Refraction coefficient determination and modelling for the territory of the Kingdom of Saudi Arabia

Size: px
Start display at page:

Download "Refraction coefficient determination and modelling for the territory of the Kingdom of Saudi Arabia"

Transcription

1 Presened a he FIG ongress 08, May 6-, 08 in Isanbul, Turkey Refracion coefficien deerminaion and modelling for he erriory of he Kingdom of Saudi Arabia Ohman AL-KHERAYEF, KSA Vasil VALHINOV, BG Rossen GREBENITHARSKY, KSA Sanislava VALHEVA, BG Bandar AL-MUSLMANI, KSA Uhman AL-RUBAIA, KSA General ommission for Survey P. O. Box: 8798, Riyadh: 65, Saudi Arabia Tel , Fax o.alkherayef@gcs.gov.sa

2 onens: oinroducion oaim oproblem background and mehodology of compuaions ofield ess carried ou in he KSA Naional Verical Nework and available daa Sofware developmen Refracion coefficien compuaion and accuracy esimaion oonclusions and recommendaions

3 Inroducion Precise levelling is essenial for esablishing a Naional Verical Reference Sysem (NVRS); Refracion affecs precise levelling by increasing he loop misclosures; refracion effec on measured heigh difference per seup could reach up o - mm; Levelling insrumen s sofware auomaically correc for refracion using sandard amospheric-pressure models; The real influence of refracion on he line of sigh depends on he opography roughness along he levelling line and he air emperaure (Angus-Leppan, 984) If emperaure observaions obained during levelling are available, he refracion effec could be modelled -> improve he accuracy of he levelling neworks 3

4 Aim The aim: o presen resuls from he Refracion oefficien Deerminaion for Precise Levelling Observaion (RD_PLO) projec closely linked o he esablishmen of a new Naional Verical Reference Frame for he KSA The focus: ) compuaion and modelling of refracion for precise geodeic levelling using he available emperaure riples colleced during he precise levelling; ) accouning for opography roughness along he line of sigh by employing he so-called equivalen heigh. 4

5 Problem background and mehodology Kukkamaki s formula for refracion correcion o rod reading: ( ) 0 0 i i i Z Z Z Z d cg R assuming: wih classical formula refracion coef.: 3 ln ln where, 3 wih modified refracion coefficien: ( ) 3 ln ln bu 5 wih heoreical refracion coefficien: -/3

6 Problem background and mehodology Kukkamaki s formula for refracion correcion o rod reading: ( ) 0 0 i i i Z Z Z Z d cg R New refracion coefficien formula: assuming: bu he available i a i do no saisfy he condiion Uiliing: b a b a b a 3 3 assuming ln 3 4 ln T T T 3 T 6

7 Problem background and mehodology ompuing he equivalen heigh: S S l dl he S h S 0 i 0 li h i l i l i Refracion effec on he heigh difference is: ref ( R R ) back for Accouning for opography roughness: equiv ref h e _ back h e _ for ref uses boh modified classical and new formulae for refracion coefficien! 7

8 Field ess: NVN & available daa GS is responsible for he esablishmen of Naional Verical Nework (NVN) for he KSA Since 00, GS has carried ou four phases of precise geodeic levelling: boh in forward and backward direcion A mos phases simulaneous measuremens of emperaure a 3 differen reference levels above he ground 8

9 Field ess: NVN & available daa Amoun of daa o be processed: levelling: > ; emperaure: >

10 Field ess: sofware developmen Funcions of he differen REFRATION submodules 0

11 Field ess: compuaions & accuracy Scenarios for refracion coefficien compuaions For each scenario wo formulas were applied (he modified classical formula and he new one) ) one average -value for he erriory of he KSA ) wo ypes of -values per seup considering: case of normal amosphere, where (values<0) 54% of he compued -values case of inverse amosphere, where (-values>0) 46% of he compued -values 3) average -value per secion 4) -values referring o he middle poin of he secion (subjeced o saisical esing) 5) average secion -values from single/double runs (subjeced o correlaion analysis) 6) -values per levelling line secions as a moving average from secion -values; All -values in 5) and 6) are consisen; wih STD of abou 0.0; The -values for forward and backword direcions are coheren which shows he exisence of a real signal In filered values

12 Field ess: compuaions & accuracy 3D GIS models of refracion coefficien

13 Field ess: compuaions & accuracy Resuls validaion improvemen (60% - 70%) in levelling line misclosures obained wihin he heigh dependen 3D refracion model improvemen due o equivalen heigh reaching up o 70% per observed versus 43% per onracor s values of refracion correcions 3

14 Field ess: compuaions & accuracy Resuls validaion loop misclosures decreased wih 3-4 cm (70% improvemen); he effec of he equivalen heigh was no considered loop misclosures improvemen of 30% when he equivalen heigh was included 4

