Time Series Approaches to Statistical Process Control
|
|
- Cameron Martin
- 5 years ago
- Views:
Transcription
1 Inernaional Journal of Innovaive Research in Compuer Science & Technolog (IJIRCST) ISSN: , Volume-1, Issue-, November- 013 Time Series Approaches o Saisical Process Conrol Aji Goswami, Prof. (Dr.) H.Nr. Dua Absrac- In radiional Saisical Process Conrol (SPC) procedure, a sandard assumpion is ha observaion from he process a differen ime poins are independen random variable. However, his independen assumpion is no alwas rue.. In fac, in he las decade, he ime-series approach o Saisical Process Conrol has been a opic of ineres of man quali scieniss. In his paper, an aemp has been made o highligh some of he works in his area and a few models will be discussed o analze he effecs of auocorrelaion on some sandard conrol chars echniques. Index Terms Auocorrelaion, Dependen observaion, EWMA conrol char, SPC conrol o deal wih he auo correlaion in recen imes, he are: (a) Tradiional conrol chars are used, bu mehods used o esimae he process parameers and finall he conrol limis are adjused in order o accoun for he auo correlaion. This mehod is recommended when he level of auo correlaion is no exremel high. (b) A ime series model is fied o he process observaions and he residuals from his model are used in radiional conrol chars I. INTRODUCTION Mos of he indusrial processes ofen have complex behaviors, when successive unis are relaed o previous one. When here are carr over effec from he earlier observaions, he sandard conrol chars ma exhibi an increased frequenc of false alarms. There is an increased likelihood ha he daa will exhibi auocorrelaion in ssems where he process ime is longer han he ime beween sample collecions [1]. Auocorrelaion resuls from man facors- such as work shif, operaor roaions, mechanic or echnician changes. Someimes some processes, inherenl produces auocorrelaed daa. Tradiional Shewhar conrol chars are sensiive o auocorrelaed daa and even a low levels of correlaion, a significan changes ma occur in char properies including shor Average Run Lengh (ARL). Hence in recen imes, sudies on auocorrelaion daa is an imporan area for SPC users and more aenion is being paid b man quali scieniss o sud he behavior of conrol char performance in presence of auocorrelaion. II. EFFECT OF AUTO CORRELATION IN PROCESS DATA When here is significan auocorrelaion in he process daa, i is no advisable o use radiional conrol char echnique wihou modificaion. Two general approaches have been considered b he scholars of quali Manuscrip received November, 013. Aji Goswami, Research Scholar, Dibrugarh Universi, Dibrugarh, Assam, India ( agoswami09@gmail.com). Dr. H. Nr. Dua, Professor, Deparmen of Saisics, Dibrugarh Universi, Dibrugarh, Assam, India ( hnd_sa@rediffmail.com). III. REVIEW OF PAST WORKS ON AUTOCORRELATED PROCESS DATA Dua & Phukan [] reviewed he effec of auocorrelaion on radiional variable conrol chars and oher modified variable conrol chars like Cumulaive Sum Char (CUSUM), Exponeniall Weighed Moving Average (EWMA) conrol char and Mulivariae (T ) conrol char covering he period The, however, did no considered he pas works done b he quali scieniss in he area of auocorrelaed aribue conrol chars. In our presen sud, we shall r o include (as far as possible) mos of he curren research works in hese area, boh for variable (secion A) as well as for aribue conrol chars (secion B). However, considering he fas growing naure of he opic, sudies on auocorrelaion effecs on variable sampling inervals (VSI) conrol chars and non-parameric conrol chars could no be discussed in his secion and i will be repored in a fuure sud. A. Pas Works on Variable Conrol Chars Since 008, man papers have been published b he scholars in suding he effec of auocorrelaion in variable conrol chars and more are offing. Sheu & Lu [3] presens a useful discussion of a mehod ha enables he deecing abili of he EWMA conrol char o be enhanced and shows ha when he observaions are drawn from an AR(1) process wih random error, he EWMA conrol char is far more useful han he Shewhar conrol char in deecing small shifs. The found ha The Generalized Weighed Moving Average (GWMA) conrol char of observaions is shown o be superior o he EWMA conrol char in deecing small shifs in he process mean and variance. The GWMA conrol char of observaions 34
2 Time Series Approaches o Saisical Process Conrol requires less ime o deec small process mean and/or variance shifs as he level of auocorrelaion declines. Keoagile [4] considers he problem of monioring a process in which he observaions can be represened as a firs-order auoregressive model following a heav ailed disribuion. He propose a char based on compuing he conrol limis using he process mean and he sandard error of he leas absolue deviaion for he case when he process quali characerisics follows a heav ailed -disribuion. Chang & Wu [5] developed a general and unified approach based on he use of discreizaion and he finie Markov chain imbedding echnique o invesigae he run lengh properies for various conrol chars when he process observaions are auocorrelaed. Also numerical resuls are presened for illusraive purposes. Suriaka e.al [6] derived an explici formula for he characerisic of EWMA conrol char for rend saionar exponenial AR (1) processes. The compare he resuls for Average Run Lengh (ARL) obained from he explici formula wih values obained from he inegral equaion and found ha he new resuls are simple, eas o programming, which make i aracive o be used in pracice b performers. Karaoglan & Bahan [7] compued ARL performances of conrol chars for peroxide daa from wo baches, for which rend saionar firs order auoregressive (rend AR(1) for shor) model is a represenaive model. B. Auocorrelaed Aribue Conrol Chars To our knowledge lile aenion has been given o he developmen of conrol chars in he case of correlaed aribue daa. A few work in his area are Deligonul and Mergen [8], Bha and Lal [9]. The assumed a wo-sae Markov chain model for auo correlaed aribue daa. Harve and Fernandes [] and Wisnowski and Keas [11] shows ha correlaed coun daa can be modeled wih a EWMA approach.. Simson and Masrangelo [1] sudied he monioring of seriall dependen processes wih aribues daa obained from mulisaions of producion. Lai e al. [13] examined conrol procedures based on he conforming uni run lengh applied o near-zero-defec processes in he presence of serial correlaion. Lai e al. [14] also