing differen concenraions of he 3 species
More informationIntroduction D P. r = constant discount rate, g = Gordon Model (1962): constant dividend growth rate.
Inroducion Gordon Model (1962): D P = r g r = consan discoun rae, g = consan dividend growh rae. If raional expecaions of fuure discoun raes and dividend growh vary over ime, so should he D/P raio. Since
More informationEECE 301 Signals & Systems Prof. Mark Fowler
EECE 31 Signals & Sysems Prof. Mark Fowler Noe Se #1 C-T Sysems: Convoluion Represenaion Reading Assignmen: Secion 2.6 of Kamen and Heck 1/11 Course Flow Diagram The arrows here show concepual flow beween
More informationComparing Means: t-tests for One Sample & Two Related Samples
Comparing Means: -Tess for One Sample & Two Relaed Samples Using he z-tes: Assumpions -Tess for One Sample & Two Relaed Samples The z-es (of a sample mean agains a populaion mean) is based on he assumpion
More informationOn Measuring Pro-Poor Growth. 1. On Various Ways of Measuring Pro-Poor Growth: A Short Review of the Literature
On Measuring Pro-Poor Growh 1. On Various Ways of Measuring Pro-Poor Growh: A Shor eview of he Lieraure During he pas en years or so here have been various suggesions concerning he way one should check
More informationReferences are appeared in the last slide. Last update: (1393/08/19)
SYSEM IDEIFICAIO Ali Karimpour Associae Professor Ferdowsi Universi of Mashhad References are appeared in he las slide. Las updae: 0..204 393/08/9 Lecure 5 lecure 5 Parameer Esimaion Mehods opics o be
More informationWednesday, November 7 Handout: Heteroskedasticity
Amhers College Deparmen of Economics Economics 360 Fall 202 Wednesday, November 7 Handou: Heeroskedasiciy Preview Review o Regression Model o Sandard Ordinary Leas Squares (OLS) Premises o Esimaion Procedures
More informationMATH 128A, SUMMER 2009, FINAL EXAM SOLUTION
MATH 28A, SUMME 2009, FINAL EXAM SOLUTION BENJAMIN JOHNSON () (8 poins) [Lagrange Inerpolaion] (a) (4 poins) Le f be a funcion defined a some real numbers x 0,..., x n. Give a defining equaion for he Lagrange
More informationFinite Element Analysis of Structures
KAIT OE5 Finie Elemen Analysis of rucures Mid-erm Exam, Fall 9 (p) m. As shown in Fig., we model a russ srucure of uniform area (lengh, Area Am ) subjeced o a uniform body force ( f B e x N / m ) using
More informationWe just finished the Erdős-Stone Theorem, and ex(n, F ) (1 1/(χ(F ) 1)) ( n
Lecure 3 - Kövari-Sós-Turán Theorem Jacques Versraëe jacques@ucsd.edu We jus finished he Erdős-Sone Theorem, and ex(n, F ) ( /(χ(f ) )) ( n 2). So we have asympoics when χ(f ) 3 bu no when χ(f ) = 2 i.e.
More information= ( ) ) or a system of differential equations with continuous parametrization (T = R
XIII. DIFFERENCE AND DIFFERENTIAL EQUATIONS Ofen funcions, or a sysem of funcion, are paramerized in erms of some variable, usually denoed as and inerpreed as ime. The variable is wrien as a funcion of
More informationTHE UNIVERSITY OF TEXAS AT AUSTIN McCombs School of Business
THE UNIVERITY OF TEXA AT AUTIN McCombs chool of Business TA 7.5 Tom hively CLAICAL EAONAL DECOMPOITION - MULTIPLICATIVE MODEL Examples of easonaliy 8000 Quarerly sales for Wal-Mar for quarers a l e s 6000
More information04. Kinetics of a second order reaction
4. Kineics of a second order reacion Imporan conceps Reacion rae, reacion exen, reacion rae equaion, order of a reacion, firs-order reacions, second-order reacions, differenial and inegraed rae laws, Arrhenius
More informationMath 334 Fall 2011 Homework 11 Solutions
Dec. 2, 2 Mah 334 Fall 2 Homework Soluions Basic Problem. Transform he following iniial value problem ino an iniial value problem for a sysem: u + p()u + q() u g(), u() u, u () v. () Soluion. Le v u. Then
More information12: AUTOREGRESSIVE AND MOVING AVERAGE PROCESSES IN DISCRETE TIME. Σ j =
1: AUTOREGRESSIVE AND MOVING AVERAGE PROCESSES IN DISCRETE TIME Moving Averages Recall ha a whie noise process is a series { } = having variance σ. The whie noise process has specral densiy f (λ) = of
More informationVector autoregression VAR
Vecor auoregression VAR So far we have focused mosly on models where y deends on as y. More generally we migh wan o consider models for more han on variable. If we only care abou forecasing one series
More informationInstructor: Barry McQuarrie Page 1 of 5
Procedure for Solving radical equaions 1. Algebraically isolae one radical by iself on one side of equal sign. 2. Raise each side of he equaion o an appropriae power o remove he radical. 3. Simplify. 4.