15 onclusions and recommendaions: Four possible scenarios based on he geodeic applicaion (he desired accuracy of levelling) and he availabiliy of emperaure measuremens; All scenarios need o be esed and validaed wih respec o heir conribuion o accuracy improvemen on he enire precise levelling nework in erms of adjused heighs. 5

16 onclusions and recommendaions: 6

17 onclusions and recommendaions: For fuure applicaions of Kukkamaki s formula, reference levels for he emperaure sensors shown in he Figure on he righ should are recomended; The emperaure measuremens are needed only o deermine he ype of he amosphere (normal or inverse), i.e. he sign of while he acual come from a R model; The new formula for compuing could be used as well, providing ha he relevan emperaure measuremens are obained a reference levels of 0.5 m,.5 m and 3.5 m 7

18 8

CHAPTER 10 VALIDATION OF TEST WITH ARTIFICAL NEURAL NETWORK

CHAPTER 10 VALIDATION OF TEST WITH ARTIFICAL NEURAL NETWORK 175 CHAPTER 10 VALIDATION OF TEST WITH ARTIFICAL NEURAL NETWORK 10.1 INTRODUCTION Amongs he research work performed, he bes resuls of experimenal work are validaed wih Arificial Neural Nework. From he

More information

The electromagnetic interference in case of onboard navy ships computers - a new approach

The electromagnetic interference in case of onboard navy ships computers - a new approach The elecromagneic inerference in case of onboard navy ships compuers - a new approach Prof. dr. ing. Alexandru SOTIR Naval Academy Mircea cel Bărân, Fulgerului Sree, Consanţa, soiralexandru@yahoo.com Absrac.

More information

Physics 235 Chapter 2. Chapter 2 Newtonian Mechanics Single Particle

Physics 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 information

Shiva Akhtarian MSc Student, Department of Computer Engineering and Information Technology, Payame Noor University, Iran

Shiva Akhtarian MSc Student, Department of Computer Engineering and Information Technology, Payame Noor University, Iran Curren Trends in Technology and Science ISSN : 79-055 8hSASTech 04 Symposium on Advances in Science & Technology-Commission-IV Mashhad, Iran A New for Sofware Reliabiliy Evaluaion Based on NHPP wih Imperfec

More information

Chapter 2. First Order Scalar Equations

Chapter 2. First Order Scalar Equations Chaper. Firs Order Scalar Equaions We sar our sudy of differenial equaions in he same way he pioneers in his field did. We show paricular echniques o solve paricular ypes of firs order differenial equaions.

More information

d 1 = c 1 b 2 - b 1 c 2 d 2 = c 1 b 3 - b 1 c 3

d 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 information

Bias in Conditional and Unconditional Fixed Effects Logit Estimation: a Correction * Tom Coupé

Bias in Conditional and Unconditional Fixed Effects Logit Estimation: a Correction * Tom Coupé Bias in Condiional and Uncondiional Fixed Effecs Logi Esimaion: a Correcion * Tom Coupé Economics Educaion and Research Consorium, Naional Universiy of Kyiv Mohyla Academy Address: Vul Voloska 10, 04070

More information

Scientific Herald of the Voronezh State University of Architecture and Civil Engineering. Construction and Architecture

Scientific Herald of the Voronezh State University of Architecture and Civil Engineering. Construction and Architecture Scienific Herald of he Voronezh Sae Universiy of Archiecure and Civil Engineering. Consrucion and Archiecure UDC 625.863.6:551.328 Voronezh Sae Universiy of Archiecure and Civil Engineering Ph. D. applican

More information

Methodology. -ratios are biased and that the appropriate critical values have to be increased by an amount. that depends on the sample size.

Methodology. -ratios are biased and that the appropriate critical values have to be increased by an amount. that depends on the sample size. Mehodology. Uni Roo Tess A ime series is inegraed when i has a mean revering propery and a finie variance. I is only emporarily ou of equilibrium and is called saionary in I(0). However a ime series ha

More information

A Specification Test for Linear Dynamic Stochastic General Equilibrium Models

A 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 information

References are appeared in the last slide. Last update: (1393/08/19)

References 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 information

Testing for a Single Factor Model in the Multivariate State Space Framework

Testing 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 information

Time series Decomposition method

Time 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 information

UNIVERSITÀ DI PISA DIPARTIMENTO DI INGEGNERIA MECCANICA, NUCLEARE E DELLA PRODUZIONE VIA DIOTISALVI 2, PISA

UNIVERSITÀ DI PISA DIPARTIMENTO DI INGEGNERIA MECCANICA, NUCLEARE E DELLA PRODUZIONE VIA DIOTISALVI 2, PISA G r u p p o R I c e r c a N u c le a r e S a n P I e r o a G r a d o N u c l e a r a n d I n d u s r I a l E n g I n e e r I n g UNIVERSIÀ DI PISA DIPARIMENO DI INGEGNERIA MECCANICA NUCLEARE E DELLA PRODUZIONE