sudied he problem of process monioring when he process is of high quali and measuremen values possess a cerain serial dependence. Nembhard e.al [15] sudied a demeris conrol chars (U-char) for auocorrelaed daa. Their sud is relaed o injecion-modeling producion lines produced b various models of leak proof plasic conainers. Tang and Cheong [16] proposed a conrol scheme ha is effecive in deecing changes in fracion nonconforming for high ield processes wih correlaion wihin each inspecion group. Shepherd e al. [17] proposed wo conrol char schemes. These conrol chars are based on a sequence of random variables ha are used o classif an iem as conforming or nonconforming under a saionar Markov chain model and 0% sequenial sampling. IV. TIME SERIES MODEL To appl conrol char for residual, we can modeled quali characerisics as follows p p (1) Here, is a p h order auoregressive or AR (p) Process where, and (-1 < <1) are unknown consan and i is normall and independenl disribued wih mean 0 and sandard deviaion. If we modeled 1 1 () hen i is called firs order auoregressive AR (1) model; he observaions from such a model have mean /(1 ), sandard deviaion /(1 ) 1/ and he observaions ha are k periods apar ( ) have correlaion coefficien k k. Suppose ha is an esimae of, obained from analsis of sample daa from he process, and is he fied value of. Then he residuals e are approximael normall and independenl disribued wih mean zero and consan variance. Convenional conrol chars could now be applied o he sequence of residuals. Similarl, he second order auoregressive model AR () will be (3) 1 1. Anoher possibili is o model he dependenc hrough he random componen. A simple wa o do his is 1 (4) This is called a firs-order moving average model. In his model, he correlaion beween and 1 is p and is zero a all oher lags. Thus, he 1 /(1 ) correlaive srucure in onl exends backwards one ime period. Someimes combinaions of auoregressive and moving average erms are useful. A firs order mixed model is 35
3 1 1 Inernaional Journal of Innovaive Research in Compuer Science & Technolog (IJIRCST) ISSN: , Volume-1, Issue-, November- 013 (5) We also encouner he firs-order inegraed moving average model (6) 1 1 in some applicaions. Whereas he previous models are used o describe saionar behavior (ha is wanders around a fixed mean), he model in equaion (6) describes non-saionar behavior (he variable drifs as if here is no fixed value of he process mean). V. CALCULATION OF AUTOCORRELATION The auocorrelaion coefficien for daa ha are k ime period apar r k is defined as r k nk 1 ( )( k ), k 0,1,,3,.. n ( ) 1 where n is he oal number of observaions in he daa se. The sandard error a lag k is sek (7) 1/ n ;k=1 (8) = k1 1/ n(1 ri ) i1 ; k>1 (9) CL z LCL z 3 UCL z 3 [1 (1 ) [1 (1 ) ] ] (11) where he esimae of he process variabili,, picall is esimaed using he same mehod as for he individual conrol char. VII. ANALYSIS To analze he conrol char model in presence of auocorrelaion, we have sudied he finished produc of Formalin from a chemical facor in Assam. I ma be menioned here ha formalin produc of he chemical facor is se as 37 ± 0.5 % weigh of formaldehde gas. If he finished Produc is below 36.5%, he cusomers don accep i. If he finished produc is above 37.5%, i is no affordable o he managemen so far is cos benefi margin is concerned. To analze he daa, we have colleced 68 se of raw daa of formalin and deal wih using saisical process conrol ools. Firs, we calculae he auocorrelaion funcion (ACF) of he formalin (chemical) produc daa which will indicae he presence of he auocorrelaion in he daa. Graph of ACF and PACF are shown in figure 1 and respecivel. VI. EWMA CONTROL CHART Robers inroduced exponeniall weighed moving average char in 1959 [18]. This char is popular for he conrol of indusrial processes where he individual observaions arrive one b one. The EWMA, is compued sequeniall as a linear inerpolaion beween he presen observaion, he previous EWMA 1 ( 1 ) 1 z and z () Auocorrelaion Fig.1. Auocorrelaion Funcion (ACF) of Puri of Chemical Produc (Formalin) Where is a consan 0 1. Huner [19] has shown ha for independen and normall disribued daa, he conrol limis for he EWMA are given b 36
4 Time Series Approaches o Saisical Process Conrol Parial Auocorrelaion Normal Probabili Plo for Residual Mean: SDev: 5.09E Percen Fig.. Parial Auocorrelaion Funcion (PACF) of Puri of Chemical Produc (Formalin) From he visual inspecion of he figure 1, we can easil conclude ha here is auocorrelaion in he original se of daa. Also, from he ACF plo fig 1., i is clear ha he lag(s) is significanl differen from zero and he series is no whie noise i.e he daa has auo correlaion. A. Removing Auocorrelaion from he Observed Daa To achieve an independen, normall disribued daa se, Mongomer [1] recommends modeling he correlaive srucure and conrol charing he residuals direcl. For he formalin daa, he prediced puri of formalin (chemical) produc a period ime is (from he fig 1) (1) Onl four poins from he previous daa were used because of he high auocorrelaion coefficien for lags 1-4 (fig-6.1). To deermine he parameers of his model muliple linear regression can be performed. Using Miniab Sofware, he regression are calculaed which is given parameers 1,, 3 and 4 below. = = =971 3 = =-116 To check he model, we show in figure 3, a normal plo of he residuals, and in figure 4, a plo of he residuals in ime order. Boh plos indicae ha he model fis he daa well. The ACF and he PACF of he residual provide a furher check. Ideall, if he model fis well, all auocorrelaion would have been removed from he daa and he residual behave like whie noise. Figure 5 and 6 show he ACF and he PACF for he residual afer fiing he AR (4) model o he formalin daa. Boh he ACF and he PACF are esseniall zero for all lags Daa Fig. 3. Normal Plo of Residuals afer Fiing an AR (4) Model o he Formalin Daa Residuals Auocorrelaion Index 0 00 Fig. 4. Time Series Plo of he Residuals Fig. 5. The ACF of he Residuals afer Fiing an AR (4) Model o he Formalin Daa Parial Auocorrelaion Fig. 6. The PACF of he Residuals afer Fiing an AR (4) Model To he Formalin Daa 37