More informationReady for euro? Empirical study of the actual monetary policy independence in Poland VECM modelling
Macroeconomerics Handou 2 Ready for euro? Empirical sudy of he acual moneary policy independence in Poland VECM modelling 1. Inroducion This classes are based on: Łukasz Goczek & Dagmara Mycielska, 2013.
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 informationECON 482 / WH Hong Time Series Data Analysis 1. The Nature of Time Series Data. Example of time series data (inflation and unemployment rates)
ECON 48 / WH Hong Time Series Daa Analysis. The Naure of Time Series Daa Example of ime series daa (inflaion and unemploymen raes) ECON 48 / WH Hong Time Series Daa Analysis The naure of ime series daa
More informationLinear Dynamic Models
Linear Dnamic Models and Forecasing Reference aricle: Ineracions beween he muliplier analsis and he principle of acceleraion Ouline. The sae space ssem as an approach o working wih ssems of difference
More informationDYNAMIC ECONOMETRIC MODELS vol NICHOLAS COPERNICUS UNIVERSITY - TORUŃ Józef Stawicki and Joanna Górka Nicholas Copernicus University
DYNAMIC ECONOMETRIC MODELS vol.. - NICHOLAS COPERNICUS UNIVERSITY - TORUŃ 996 Józef Sawicki and Joanna Górka Nicholas Copernicus Universiy ARMA represenaion for a sum of auoregressive processes In he ime
More informationVehicle Arrival Models : Headway
Chaper 12 Vehicle Arrival Models : Headway 12.1 Inroducion Modelling arrival of vehicle a secion of road is an imporan sep in raffic flow modelling. I has imporan applicaion in raffic flow simulaion where
More informationSub Module 2.6. Measurement of transient temperature
Mechanical Measuremens Prof. S.P.Venkaeshan Sub Module 2.6 Measuremen of ransien emperaure Many processes of engineering relevance involve variaions wih respec o ime. The sysem properies like emperaure,
More informationConcourse Math Spring 2012 Worked Examples: Matrix Methods for Solving Systems of 1st Order Linear Differential Equations
Concourse Mah 80 Spring 0 Worked Examples: Marix Mehods for Solving Sysems of s Order Linear Differenial Equaions The Main Idea: Given a sysem of s order linear differenial equaions d x d Ax wih iniial
More informationContinuous Time Linear Time Invariant (LTI) Systems. Dr. Ali Hussein Muqaibel. Introduction
/9/ Coninuous Time Linear Time Invarian (LTI) Sysems Why LTI? Inroducion Many physical sysems. Easy o solve mahemaically Available informaion abou analysis and design. We can apply superposiion LTI Sysem
More informationd 1 = c 1 b 2 - b 1 c 2 d 2 = c 1 b 3 - b 1 c 3
and d = c b - b c c d = c b - b c c This process is coninued unil he nh row has been compleed. The complee array of coefficiens is riangular. Noe ha in developing he array an enire row may be divided or
More informationA Specification Test for Linear Dynamic Stochastic General Equilibrium Models
Journal of Saisical and Economeric Mehods, vol.1, no.2, 2012, 65-70 ISSN: 2241-0384 (prin), 2241-0376 (online) Scienpress Ld, 2012 A Specificaion Tes for Linear Dynamic Sochasic General Equilibrium Models
More informationI. Return Calculations (20 pts, 4 points each)
Universiy of Washingon Spring 015 Deparmen of Economics Eric Zivo Econ 44 Miderm Exam Soluions This is a closed book and closed noe exam. However, you are allowed one page of noes (8.5 by 11 or A4 double-sided)
More informationDistribution of Estimates
Disribuion of Esimaes From Economerics (40) Linear Regression Model Assume (y,x ) is iid and E(x e )0 Esimaion Consisency y α + βx + he esimaes approach he rue values as he sample size increases Esimaion
More informationLecture 2 April 04, 2018
Sas 300C: Theory of Saisics Spring 208 Lecure 2 April 04, 208 Prof. Emmanuel Candes Scribe: Paulo Orensein; edied by Sephen Baes, XY Han Ouline Agenda: Global esing. Needle in a Haysack Problem 2. Threshold
More informationChapter 6. Systems of First Order Linear Differential Equations
Chaper 6 Sysems of Firs Order Linear Differenial Equaions We will only discuss firs order sysems However higher order sysems may be made ino firs order sysems by a rick shown below We will have a sligh
More informationPhysics 127b: Statistical Mechanics. Fokker-Planck Equation. Time Evolution
Physics 7b: Saisical Mechanics Fokker-Planck Equaion The Langevin equaion approach o he evoluion of he velociy disribuion for he Brownian paricle migh leave you uncomforable. A more formal reamen of his
More information