More information

Regression with Time Series Data

Regression with Time Series Data Regression wih Time Series Daa y = β 0 + β 1 x 1 +...+ β k x k + u Serial Correlaion and Heeroskedasiciy Time Series - Serial Correlaion and Heeroskedasiciy 1 Serially Correlaed Errors: Consequences Wih

More information

On Measuring Pro-Poor Growth. 1. On Various Ways of Measuring Pro-Poor Growth: A Short Review of the Literature

On 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 information

Calculation of the Two High Voltage Transmission Line Conductors Minimum Distance

Calculation of the Two High Voltage Transmission Line Conductors Minimum Distance World Journal of Engineering and Technology, 15, 3, 89-96 Published Online Ocober 15 in SciRes. hp://www.scirp.org/journal/wje hp://dx.doi.org/1.436/wje.15.33c14 Calculaion of he Two High Volage Transmission

More information

DEPARTMENT OF STATISTICS

DEPARTMENT 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 information

20. Applications of the Genetic-Drift Model

20. Applications of the Genetic-Drift Model 0. Applicaions of he Geneic-Drif Model 1) Deermining he probabiliy of forming any paricular combinaion of genoypes in he nex generaion: Example: If he parenal allele frequencies are p 0 = 0.35 and q 0

More information

Air Traffic Forecast Empirical Research Based on the MCMC Method

Air Traffic Forecast Empirical Research Based on the MCMC Method Compuer and Informaion Science; Vol. 5, No. 5; 0 ISSN 93-8989 E-ISSN 93-8997 Published by Canadian Cener of Science and Educaion Air Traffic Forecas Empirical Research Based on he MCMC Mehod Jian-bo Wang,

More information

Robust estimation based on the first- and third-moment restrictions of the power transformation model

Robust estimation based on the first- and third-moment restrictions of the power transformation model h Inernaional Congress on Modelling and Simulaion, Adelaide, Ausralia, 6 December 3 www.mssanz.org.au/modsim3 Robus esimaion based on he firs- and hird-momen resricions of he power ransformaion Nawaa,

More information

The expectation value of the field operator.

The 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 information

ACE 564 Spring Lecture 7. Extensions of The Multiple Regression Model: Dummy Independent Variables. by Professor Scott H.

ACE 564 Spring Lecture 7. Extensions of The Multiple Regression Model: Dummy Independent Variables. by Professor Scott H. ACE 564 Spring 2006 Lecure 7 Exensions of The Muliple Regression Model: Dumm Independen Variables b Professor Sco H. Irwin Readings: Griffihs, Hill and Judge. "Dumm Variables and Varing Coefficien Models

More information

GMM - Generalized Method of Moments

GMM - 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 information

Lecture 20: Riccati Equations and Least Squares Feedback Control

Lecture 20: Riccati Equations and Least Squares Feedback Control 34-5 LINEAR SYSTEMS Lecure : Riccai Equaions and Leas Squares Feedback Conrol 5.6.4 Sae Feedback via Riccai Equaions A recursive approach in generaing he marix-valued funcion W ( ) equaion for i for he

More information

Comparing Means: t-tests for One Sample & Two Related Samples

Comparing 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 information

Unified Framework For Developing Testing Effort Dependent Software Reliability Growth Models With Change Point And Imperfect Debugging

Unified Framework For Developing Testing Effort Dependent Software Reliability Growth Models With Change Point And Imperfect Debugging Proceedings of he 4 h Naional Conference; INDIACom-00 Compuing For Naion Developmen, February 5 6, 00 Bharai Vidyapeeh s Insiue of Compuer Applicaions and Managemen, New Delhi Unified Framework For Developing

More information

Probabilistic Robotics SLAM

Probabilistic Robotics SLAM Probabilisic Roboics SLAM The SLAM Problem SLAM is he process by which a robo builds a map of he environmen and, a he same ime, uses his map o compue is locaion Localizaion: inferring locaion given a map

More information

3.1.3 INTRODUCTION TO DYNAMIC OPTIMIZATION: DISCRETE TIME PROBLEMS. A. The Hamiltonian and First-Order Conditions in a Finite Time Horizon

3.1.3 INTRODUCTION TO DYNAMIC OPTIMIZATION: DISCRETE TIME PROBLEMS. A. The Hamiltonian and First-Order Conditions in a Finite Time Horizon 3..3 INRODUCION O DYNAMIC OPIMIZAION: DISCREE IME PROBLEMS A. he Hamilonian and Firs-Order Condiions in a Finie ime Horizon Define a new funcion, he Hamilonian funcion, H. H he change in he oal value of

More information

5 The fitting methods used in the normalization of DSD

5 The fitting methods used in the normalization of DSD The fiing mehods used in he normalizaion of DSD.1 Inroducion Sempere-Torres e al. 1994 presened a general formulaion for he DSD ha was able o reproduce and inerpre all previous sudies of DSD. The mehodology