5 Inernaional Journal of Innovaive Research in Compuer Science & Technolog (IJIRCST) ISSN: , Volume-1, Issue-, November- 013 B. EWMA Conrol Char for Residual In using he inflaed limis for he individuals conrol char, we emphasized he imporance of reducing he false alarm rae, and making he char eas o inerpre. However, his approach desensiizes he char and will likel increase he average run lengh (ARL) o signal an alarm in case of a real change. For he curren process, we could use an individual s conrol char, a cumulaive sum (CUSUM) char or an EWMA char. The residuals are no on a meaningful scale. Hence he pracical inerpreaion argumen for using he individuals conrol char no longer applies. We herefore sugges using an EWMA char. EWMA Sample Number 0 UCL=39.35 CL=38.57 LCL=37.78 Fig. 7. An EWMA of Observaions from he Original Formalin Daa EWMA Sample Number 0 UCL=0. CL=0 LCL= Fig. 8. An EWMA of Observaions from he Residual Formalin Daa Using equaion (11), he original formalin daa is ploed for EWMA char using Miniab 11 version wih he pical defaul value = 0.. Fig 7 shows an EWMA for original formalin daa se. We see ha he process wih his char appears o be ou of conrol. Bu afer removing he effec of auocorrelaion when we use he EWMA conrol char for he residual formalin daa, he process is found in saisical conrol. (fig.8) VIII. CONCLUSION In modern approach of applicaion of saisical process conrol, he effec of auocorrelaion is increasingl becoming a fac of life and mus no be ignored. In our sud, we have ried o explain wih a chemical daa how o deec auocorrelaion; illusraed i s consequences for sandard conrol char EWMA char. Oher conrol char can be used. As demonsraed, modern sofware packages such as MINITAB, make i relaivel eas o perform he compuaions needed when dealing wih auocorrelaed processes and using AR ime series models. REFERENCES [1] Mongomer, D.C., Inroducion o Saisical Quali Conrol, 6 h Ediion. John Wile and Sons-New York.,009 [] Dua, H.Nr. & Phukan, A, Performance of Some Conrol Char in Presence of Auocorrelaion: A Review and Surve of Lieraure, Assam Saisical Review, 008,, 1-, [3] Sheu, S, S. Lu., The effec of auocorrelaed observaions on a GWMA conrol char performance, Inernaional Journal of Quali & Reliabili Managemen., 009, Vol. 6 Iss: pp [4] Keoagile, T., Conrol Char for Auocorrelaed Processes wih Heav Tailed Disribuions. Economic Quali Conrol., 0, Volume 3, Issue, Pages , ISSN (Online) [5] ] Chang, Y.M., & T. Wu, On Average Run Lenghs of Conrol Chars for Auocorrelaed Processes, Mehodol Compu Appl Probab, 011,13: [6] Suriaka, W, Y. Areepong, S. Sukparungsee and G. Miielu, An Analical Approach o EWMA Conrol Char for Trend Saionar Exponenial AR (1)Processes, Proceedings of he World Congress on Engineering, 01, Vol. IJul 4 6 [7] Karaoglan, A.D and G.M. Bahan., ARL performance of residual conrol chars for rend AR (1) process: A case sud on peroxide values of sored vegeable oil. Scienific Research and Essas, 01, Vol. 7(13), pp , [8] Deligonul and Mergen, Dependence bias in convenional p-chars and is correcion wih an appropriae lo quali disribuion, Jrnl. Of Applied Saisics, (1), [9] Bha, U.N. and Lal, R., Aribue Conrol Chars for Markov Dependen Producion Processes, IIE Transacions, (), 1990, [] Harve, A.C. and Fernandes, C., Time Series Modeling for coun or Correlaed Observaions, Jrnl. Of Business and economic Saisics, 1989,7(4), 7-4. [11] Wisnowski, J. W. and Keas, J. B., Monioring he availabili of asses wih binomial and correlaed observaions, Quali Engineering, 1999,11(3), [1] Simson, W.A. and Masrangelo, C.M., Monioring Serialldependen Processes wih Aribue Daa JQT,1996, 8(3), [13] Lai, C.D., Govindaraju, K. and ie, M., Effecs of Correlaion on Fracion non conforming Saisical Process Conrol Procedures, Jrnl. Of Applied Saisics, 1998, 5(4), [14] Lai, C.D., Govindaraju, K. and ie, M., Sud of Markov Model for a high quali dependen process, Jrnl. Of Applied Saisics, 000, 7(4), [15] Nembhard D.A., A Demeris Conrol Char for Auocorrelaed daa Quali Engineering, 000, 13(), [16] Tang, L.C. and Cheong, W.-T., A conrol scheme for high-ield correlaed Producion under group inspecion, Journal of Quali Technolog, 006, 38(1), [17] Shepherd, D.K., Champ, C.W., Rigdon, S.E. and Fuller, H.T. (006):Aribue Chars for monioring a dependen process, Quali and Reliabili Engineering Inernaional [18] Robers, S.W., Conrol Char Tes based on Geomeric Moving Averages. Technomerices, 1959, 1. [19] Huner, J.S., The Exponeniall Weighed Moving Average, Journal of Quali Technolog,1998, 18 38
4452 Mathematical Modeling Lecture 17: Modeling of Data: Linear Regression
Mah Modeling Lecure 17: Modeling of Daa: Linear Regression Page 1 5 Mahemaical Modeling Lecure 17: Modeling of Daa: Linear Regression Inroducion In modeling of daa, we are given a se of daa poins, and
More informationRobustness of Memory-Type Charts to Skew Processes
Inernaional Journal of Applied Physics and Mahemaics Robusness of Memory-Type Chars o Skew Processes Saowani Sukparungsee* Deparmen of Applied Saisics, Faculy of Applied Science, King Mongku s Universiy
More informationA Study of Process Capability Analysis on Second-order Autoregressive Processes
A Sudy of Process apabiliy Analysis on Second-order Auoregressive Processes Dja Shin Wang, Business Adminisraion, TransWorld Universiy, Taiwan. E-mail: shin@wu.edu.w Szu hi Ho, Indusrial Engineering and
More informationA Note on Missing Data Effects on the Hausman (1978) Simultaneity Test:
A Noe on Missing Daa Effecs on he Hausman (978) Simulaneiy Tes: Some Mone Carlo Resuls. Dikaios Tserkezos and Konsaninos P. Tsagarakis Deparmen of Economics, Universiy of Cree, Universiy Campus, 7400,
More informationComputer Lab 6. Minitab Project Report. Time Series Plot of x. Year
Compuer Lab Problem. Lengh of Growing Season in England Miniab Projec Repor Time Series Plo of x x 77 8 8 889 Year 98 97 The ime series plo indicaes a consan rend up o abou 9, hen he lengh of growing season
More informationMarket and Information Economics
Marke and Informaion Economics Preliminary Examinaion Deparmen of Agriculural Economics Texas A&M Universiy May 2015 Insrucions: This examinaion consiss of six quesions. You mus answer he firs quesion
More informationEstimating Earnings Trend Using Unobserved Components Framework
Esimaing Earnings Trend Using Unobserved Componens Framework Arabinda Basisha and Alexander Kurov College of Business and Economics, Wes Virginia Universiy December 008 Absrac Regressions using valuaion
More informationARIMA Using ARIMA model to forecasting with production of electrics in Australia
ARIMA Using ARIMA model o forecasing wih producion of elecrics in Ausralia $%&'() #!" /*12"/*3."*+, - Ass.Lecure. Mushaq.T.H Al-Anbar Universi/College of Adminisraion and Economics 4!" #$ ARIMA.!"%&'()**+,-!"