More information

4.1 Other Interpretations of Ridge Regression

4.1 Other Interpretations of Ridge Regression CHAPTER 4 FURTHER RIDGE THEORY 4. Oher Inerpreaions of Ridge Regression In his secion we will presen hree inerpreaions for he use of ridge regression. The firs one is analogous o Hoerl and Kennard reasoning

More information

INTEGRATION OF LEVELING AND INSAR DATA FOR LAND SUBSIDENCE MONITORING

INTEGRATION OF LEVELING AND INSAR DATA FOR LAND SUBSIDENCE MONITORING Proceedings 11 h FIG Symposium on Deformaion Measuremens Sanorin Greece 3. INTEGRATION OF LEVELING AND INSAR DATA FOR LAND SUSIDENCE MONITORING Dennis Odijk Frank Kenselaar and Ramon Hanssen Delf Universiy

More information

R t. C t P t. + u t. C t = αp t + βr t + v t. + β + w t

R t. C t P t. + u t. C t = αp t + βr t + v t. + β + w t Exercise 7 C P = α + β R P + u C = αp + βr + v (a) (b) C R = α P R + β + w (c) Assumpions abou he disurbances u, v, w : Classical assumions on he disurbance of one of he equaions, eg. on (b): E(v v s P,

More information

Reliability of Technical Systems

Reliability of Technical Systems eliabiliy of Technical Sysems Main Topics Inroducion, Key erms, framing he problem eliabiliy parameers: Failure ae, Failure Probabiliy, Availabiliy, ec. Some imporan reliabiliy disribuions Componen reliabiliy

More information

RC, RL and RLC circuits

RC, RL and RLC circuits Name Dae Time o Complee h m Parner Course/ Secion / Grade RC, RL and RLC circuis Inroducion In his experimen we will invesigae he behavior of circuis conaining combinaions of resisors, capaciors, and inducors.

More information

Development of a new metrological model for measuring of the water surface evaporation Tovmach L. Tovmach Yr. Abstract Introduction

Development of a new metrological model for measuring of the water surface evaporation Tovmach L. Tovmach Yr. Abstract Introduction Developmen of a new merological model for measuring of he waer surface evaporaion Tovmach L. Tovmach Yr. Sae Hydrological Insiue 23 Second Line, 199053 S. Peersburg, Russian Federaion Telephone (812) 323

More information

UNC resolution Uncertainty Learning Objectives: measurement interval ( You will turn in two worksheets and

UNC resolution Uncertainty Learning Objectives: measurement interval ( You will turn in two worksheets and UNC Uncerainy revised Augus 30, 017 Learning Objecives: During his lab, you will learn how o 1. esimae he uncerainy in a direcly measured quaniy.. esimae he uncerainy in a quaniy ha is calculaed from quaniies

More information

Stable block Toeplitz matrix for the processing of multichannel seismic data

Stable block Toeplitz matrix for the processing of multichannel seismic data Indian Journal of Marine Sciences Vol. 33(3), Sepember 2004, pp. 215-219 Sable block Toepliz marix for he processing of mulichannel seismic daa Kiri Srivasava* & V P Dimri Naional Geophysical Research

More information

1. VELOCITY AND ACCELERATION

1. VELOCITY AND ACCELERATION 1. VELOCITY AND ACCELERATION 1.1 Kinemaics Equaions s = u + 1 a and s = v 1 a s = 1 (u + v) v = u + as 1. Displacemen-Time Graph Gradien = speed 1.3 Velociy-Time Graph Gradien = acceleraion Area under

More information

Solutions to Odd Number Exercises in Chapter 6

Solutions 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 information

Notes on Kalman Filtering

Notes 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 information

Linear Response Theory: The connection between QFT and experiments

Linear Response Theory: The connection between QFT and experiments Phys540.nb 39 3 Linear Response Theory: The connecion beween QFT and experimens 3.1. Basic conceps and ideas Q: How do we measure he conduciviy of a meal? A: we firs inroduce a weak elecric field E, and

More information

Introduction D P. r = constant discount rate, g = Gordon Model (1962): constant dividend growth rate.

Introduction 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 information

Anti-Disturbance Control for Multiple Disturbances

Anti-Disturbance Control for Multiple Disturbances Workshop a 3 ACC Ani-Disurbance Conrol for Muliple Disurbances Lei Guo (lguo@buaa.edu.cn) Naional Key Laboraory on Science and Technology on Aircraf Conrol, Beihang Universiy, Beijing, 9, P.R. China. Presened

More information

Probabilistic Robotics SLAM

Probabilistic Robotics SLAM Probabilisic Roboics SLAM The SLAM Problem SLAM is he process by which a robo builds a map of he environmen and, a he same ime, uses his map o compue is locaion Localizaion: inferring locaion given a map

More information

8. Basic RL and RC Circuits

8. Basic RL and RC Circuits 8. Basic L and C Circuis This chaper deals wih he soluions of he responses of L and C circuis The analysis of C and L circuis leads o a linear differenial equaion This chaper covers he following opics

More information

The Potential Effectiveness of the Detection of Pulsed Signals in the Non-Uniform Sampling

The Potential Effectiveness of the Detection of Pulsed Signals in the Non-Uniform Sampling The Poenial Effeciveness of he Deecion of Pulsed Signals in he Non-Uniform Sampling Arhur Smirnov, Sanislav Vorobiev and Ajih Abraham 3, 4 Deparmen of Compuer Science, Universiy of Illinois a Chicago,

More information

Measurement Error 1: Consequences Page 1. Definitions. For two variables, X and Y, the following hold: Expectation, or Mean, of X.

Measurement Error 1: Consequences Page 1. Definitions. For two variables, X and Y, the following hold: Expectation, or Mean, of X. Measuremen Error 1: Consequences of Measuremen Error Richard Williams, Universiy of Nore Dame, hps://www3.nd.edu/~rwilliam/ Las revised January 1, 015 Definiions. For wo variables, X and Y, he following

More information

Module 4: Time Response of discrete time systems Lecture Note 2

Module 4: Time Response of discrete time systems Lecture Note 2 Module 4: Time Response of discree ime sysems Lecure Noe 2 1 Prooype second order sysem The sudy of a second order sysem is imporan because many higher order sysem can be approimaed by a second order model

More information

Final Spring 2007

Final 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 information

A Fusion Model for Day-Ahead Wind Speed Prediction based on the Validity of the Information

A Fusion Model for Day-Ahead Wind Speed Prediction based on the Validity of the Information A Fusion Model for Day-Ahead Wind Speed Predicion based on he Validiy of he Informaion Jie Wan 1,a, Wenbo Hao 2,b, Guorui Ren 1,c,Leilei Zhao 2,d, Bingliang Xu 2,e, Chengzhi Sun 2,f,Zhigang Zhao 3,g, Chengrui

More information

Modeling and Forecasting Volatility Autoregressive Conditional Heteroskedasticity Models. Economic Forecasting Anthony Tay Slide 1

Modeling and Forecasting Volatility Autoregressive Conditional Heteroskedasticity Models. Economic Forecasting Anthony Tay Slide 1 Modeling and Forecasing Volailiy Auoregressive Condiional Heeroskedasiciy Models Anhony Tay Slide 1 smpl @all line(m) sii dl_sii S TII D L _ S TII 4,000. 3,000.1.0,000 -.1 1,000 -. 0 86 88 90 9 94 96 98

More information

Recursive Least-Squares Fixed-Interval Smoother Using Covariance Information based on Innovation Approach in Linear Continuous Stochastic Systems

Recursive Least-Squares Fixed-Interval Smoother Using Covariance Information based on Innovation Approach in Linear Continuous Stochastic Systems 8 Froniers in Signal Processing, Vol. 1, No. 1, July 217 hps://dx.doi.org/1.2266/fsp.217.112 Recursive Leas-Squares Fixed-Inerval Smooher Using Covariance Informaion based on Innovaion Approach in Linear

More information

Some Basic Information about M-S-D Systems

Some 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 information

Econ107 Applied Econometrics Topic 7: Multicollinearity (Studenmund, Chapter 8)

Econ107 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 information

ACE 562 Fall Lecture 4: Simple Linear Regression Model: Specification and Estimation. by Professor Scott H. Irwin

ACE 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 information

Maple Tools for Differential Equations A. J. Meir

Maple Tools for Differential Equations A. J. Meir Maple Tools for Differenial Equaions A. J. Meir Copyrigh (C) A. J. Meir. All righs reserved. This workshee is for educaional use only. No par of his publicaion may be reproduced or ransmied for profi in

More information

THE BERNOULLI NUMBERS. t k. = lim. = lim = 1, d t B 1 = lim. 1+e t te t = lim t 0 (e t 1) 2. = lim = 1 2.

THE BERNOULLI NUMBERS. t k. = lim. = lim = 1, d t B 1 = lim. 1+e t te t = lim t 0 (e t 1) 2. = lim = 1 2. THE BERNOULLI NUMBERS The Bernoulli numbers are defined here by he exponenial generaing funcion ( e The firs one is easy o compue: (2 and (3 B 0 lim 0 e lim, 0 e ( d B lim 0 d e +e e lim 0 (e 2 lim 0 2(e

More information

14 Autoregressive Moving Average Models

14 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 information

Recursive Estimation and Identification of Time-Varying Long- Term Fading Channels

Recursive Estimation and Identification of Time-Varying Long- Term Fading Channels Recursive Esimaion and Idenificaion of ime-varying Long- erm Fading Channels Mohammed M. Olama, Kiran K. Jaladhi, Seddi M. Djouadi, and Charalambos D. Charalambous 2 Universiy of ennessee Deparmen of Elecrical

More information

EUROINDICATORS WORKING GROUP. A new method for assessing direct versus indirect adjustment