More informationIJRSS Volume 2, Issue 2 ISSN:
A LOGITIC BROWNIAN MOTION WITH A PRICE OF DIVIDEND YIELDING AET D. B. ODUOR ilas N. Onyango _ Absrac: In his paper, we have used he idea of Onyango (2003) he used o develop a logisic equaion used in naural
More informationMissing Data Prediction and Forecasting for Water Quantity Data
2011 Inernaional Conference on Modeling, Simulaion and Conrol ICSIT vol.10 (2011) (2011) IACSIT ress, Singapore Missing Daa redicion and Forecasing for Waer Quaniy Daa rakhar Gupa 1 and R.Srinivasan 2
More informationPredictive Analytics : QM901.1x Prof U Dinesh Kumar, IIMB. All Rights Reserved, Indian Institute of Management Bangalore
Predicive Analyics : QM901.1x All Righs Reserved, Indian Insiue of Managemen Bangalore Predicive Analyics : QM901.1x Those who have knowledge don predic. Those who predic don have knowledge. - Lao Tzu
More informationHomework 5 (with keys)
Homework 5 (wih keys) 2. (Selecing an employmen forecasing model wih he AIC and SIC) Use he AIC and SIC o assess he necessiy and desirabiliy of including rend and seasonal componens in a forecasing model
More informationOn the Impact of Inflation and Exchange Rate on Conditional Stock Market Volatility: A Re-Assessment
MPRA Munich Personal RePEc Archive On he Impac of Inflaion and Exchange Rae on Condiional Sock Marke Volailiy: A Re-Assessmen OlaOluwa S Yaya and Olanrewaju I Shiu Deparmen of Saisics, Universiy of Ibadan,
More informationDocumentation: Philadelphia Fed's Real-Time Data Set for Macroeconomists First-, Second-, and Third-Release Values
Documenaion: Philadelphia Fed's Real-Time Daa Se for Macroeconomiss Firs-, Second-, and Third-Release Values Las Updaed: December 16, 2013 1. Inroducion We documen our compuaional mehods for consrucing
More informationPrediction of Rain-fall flow Time Series using Auto-Regressive Models
Available online a www.pelagiaresearchlibrary.com Advances in Applied Science Research, 2011, 2 (2): 128-133 ISSN: 0976-8610 CODEN (USA): AASRFC Predicion of Rain-fall flow Time Series using Auo-Regressive
More informationBusiness Statistics: A Decision-Making Approach, 6e
Chaper 15 Suden Lecure Noes 15-1 Business Saisics: A Decision-Making Approach 6 h Ediion Chaper 16 Analzing and Forecasing Time-Series Daa Business Saisics: A Decision-Making Approach, 6e 2005 Prenice-Hall,
More information1 Purpose of the paper
Moneary Economics 2 F.C. Bagliano - Sepember 2017 Noes on: F.X. Diebold and C. Li, Forecasing he erm srucure of governmen bond yields, Journal of Economerics, 2006 1 Purpose of he paper The paper presens
More informationVaR and Low Interest Rates
VaR and Low Ineres Raes Presened a he Sevenh Monreal Indusrial Problem Solving Workshop By Louis Doray (U de M) Frédéric Edoukou (U de M) Rim Labdi (HEC Monréal) Zichun Ye (UBC) 20 May 2016 P r e s e n
More informationUSE REAL-LIFE DATA TO MOTIVATE YOUR STUDENTS 1
USE REAL-LIFE DATA TO MOTIVATE YOUR STUDENTS 1 Rober E. Kowalczk and Adam O. Hausknech Universi of Massachuses Darmouh Mahemaics Deparmen, 285 Old Wespor Road, N. Darmouh, MA 2747-23 rkowalczk@umassd.edu
More informationOnline Appendix to: Implementing Supply Routing Optimization in a Make-To-Order Manufacturing Network
Online Appendix o: Implemening Supply Rouing Opimizaion in a Make-To-Order Manufacuring Nework A.1. Forecas Accuracy Sudy. July 29, 2008 Assuming a single locaion and par for now, his sudy can be described
More informationSystemic Risk Illustrated
Sysemic Risk Illusraed Jean-Pierre Fouque Li-Hsien Sun March 2, 22 Absrac We sudy he behavior of diffusions coupled hrough heir drifs in a way ha each componen mean-revers o he mean of he ensemble. In
More informationAn inventory model for Gompertz deteriorating items with time-varying holding cost and price dependent demand
Inernaional Journal of Mahemaics rends and echnology (IJM) Volume 49 Number 3 Sepember 7 An invenory model for Gomperz deerioraing iems wih ime-varying holding cos and price dependen demand Absrac Nurul
More informationFinancial Econometrics Jeffrey R. Russell Midterm Winter 2011
Name Financial Economerics Jeffrey R. Russell Miderm Winer 2011 You have 2 hours o complee he exam. Use can use a calculaor. Try o fi all your work in he space provided. If you find you need more space
More informationKey Formulas. From Larson/Farber Elementary Statistics: Picturing the World, Fifth Edition 2012 Prentice Hall. Standard Score: CHAPTER 3.