EUROINDICATORS WORKING GROUP. A new method for assessing direct versus indirect adjustment EUROINDICATOR WORKING GROUP 5 TH MEETING TH & TH JUNE 0 EUROTAT C4 DOC 330/ A new mehod for assessing direc versus indirec adjusmen ITEM 4.3 ON THE AGENDA OF THE MEETING OF THE WORKING GROUP ON EUROINDICATOR

More information

NOVEL PROCEDURE TO COMPUTE A CONTACT ZONE MAGNITUDE OF VIBRATIONS OF TWO-LAYERED UNCOUPLED PLATES

NOVEL PROCEDURE TO COMPUTE A CONTACT ZONE MAGNITUDE OF VIBRATIONS OF TWO-LAYERED UNCOUPLED PLATES NOVEL PROCEDURE TO COMPUTE A CONTACT ZONE MAGNITUDE OF VIBRATION OF TO-LAYERED UNCOUPLED PLATE J. AREJCEICZ, V. A. KRYKO, AND O. OVIANNIKOVA Received February A novel ieraion procedure for dynamical problems,

More information

Structural Dynamics and Earthquake Engineering

Structural Dynamics and Earthquake Engineering Srucural Dynamics and Earhquae Engineering Course 1 Inroducion. Single degree of freedom sysems: Equaions of moion, problem saemen, soluion mehods. Course noes are available for download a hp://www.c.up.ro/users/aurelsraan/

More information

Dynamic Models, Autocorrelation and Forecasting

Dynamic Models, Autocorrelation and Forecasting ECON 4551 Economerics II Memorial Universiy of Newfoundland Dynamic Models, Auocorrelaion and Forecasing Adaped from Vera Tabakova s noes 9.1 Inroducion 9.2 Lags in he Error Term: Auocorrelaion 9.3 Esimaing

More information

Combined Bending with Induced or Applied Torsion of FRP I-Section Beams

Combined Bending with Induced or Applied Torsion of FRP I-Section Beams Combined Bending wih Induced or Applied Torsion of FRP I-Secion Beams MOJTABA B. SIRJANI School of Science and Technology Norfolk Sae Universiy Norfolk, Virginia 34504 USA sirjani@nsu.edu STEA B. BONDI

More information

Generation of Simulated Time Series for Wind Speed Based on a Statistical Wind Atlas for Iceland

Generation of Simulated Time Series for Wind Speed Based on a Statistical Wind Atlas for Iceland Generaion of Simulaed Time Series for Wind Speed Based on a Saisical Wind Alas for Iceland Krisján Jónasson Naural Sciences Universiy of Iceland jonasson@hi.is Gunnar Geir Péursson Naural Sciences Universiy

More information

Chapter 3 (Lectures 12, 13 and 14) Longitudinal stick free static stability and control

Chapter 3 (Lectures 12, 13 and 14) Longitudinal stick free static stability and control Fligh dynamics II Sabiliy and conrol haper 3 (Lecures 1, 13 and 14) Longiudinal sick free saic sabiliy and conrol Keywords : inge momen and is variaion wih ail angle, elevaor deflecion and ab deflecion

More information

STATE-SPACE MODELLING. A mass balance across the tank gives:

STATE-SPACE MODELLING. A mass balance across the tank gives: B. Lennox and N.F. Thornhill, 9, Sae Space Modelling, IChemE Process Managemen and Conrol Subjec Group Newsleer STE-SPACE MODELLING Inroducion: Over he pas decade or so here has been an ever increasing

More information

Content-Based Shape Retrieval Using Different Shape Descriptors: A Comparative Study Dengsheng Zhang and Guojun Lu

Content-Based Shape Retrieval Using Different Shape Descriptors: A Comparative Study Dengsheng Zhang and Guojun Lu Conen-Based Shape Rerieval Using Differen Shape Descripors: A Comparaive Sudy Dengsheng Zhang and Guojun Lu Gippsland School of Compuing and Informaion Technology Monash Universiy Churchill, Vicoria 3842

More information

Problem Set on Differential Equations

Problem Set on Differential Equations Problem Se on Differenial Equaions 1. Solve he following differenial equaions (a) x () = e x (), x () = 3/ 4. (b) x () = e x (), x (1) =. (c) xe () = + (1 x ()) e, x () =.. (An asse marke model). Le p()

More information

ESTIMATION OF DYNAMIC PANEL DATA MODELS WHEN REGRESSION COEFFICIENTS AND INDIVIDUAL EFFECTS ARE TIME-VARYING

ESTIMATION OF DYNAMIC PANEL DATA MODELS WHEN REGRESSION COEFFICIENTS AND INDIVIDUAL EFFECTS ARE TIME-VARYING Inernaional Journal of Social Science and Economic Research Volume:02 Issue:0 ESTIMATION OF DYNAMIC PANEL DATA MODELS WHEN REGRESSION COEFFICIENTS AND INDIVIDUAL EFFECTS ARE TIME-VARYING Chung-ki Min Professor