Key Formulas From Larson/Farber Elemenary Saisics: Picuring he World, Fifh Ediion 01 Prenice Hall CHAPTER Class Widh = Range of daa Number of classes 1round up o nex convenien number 1Lower class limi
More informationCENTRO DE ESTUDIOS MONETARIOS Y FINANCIEROS T. J. KEHOE MACROECONOMICS I WINTER 2011 PROBLEM SET #6
CENTRO DE ESTUDIOS MONETARIOS Y FINANCIEROS T J KEHOE MACROECONOMICS I WINTER PROBLEM SET #6 This quesion requires you o apply he Hodrick-Presco filer o he ime series for macroeconomic variables for he
More informationUNIVERSITY OF MORATUWA
MA5100 UNIVERSITY OF MORATUWA MSC/POSTGRADUATE DIPLOMA IN FINANCIAL MATHEMATICS 009 MA 5100 INTRODUCTION TO STATISTICS THREE HOURS November 009 Answer FIVE quesions and NO MORE. Quesion 1 (a) A supplier
More informationINSTITUTE OF ACTUARIES OF INDIA
INSTITUTE OF ACTUARIES OF INDIA EXAMINATIONS 9 h November 2010 Subjec CT6 Saisical Mehods Time allowed: Three Hours (10.00 13.00 Hrs.) Toal Marks: 100 INSTRUCTIONS TO THE CANDIDATES 1. Please read he insrucions
More informationSubdivided Research on the Inflation-hedging Ability of Residential Property: A Case of Hong Kong
Subdivided Research on he -hedging Abiliy of Residenial Propery: A Case of Hong Kong Guohua Huang 1, Haili Tu 2, Boyu Liu 3,* 1 Economics and Managemen School of Wuhan Universiy,Economics and Managemen
More informationAdvanced Forecasting Techniques and Models: Time-Series Forecasts
Advanced Forecasing Techniques and Models: Time-Series Forecass Shor Examples Series using Risk Simulaor For more informaion please visi: www.realopionsvaluaion.com or conac us a: admin@realopionsvaluaion.com
More informationReconciling Gross Output TFP Growth with Value Added TFP Growth
Reconciling Gross Oupu TP Growh wih Value Added TP Growh Erwin Diewer Universiy of Briish Columbia and Universiy of New Souh Wales ABSTRACT This aricle obains relaively simple exac expressions ha relae
More informationAn Introduction to Statistical Process Control
An Inroducion o Saisical Process Conrol F. F. Gan Deparmen of Mahemaics Naional Universiy of Singapore Inroducion The basic idea in saisical process conrol (SPC) is o ake random samples of producs from
More informationRobust localization algorithms for an autonomous campus tour guide. Richard Thrapp Christian Westbrook Devika Subramanian.
Robus localizaion algorihms for an auonomous campus our guide Richard Thrapp Chrisian Wesbrook Devika Subramanian Rice Universiy Presened a ICRA 200 Ouline The ask and is echnical challenges The localizaion
More informationVOLATILITY CLUSTERING, NEW HEAVY-TAILED DISTRIBUTION AND THE STOCK MARKET RETURNS IN SOUTH KOREA
64 VOLATILITY CLUSTERING, NEW HEAVY-TAILED DISTRIBUTION AND THE STOCK MARKET RETURNS IN SOUTH KOREA Yoon Hong, PhD, Research Fellow Deparmen of Economics Hanyang Universiy, Souh Korea Ji-chul Lee, PhD,
More information2. Quantity and price measures in macroeconomic statistics 2.1. Long-run deflation? As typical price indexes, Figure 2-1 depicts the GDP deflator,
1 2. Quaniy and price measures in macroeconomic saisics 2.1. Long-run deflaion? As ypical price indexes, Figure 2-1 depics he GD deflaor, he Consumer rice ndex (C), and he Corporae Goods rice ndex (CG)
More informationSession 4.2: Price and Volume Measures
Session 4.2: Price and Volume Measures Regional Course on Inegraed Economic Saisics o Suppor 28 SNA Implemenaion Leonidas Akriidis Office for Naional Saisics Unied Kingdom Conen 1. Inroducion 2. Price
More informationComparison of back-testing results for various VaR estimation methods. Aleš Kresta, ICSP 2013, Bergamo 8 th July, 2013
Comparison of back-esing resuls for various VaR esimaion mehods, ICSP 3, Bergamo 8 h July, 3 THE MOTIVATION AND GOAL In order o esimae he risk of financial invesmens, i is crucial for all he models o esimae
More informationDetermination Forecasting Sporadic Demand in Supply Chain Management
07 Published in 5h Inernaional Symposium on Innovaive Technologies in Engineering and Science 9-30 Sepember 07 (ISITES07 Baku - Azerbaijan Deerminaion Forecasing Sporadic Demand in Supply Chain Managemen
More informationForecast Response Variable
Foreca Repone Variable When he value in a repone column are ordered equeniall over ime, i i ofen of inere o foreca heir behavior beond he end of he daa. Thi procedure fi a parameric ARIMA ime erie model
More informationCh. 10 Measuring FX Exposure. Is Exchange Rate Risk Relevant? MNCs Take on FX Risk
Ch. 10 Measuring FX Exposure Topics Exchange Rae Risk: Relevan? Types of Exposure Transacion Exposure Economic Exposure Translaion Exposure Is Exchange Rae Risk Relevan?? Purchasing Power Pariy: Exchange
More informationSession IX: Special topics
Session IX: Special opics 2. Subnaional populaion projecions 10 March 2016 Cheryl Sawyer, Lina Bassarsky Populaion Esimaes and Projecions Secion www.unpopulaion.org Maerials adaped from Unied Naions Naional
More informationForecasting of Intermittent Demand Data in the Case of Medical Apparatus
ISSN: 39-5967 ISO 900:008 Cerified Inernaional Journal of Engineering Science and Innovaive Technology (IJESIT) Volume 3, Issue, March 04 Forecasing of Inermien Demand Daa in he Case of Medical Apparaus
More information(1 + Nominal Yield) = (1 + Real Yield) (1 + Expected Inflation Rate) (1 + Inflation Risk Premium)
5. Inflaion-linked bonds Inflaion is an economic erm ha describes he general rise in prices of goods and services. As prices rise, a uni of money can buy less goods and services. Hence, inflaion is an
More informationData Mining Anomaly Detection. Lecture Notes for Chapter 10. Introduction to Data Mining
Daa Mining Anomaly Deecion Lecure Noes for Chaper 10 Inroducion o Daa Mining by Tan, Seinbach, Kumar Tan,Seinbach, Kumar Inroducion o Daa Mining 4/18/2004 1 Anomaly/Oulier Deecion Wha are anomalies/ouliers?
More informationACE 564 Spring Lecture 9. Violations of Basic Assumptions II: Heteroskedasticity. by Professor Scott H. Irwin
ACE 564 Spring 006 Lecure 9 Violaions of Basic Assumpions II: Heeroskedasiciy by Professor Sco H. Irwin Readings: Griffihs, Hill and Judge. "Heeroskedasic Errors, Chaper 5 in Learning and Pracicing Economerics
More informationData Mining Anomaly Detection. Lecture Notes for Chapter 10. Introduction to Data Mining
Daa Mining Anomaly Deecion Lecure Noes for Chaper 10 Inroducion o Daa Mining by Tan, Seinbach, Kumar Tan,Seinbach, Kumar Inroducion o Daa Mining 4/18/2004 1 Anomaly/Oulier Deecion Wha are anomalies/ouliers?
More information1.2 A CATALOG OF ESSENTIAL FUNCTIONS
SETION. A ATALOG OF ESSENTIAL FUNTIONS. A ATALOG OF ESSENTIAL FUNTIONS V Pla he Video V EXAMPLE A Table liss he average carbon dioide level in he amosphere, measured in pars per million a Mauna Loa Observaor
More informationEffect of Probabilistic Backorder on an Inventory System with Selling Price Demand Under Volume Flexible Strategy
Inernaional Transacions in Mahemaical Sciences and compuers July-December 0, Volume 5, No., pp. 97-04 ISSN-(Prining) 0974-5068, (Online) 0975-75 AACS. (www.aacsjournals.com) All righ reserved. Effec of
More informationFinance Solutions to Problem Set #6: Demand Estimation and Forecasting
Finance 30210 Soluions o Problem Se #6: Demand Esimaion and Forecasing 1) Consider he following regression for Ice Cream sales (in housands) as a funcion of price in dollars per pin. My daa is aken from
More informationEmpirical analysis on China money multiplier
Aug. 2009, Volume 8, No.8 (Serial No.74) Chinese Business Review, ISSN 1537-1506, USA Empirical analysis on China money muliplier SHANG Hua-juan (Financial School, Shanghai Universiy of Finance and Economics,
More informationNon-Stationary Processes: Part IV. ARCH(m) (Autoregressive Conditional Heteroskedasticity) Models
Alber-Ludwigs Universiy Freiburg Deparmen of Economics Time Series Analysis, Summer 29 Dr. Sevap Kesel Non-Saionary Processes: Par IV ARCH(m) (Auoregressive Condiional Heeroskedasiciy) Models Saionary
More informationSan Francisco State University ECON 560 Summer 2018 Problem set 3 Due Monday, July 23
San Francisco Sae Universiy Michael Bar ECON 56 Summer 28 Problem se 3 Due Monday, July 23 Name Assignmen Rules. Homework assignmens mus be yped. For insrucions on how o ype equaions and mah objecs please
More informationMeasuring and Forecasting the Daily Variance Based on High-Frequency Intraday and Electronic Data
Measuring and Forecasing he Daily Variance Based on High-Frequency Inraday and Elecronic Daa Faemeh Behzadnejad Supervisor: Benoi Perron Absrac For he 4-hr foreign exchange marke, Andersen and Bollerslev
More informationModeling and Forecasting by using Time Series ARIMA Models
Inernaional Journal of Engineering Research & Technology (IJERT) ISSN: 78-08 Vol. 4 Issue 03, March-05 Modeling and Forecasing by using Time Series ARIMA Models Musafa M. Ali Alfaki Research Scholar,School
More informationThe Impact of Interest Rate Liberalization Announcement in China on the Market Value of Hong Kong Listed Chinese Commercial Banks
Journal of Finance and Invesmen Analysis, vol. 2, no.3, 203, 35-39 ISSN: 224-0998 (prin version), 224-0996(online) Scienpress Ld, 203 The Impac of Ineres Rae Liberalizaion Announcemen in China on he Marke
More informationComplex exponential Smoothing
Complex exponenial Smoohing Ivan Sveunkov Nikolaos Kourenzes 3 June 24 This maerial has been creaed and coprighed b Lancaser Cenre for Forecasing, Lancaser Universi Managemen School, all righs reserved.
More informationThis specification describes the models that are used to forecast
PCE and CPI Inflaion Differenials: Convering Inflaion Forecass Model Specificaion By Craig S. Hakkio This specificaion describes he models ha are used o forecas he inflaion differenial. The 14 forecass
More informationTransfer Function Approach to Modeling Rice Production in Bangladesh
EUROPEAN ACADEMIC RESEARCH Vol. II, Issue 4/ July 204 ISSN 2286-4822 www.euacademic.org Impac Facor: 3. (UIF) DRJI Value: 5.9 (B+) Transfer Funcion Approach o Modeling Rice Producion in Bangladesh Md.
More informationThe Relationship between Money Demand and Interest Rates: An Empirical Investigation in Sri Lanka
The Relaionship beween Money Demand and Ineres Raes: An Empirical Invesigaion in Sri Lanka R. C. P. Padmasiri 1 and O. G. Dayarana Banda 2 1 Economic Research Uni, Deparmen of Expor Agriculure 2 Deparmen
More informationSTATIONERY REQUIREMENTS SPECIAL REQUIREMENTS 20 Page booklet List of statistical formulae New Cambridge Elementary Statistical Tables
ECONOMICS RIPOS Par I Friday 7 June 005 9 Paper Quaniaive Mehods in Economics his exam comprises four secions. Secions A and B are on Mahemaics; Secions C and D are on Saisics. You should do he appropriae
More informationFinal Exam Answers Exchange Rate Economics
Kiel Insiu für Welwirhschaf Advanced Sudies in Inernaional Economic Policy Research Spring 2005 Menzie D. Chinn Final Exam Answers Exchange Rae Economics This exam is 1 ½ hours long. Answer all quesions.