More information

LINEAR SLOT DIFFUSERS

LINEAR SLOT DIFFUSERS Supply, Reurn, Dummy Linear Slo Diffusers A OSLD OTIMA model OSLD is a supply linear slo diffuser wih inegral volume conrol damper and hi and miss air sraighening deflecors. h Hi and miss air sraigheners

More information

Experiment 123 Determination of the sound wave velocity with the method of Lissajous figures

Experiment 123 Determination of the sound wave velocity with the method of Lissajous figures perimen 3 Deerminaion of he sound wave veloci wih he mehod of Lissajous figures The aim of he eercise To sud acousic wave propagaion in he air To deermine of he sound wave veloci in he air Mehodolog of

More information

THE MYSTERY OF STOCHASTIC MECHANICS. Edward Nelson Department of Mathematics Princeton University

THE MYSTERY OF STOCHASTIC MECHANICS. Edward Nelson Department of Mathematics Princeton University THE MYSTERY OF STOCHASTIC MECHANICS Edward Nelson Deparmen of Mahemaics Princeon Universiy 1 Classical Hamilon-Jacobi heory N paricles of various masses on a Euclidean space. Incorporae he masses in he

More information

Numerical Dispersion

Numerical Dispersion eview of Linear Numerical Sabiliy Numerical Dispersion n he previous lecure, we considered he linear numerical sabiliy of boh advecion and diffusion erms when approimaed wih several spaial and emporal

More information

Symmetry and Numerical Solutions for Systems of Non-linear Reaction Diffusion Equations

Symmetry and Numerical Solutions for Systems of Non-linear Reaction Diffusion Equations Symmery and Numerical Soluions for Sysems of Non-linear Reacion Diffusion Equaions Sanjeev Kumar* and Ravendra Singh Deparmen of Mahemaics, (Dr. B. R. Ambedkar niversiy, Agra), I. B. S. Khandari, Agra-8

More information

Vectorautoregressive Model and Cointegration Analysis. Time Series Analysis Dr. Sevtap Kestel 1

Vectorautoregressive 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 information

Keywords: thermal stress; thermal fatigue; inverse analysis; heat conduction; regularization

Keywords: thermal stress; thermal fatigue; inverse analysis; heat conduction; regularization Proceedings Inverse Analysis for Esimaing Temperaure and Residual Sress Disribuions in a Pipe from Ouer Surface Temperaure Measuremen and Is Regularizaion Shiro Kubo * and Shoki Taguwa Deparmen of Mechanical

More information

Kriging Models Predicting Atrazine Concentrations in Surface Water Draining Agricultural Watersheds

Kriging 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 information

Wavelet Variance, Covariance and Correlation Analysis of BSE and NSE Indexes Financial Time Series

Wavelet Variance, Covariance and Correlation Analysis of BSE and NSE Indexes Financial Time Series Wavele Variance, Covariance and Correlaion Analysis of BSE and NSE Indexes Financial Time Series Anu Kumar 1*, Sangeea Pan 1, Lokesh Kumar Joshi 1 Deparmen of Mahemaics, Universiy of Peroleum & Energy

More information

Modal identification of structures from roving input data by means of maximum likelihood estimation of the state space model

Modal identification of structures from roving input data by means of maximum likelihood estimation of the state space model Modal idenificaion of srucures from roving inpu daa by means of maximum likelihood esimaion of he sae space model J. Cara, J. Juan, E. Alarcón Absrac The usual way o perform a forced vibraion es is o fix

More information

(a) Set up the least squares estimation procedure for this problem, which will consist in minimizing the sum of squared residuals. 2 t.

(a) Set up the least squares estimation procedure for this problem, which will consist in minimizing the sum of squared residuals. 2 t. Insrucions: The goal of he problem se is o undersand wha you are doing raher han jus geing he correc resul. Please show your work clearly and nealy. No credi will be given o lae homework, regardless of

More information

Estimation of Kinetic Friction Coefficient for Sliding Rigid Block Nonstructural Components

Estimation of Kinetic Friction Coefficient for Sliding Rigid Block Nonstructural Components 7 Esimaion of Kineic Fricion Coefficien for Sliding Rigid Block Nonsrucural Componens Cagdas Kafali Ph.D. Candidae, School of Civil and Environmenal Engineering, Cornell Universiy Research Supervisor:

More information

Excel-Based Solution Method For The Optimal Policy Of The Hadley And Whittin s Exact Model With Arma Demand

Excel-Based Solution Method For The Optimal Policy Of The Hadley And Whittin s Exact Model With Arma Demand Excel-Based Soluion Mehod For The Opimal Policy Of The Hadley And Whiin s Exac Model Wih Arma Demand Kal Nami School of Business and Economics Winson Salem Sae Universiy Winson Salem, NC 27110 Phone: (336)750-2338