More informationLIDSTONE IN THE CONTINUOUS CASE by. Ragnar Norberg
LIDSTONE IN THE CONTINUOUS CASE by Ragnar Norberg Absrac A generalized version of he classical Lidsone heorem, which deals wih he dependency of reserves on echnical basis and conrac erms, is proved in
More informationThe Size of Informal Economy in Pakistan
The Size of Informal Economy in Pakisan by Muhammad Farooq Arby Muhammad Jahanzeb Malik Muhammad Nadim Hanif June 2010 Moivaion of he Sudy Various Approaches o Esimae Informal Economy Direc Mehods Indirec
More informationMarket risk VaR historical simulation model with autocorrelation effect: A note
Inernaional Journal of Banking and Finance Volume 6 Issue 2 Aricle 9 3--29 Marke risk VaR hisorical simulaion model wih auocorrelaion effec: A noe Wananee Surapaioolkorn SASIN Chulalunkorn Universiy Follow
More informationBank of Japan Review. Performance of Core Indicators of Japan s Consumer Price Index. November Introduction 2015-E-7
Bank of Japan Review 5-E-7 Performance of Core Indicaors of Japan s Consumer Price Index Moneary Affairs Deparmen Shigenori Shirasuka November 5 The Bank of Japan (BOJ), in conducing moneary policy, employs
More informationA NOVEL MODEL UPDATING METHOD: UPDATING FUNCTION MODEL WITH GROSS DOMESTIC PRODUCT PER CAPITA
1 1 1 1 1 1 1 1 0 1 A NOVEL MODEL UPDATING METHOD: UPDATING FUNCTION MODEL WITH GROSS DOMESTIC PRODUCT PER CAPITA Nobuhiro Graduae School of Business Adminisraion, Kobe Universiy, Japan -1 Rokkodai-cho,
More informationVolume 31, Issue 1. Pitfall of simple permanent income hypothesis model
Volume 31, Issue 1 ifall of simple permanen income hypohesis model Kazuo Masuda Bank of Japan Absrac ermanen Income Hypohesis (hereafer, IH) is one of he cenral conceps in macroeconomics. Single equaion
More informationAn Analysis of Trend and Sources of Deficit Financing in Nepal
Economic Lieraure, Vol. XII (8-16), December 014 An Analysis of Trend and Sources of Defici Financing in Nepal Deo Narayan Suihar ABSTRACT Defici financing has emerged as an imporan ool of financing governmen
More informationHow Well Does the Vasicek-Basel AIRB Model Fit the Data? Evidence from a Long Time Series of Corporate Credit Ratings Data
How Well Does he Vasicek-Basel AIRB Model Fi he Daa? Evidence from a Long ime Series of Corporae Credi Raings Daa by Paul H. Kupiec Preliminary Sepember 2009 EXENDED ABSRAC he Basel II AIRB framework uses
More informationOrganize your work as follows (see book): Chapter 3 Engineering Solutions. 3.4 and 3.5 Problem Presentation
Chaper Engineering Soluions.4 and.5 Problem Presenaion Organize your work as follows (see book): Problem Saemen Theory and Assumpions Soluion Verificaion Tools: Pencil and Paper See Fig.. in Book or use
More informationPricing FX Target Redemption Forward under. Regime Switching Model
In. J. Conemp. Mah. Sciences, Vol. 8, 2013, no. 20, 987-991 HIKARI Ld, www.m-hikari.com hp://dx.doi.org/10.12988/ijcms.2013.311123 Pricing FX Targe Redempion Forward under Regime Swiching Model Ho-Seok
More informationMA Advanced Macro, 2016 (Karl Whelan) 1
MA Advanced Macro, 2016 (Karl Whelan) 1 The Calvo Model of Price Rigidiy The form of price rigidiy faced by he Calvo firm is as follows. Each period, only a random fracion (1 ) of firms are able o rese
More informationWeb Usage Patterns Using Association Rules and Markov Chains
Web Usage Paerns Using Associaion Rules and Markov hains handrakasem Rajabha Universiy, Thailand amnas.cru@gmail.com Absrac - The objecive of his research is o illusrae he probabiliy of web page using
More informationEarnings Quality, Risk-taking and Firm Value: Evidence from Taiwan
DOI: 10.7763/IPEDR. 2012. V50. 24 Earnings Qualy, Risk-aking and Firm Value: Evidence from Taiwan Lu, Chia-Wu 1+ 1 Deparmen of Finance & Cooperaive Managemen, Naional Taipei Universy, Taiwan Absrac. This
More informationAppendix B: DETAILS ABOUT THE SIMULATION MODEL. contained in lookup tables that are all calculated on an auxiliary spreadsheet.
Appendix B: DETAILS ABOUT THE SIMULATION MODEL The simulaion model is carried ou on one spreadshee and has five modules, four of which are conained in lookup ables ha are all calculaed on an auxiliary
More informationThe Mathematics Of Stock Option Valuation - Part Four Deriving The Black-Scholes Model Via Partial Differential Equations
The Mahemaics Of Sock Opion Valuaion - Par Four Deriving The Black-Scholes Model Via Parial Differenial Equaions Gary Schurman, MBE, CFA Ocober 1 In Par One we explained why valuing a call opion as a sand-alone
More informationUse of Inspections as a Risk Mitigation Tool. Dr. Lawrence E. Day - CSQA, PMP
Use of Inspecions as a Risk Miigaion Tool Dr. Lawrence. Day - CSQA, PMP Agenda AT and Inspecion Overview AT S/W Applicaion Inspecion Lessons Learned Inspecion Manufacuring Process Summary 2 Inspecion Overview
More informationEVA NOPAT Capital charges ( = WACC * Invested Capital) = EVA [1 P] each
VBM Soluion skech SS 2012: Noe: This is a soluion skech, no a complee soluion. Disribuion of poins is no binding for he correcor. 1 EVA, free cash flow, and financial raios (45) 1.1 EVA wihou adjusmens
More informationOption Valuation of Oil & Gas E&P Projects by Futures Term Structure Approach. Hidetaka (Hugh) Nakaoka
Opion Valuaion of Oil & Gas E&P Projecs by Fuures Term Srucure Approach March 9, 2007 Hideaka (Hugh) Nakaoka Former CIO & CCO of Iochu Oil Exploraion Co., Ld. Universiy of Tsukuba 1 Overview 1. Inroducion
More informationVolatility Spillovers between Stock Market Returns and Exchange Rate Changes: the New Zealand Case
Volailiy Spillovers beween Sock Marke eurns and Exchange ae Changes: he New Zealand Case Choi, D.F.S., V. Fang and T.Y. Fu Deparmen of Finance, Waikao Managemen School, Universiy of Waikao, Hamilon, New
More informationStylized fact: high cyclical correlation of monetary aggregates and output
SIMPLE DSGE MODELS OF MONEY PART II SEPTEMBER 27, 2011 Inroducion BUSINESS CYCLE IMPLICATIONS OF MONEY Sylized fac: high cyclical correlaion of moneary aggregaes and oupu Convenional Keynesian view: nominal
More informationImportance of the macroeconomic variables for variance. prediction: A GARCH-MIDAS approach
Imporance of he macroeconomic variables for variance predicion: A GARCH-MIDAS approach Hossein Asgharian * : Deparmen of Economics, Lund Universiy Ai Jun Hou: Deparmen of Business and Economics, Souhern
More informationR e. Y R, X R, u e, and. Use the attached excel spreadsheets to
HW # Saisical Financial Modeling ( P Theodossiou) 1 The following are annual reurns for US finance socks (F) and he S&P500 socks index (M) Year Reurn Finance Socks Reurn S&P500 Year Reurn Finance Socks
More informationCapital Controls and Interest Rate Parity
Capial Conrols and Ineres Rae Pariy Evidences from China, 1999-2004 LIU Li-Gang & Ichiro Oani Recen Discussions on Capial Conrols During and afer he Asian Financial Crises Example. Malaysia Impossible
More informationA Method for Estimating the Change in Terminal Value Required to Increase IRR
A Mehod for Esimaing he Change in Terminal Value Required o Increase IRR Ausin M. Long, III, MPA, CPA, JD * Alignmen Capial Group 11940 Jollyville Road Suie 330-N Ausin, TX 78759 512-506-8299 (Phone) 512-996-0970
More information8/17/2015. Lisa M. Grantland Product Manager, Epicor
Lisa M. Granland Produc Manager, Epicor 1 2 Release 879 Enhancemen UFO Enhancemen Commiee Addiions and Fixes in 900.13 Addiional forecasing ools Updae Demand unchanged Deermining Seasonaliy Paern 3 New
More informationAn Alternative Test of Purchasing Power Parity
An Alernaive Tes of Purchasing Power Pariy Frederic H. Wallace* Deparmen of Managemen and Mareing Prairie View A&M Universiy Prairie View, Texas 77446 and Gary L. Shelley Deparmen of Economics, Finance,
More informationSTABLE BOOK-TAX DIFFERENCES, PRIOR EARNINGS, AND EARNINGS PERSISTENCE. Joshua C. Racca. Dissertation Prepared for Degree of DOCTOR OF PHILOSOPHY
STABLE BOOK-TAX DIFFERENCES, PRIOR EARNINGS, AND EARNINGS PERSISTENCE Joshua C. Racca Disseraion Prepared for Degree of DOCTOR OF PHILOSOPHY UNIVERSITY OF NORTH TEXAS Augus 0 APPROVED: Teresa Conover,
More informationProblem Set 1 Answers. a. The computer is a final good produced and sold in Hence, 2006 GDP increases by $2,000.
Social Analysis 10 Spring 2006 Problem Se 1 Answers Quesion 1 a. The compuer is a final good produced and sold in 2006. Hence, 2006 GDP increases by $2,000. b. The bread is a final good sold in 2006. 2006
More informationFrequency Analysis for Non stationary Flood Series
Frequency Analysis for Non saionary Flood Series Prepared By: Narendra Kumar Goel, Sunil Poudel and R.B. Jigajinni Indian Insiue of Technology, Roorkee goelhy@gmail.com Presened By: Sunil Poudel INTRODUCTION
More informationCHAPTER CHAPTER26. Fiscal Policy: A Summing Up. Prepared by: Fernando Quijano and Yvonn Quijano
Fiscal Policy: A Summing Up Prepared by: Fernando Quijano and vonn Quijano CHAPTER CHAPTER26 2006 Prenice Hall usiness Publishing Macroeconomics, 4/e Olivier lanchard Chaper 26: Fiscal Policy: A Summing
More informationForecasting general insurance loss reserves in Egypt
African Journal of Business Managemen Vol. 5(22), pp. 8961-8970, 30 Sepember, 2011 Available online a hp://www.academicjournals.org/ajbm DOI: 10.5897/AJBM11.582 ISSN 1993-8233 2011 Academic Journals Full
More informationOutput: The Demand for Goods and Services
IN CHAPTER 15 how o incorporae dynamics ino he AD-AS model we previously sudied how o use he dynamic AD-AS model o illusrae long-run economic growh how o use he dynamic AD-AS model o race ou he effecs
More informationDYNAMIC ECONOMETRIC MODELS Vol. 7 Nicolaus Copernicus University Toruń Krzysztof Jajuga Wrocław University of Economics
DYNAMIC ECONOMETRIC MODELS Vol. 7 Nicolaus Copernicus Universiy Toruń 2006 Krzyszof Jajuga Wrocław Universiy of Economics Ineres Rae Modeling and Tools of Financial Economerics 1. Financial Economerics
More informationThe relation between U.S. money growth and inflation: evidence from a band pass filter. Abstract
The relaion beween U.S. money growh and inflaion: evidence from a band pass filer Gary Shelley Dep. of Economics Finance; Eas Tennessee Sae Universiy Frederick Wallace Dep. of Managemen Markeing; Prairie
More informationDATA FORECASTING USING SUPERVISED LEARNING
Inernaional Journal of Pure and Applied Mahemaics Volume 115 No. 8 2017, 9-14 ISSN: 1311-8080 (prined version); ISSN: 1314-3395 (on-line version) url: hp://www.ijpam.eu ijpam.eu DATA FORECASTING USING
More informationIs Low Responsiveness of Income Tax Functions to Sectoral Output an Answer to Sri Lanka s Declining Tax Revenue Ratio?
Is Low Responsiveness of Income Tax Funcions o Secoral Oupu an Answer o Sri Lanka s Declining Tax Revenue Raio? P.Y.N. Madhushani and Ananda Jayawickrema Deparmen of Economics and Saisics, Universiy of
More informationInternational transmission of shocks:
Inernaional ransmission of shocks: A ime-varying FAVAR approach o he Open Economy Philip Liu Haroon Mumaz Moneary Analysis Cener for Cenral Banking Sudies Bank of England Bank of England CEF 9 (Sydney)
More informationHEADWAY DISTRIBUTION FOR NH-8 TRAFFIC AT VAGHASI VILLAGE LOCATION
HEADWAY DISTRIBUTION FOR NH-8 TRAFFIC AT VAGHASI VILLAGE LOCATION Dr. L. B. Zala Associae Professor, Civil Engineering Deparmen, lbzala@yahoo.co.in Kevin B. Modi M.Tech (Civil) Transporaion Sysem Engineering
More informationPortfolio Risk of Chinese Stock Market Measured by VaR Method
Vol.53 (ICM 014), pp.6166 hp://dx.doi.org/10.1457/asl.014.53.54 Porfolio Risk of Chinese Sock Marke Measured by VaR Mehod Wu Yudong School of Basic Science,Harbin Universiy of Commerce,Harbin Email:wuyudong@aliyun.com
More informationa. If Y is 1,000, M is 100, and the growth rate of nominal money is 1 percent, what must i and P be?
Problem Se 4 ECN 101 Inermediae Macroeconomics SOLUTIONS Numerical Quesions 1. Assume ha he demand for real money balance (M/P) is M/P = 0.6-100i, where is naional income and i is he nominal ineres rae.
More information