More information

State-Space Models. Initialization, Estimation and Smoothing of the Kalman Filter

State-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 information

Wednesday, November 7 Handout: Heteroskedasticity

Wednesday, 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 information

ERROR LOCATING CODES AND EXTENDED HAMMING CODE. Pankaj Kumar Das. 1. Introduction and preliminaries

ERROR LOCATING CODES AND EXTENDED HAMMING CODE. Pankaj Kumar Das. 1. Introduction and preliminaries MATEMATIČKI VESNIK MATEMATIQKI VESNIK 70, 1 (2018), 89 94 March 2018 research paper originalni nauqni rad ERROR LOCATING CODES AND EXTENDED HAMMING CODE Pankaj Kumar Das Absrac. Error-locaing codes, firs

More information

dy dx = xey (a) y(0) = 2 (b) y(1) = 2.5 SOLUTION: See next page

dy dx = xey (a) y(0) = 2 (b) y(1) = 2.5 SOLUTION: See next page Assignmen 1 MATH 2270 SOLUTION Please wrie ou complee soluions for each of he following 6 problems (one more will sill be added). You may, of course, consul wih your classmaes, he exbook or oher resources,

More information

Vehicle Arrival Models : Headway

Vehicle 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 information

Phys1112: DC and RC circuits

Phys1112: DC and RC circuits Name: Group Members: Dae: TA s Name: Phys1112: DC and RC circuis Objecives: 1. To undersand curren and volage characerisics of a DC RC discharging circui. 2. To undersand he effec of he RC ime consan.

More information

2.160 System Identification, Estimation, and Learning. Lecture Notes No. 8. March 6, 2006

2.160 System Identification, Estimation, and Learning. Lecture Notes No. 8. March 6, 2006 2.160 Sysem Idenificaion, Esimaion, and Learning Lecure Noes No. 8 March 6, 2006 4.9 Eended Kalman Filer In many pracical problems, he process dynamics are nonlinear. w Process Dynamics v y u Model (Linearized)

More information

In this paper the innovations state space models (ETS) are used in series with:

In this paper the innovations state space models (ETS) are used in series with: Time series models for differen seasonal paerns Blaconá, M.T, Andreozzi*, L. and Magnano, L. Naional Universiy of Rosario, (*)CONICET - Argenina Absrac In his paper Innovaions Sae Space Models (ETS) are

More information

Improved Approximate Solutions for Nonlinear Evolutions Equations in Mathematical Physics Using the Reduced Differential Transform Method

Improved Approximate Solutions for Nonlinear Evolutions Equations in Mathematical Physics Using the Reduced Differential Transform Method Journal of Applied Mahemaics & Bioinformaics, vol., no., 01, 1-14 ISSN: 179-660 (prin), 179-699 (online) Scienpress Ld, 01 Improved Approimae Soluions for Nonlinear Evoluions Equaions in Mahemaical Physics

More information

Predator - Prey Model Trajectories and the nonlinear conservation law

Predator - Prey Model Trajectories and the nonlinear conservation law Predaor - Prey Model Trajecories and he nonlinear conservaion law James K. Peerson Deparmen of Biological Sciences and Deparmen of Mahemaical Sciences Clemson Universiy Ocober 28, 213 Ouline Drawing Trajecories

More information

Echocardiography Project and Finite Fourier Series

Echocardiography Project and Finite Fourier Series Echocardiography Projec and Finie Fourier Series 1 U M An echocardiagram is a plo of how a porion of he hear moves as he funcion of ime over he one or more hearbea cycles If he hearbea repeas iself every

More information

Georey E. Hinton. University oftoronto. Technical Report CRG-TR February 22, Abstract

Georey E. Hinton. University oftoronto.   Technical Report CRG-TR February 22, Abstract Parameer Esimaion for Linear Dynamical Sysems Zoubin Ghahramani Georey E. Hinon Deparmen of Compuer Science Universiy oftorono 6 King's College Road Torono, Canada M5S A4 Email: zoubin@cs.orono.edu Technical

More information

Random Walk with Anti-Correlated Steps

Random Walk with Anti-Correlated Steps Random Walk wih Ani-Correlaed Seps John Noga Dirk Wagner 2 Absrac We conjecure he expeced value of random walks wih ani-correlaed seps o be exacly. We suppor his conjecure wih 2 plausibiliy argumens and

More information

How to Deal with Structural Breaks in Practical Cointegration Analysis

How to Deal with Structural Breaks in Practical Cointegration Analysis How o Deal wih Srucural Breaks in Pracical Coinegraion Analysis Roselyne Joyeux * School of Economic and Financial Sudies Macquarie Universiy December 00 ABSTRACT In his noe we consider he reamen of srucural

More information

Cointegration and Implications for Forecasting

Cointegration 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 information

ACE 562 Fall Lecture 5: The Simple Linear Regression Model: Sampling Properties of the Least Squares Estimators. by Professor Scott H.

ACE 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