INTRODUCTION TO FORECASTING
|
|
- Laureen Washington
- 8 years ago
- Views:
Transcription
1 INTRODUCTION TO FORECASTING INTRODUCTION: Wha is a forecas? Why do managers need o forecas? A forecas is an esimae of uncerain fuure evens (lierally, o "cas forward" by exrapolaing from pas and curren daa). Forecass are used o improve decision-maing and planning. Even hough forecass are almos always in error, i is beer o have he limied informaion provided by a forecas han o mae decisions in oal ignorance abou he fuure. EXAMPLE: Demand (sales) forecasing -- Firms mus anicipae fuure demand o bes plan how o saisfy i hrough on-hand invenory or available capaciy. The producion or procuremen lead ime ofen requires producion and ordering decisions o be made before demand occurs. FORECASTING DECISION-MAKING ENVIRONMENTS: shor erm (0-6 monhs) < > long erm (2+ years) operaional decisions sraegic decisions frequenly made infrequenly made low level of responsibiliy op managemen level individual iems produc line /\ /\ /\ weely demand annual (monhly) demand 10-year demand (invenory conrol) (producion planning) (faciliy planning) QUALITATIVE VS. QUANTITATIVE METHODS Qualiaive forecasing echniques (soliciing opinions): - subjecive, based on he opinion and judgmen of consumers, expers, managers, salespersons - appropriae when pas daa are unavailable (new produc) or when pas daa are no reliable predicors of he fuure - usually applied o inermediae -- long range decisions Quaniaive forecasing echniques: - explici mahemaical models are used o esimae fuure demand as a funcion of pas daa - appropriae when pas daa are available and also are reliable predicors of he fuure - usually applied o shor -- inermediae range decisions
2 QUALITATIVE FORECASTING METHODS 1. Informed opinion and judgmen: - subjecive opinion of one or more individuals - accuracy of he forecas depends on he individuals - EXAMPLE: ("grass roos") collecion and aggregaion of individual sales forecass o obain overall sales forecas by produc or region 2. Delphi mehod: an ieraive echnique for obaining a consensus forecas from a group of expers, wihou he problems inheren in group decision-maing (he "bandwagon" effec, influenial individuals). The procedure wors as follows: firs, give a se of quesions o each exper, who provides answers (forecass) independenly from he oher expers. The responses are colleced and numeric responses are saisically summarized. If a consensus was no obained, reurn he summarized responses o he expers, along wih any commens made by he expers (anonymously), and have hem revise heir forecass based on his daa. Repea unil eiher a consensus is reached (he answers converge) or else a "salemae" occurs (no convergence can be obained). - EX: long range forecasing of echnological advances 3. Mare research: Quesionnaires and inerviews are used o solici he of poenial cusomers, curren users, and ohers. One poenial problem is ha saed inenions (expecaions) do no always ranslae ino behavior. - EX: voer preferences, new car buyers 4. Hisorical Life-Cycle Analogy: Demand for a new produc can be forecas by anicipaing an S-shaped growh curve similar (analogous) o he S-curve experienced wih relaed producs PRODUCT LIFE CYCLE CURVE Sales per ime mauriy period (unis) decline growh inroducion ime
3 QUANTITATIVE FORECASTING TECHNIQUES TIME SERIES ANALYSIS: - Assumes ha paerns in demand are due o ime - Projecs pas daa paerns ino he fuure (exrapolaes from hisorical demand) Time Series Decomposiion: decompose (brea down) he paern ino level, rend, seasonal, cyclical, and random componens. - he random componen is, by definiion, unpredicable - he cyclical componen is due o long erm (several years) business/economic cycles and hus is very difficul o idenify - ime series mehods usually ry o idenify he seasonal (a cycle ha repeas yearly), rend, and level componens Time Series Mehods: F +1 = demand forecas for period +1 A = acual demand for period 1. Las period demand (ofen called he "naive" forecas) F +1 = A 2. Arihmeic Average: average of all pas demand, o "average ou" or "smooh ou" he random flucuaions F = +1 i=1 Ai = ( A 1+A A ) / 3. Simple Moving Average (N-Period): average of he N mos recen demands, o "smooh ou" he random flucuaions -- he average "moves" o include he mos curren daa (in case demand really isn' fla) F = +1 i=+1-n Ai N Choosing he value of N involves a radeoff beween sabiliy (he abiliy o mainain consisency and no be influenced by random flucuaions) and responsiveness (he abiliy o adjus quicly o rue changes in he demand level): - large N means he average is sable bu less responsive o changes in demand - small N means he average is less sable bu will respond more quicly o changes in demand
4 4. Weighed Moving Average (N-Period): - The weighs are chosen so ha hey sum o 1 - If all weighs are equal (W i = 1/N for all i), he weighed moving average is equivalen o he simple moving average. +1 N F = W A i=1 i -i+1 - Advanage: can vary weighs o emphasize more recen daa - Disadvanage: o change responsiveness, mus change weighs individually; requires recording or soring N weighs and N pas demands 5. Simple Exponenial Smoohing: a simple way of calculaing a weighed moving average forecas wih exponenially-declining weighs; only he previous forecas, mos recen demand, F +1 = A + (1- )F, where 0 1 and he value of a smoohing consan are needed o calculae he new forecas. Anoher way of wriing he equaion clearly shows ha he new forecas is equal o he old forecas plus an adjusmen, where he adjusmen is calculaed as he smoohing consan imes F +1 = F + ( A - F ) he previous forecas error: The value of he smoohing consan, α, deermines how much of an adjusmen or correcion will be made in response o he mos recen demand. Large α means a larger adjusmen (more weigh given o recen daa, giving less sable and more responsive forecass), while small α means a smaller adjusmen (more weigh given o older daa, giving more sable and less responsive forecass). An equivalence (his only means he forecass will be similar, no idenical) beween an N-period simple moving average and an exponenially-smoohed average is obained by seing = 2/(N+1). The exponenial smoohing procedure yields a weighed moving average wih exponenially-declining weighs and an "infinie" number of erms (all pas demand daa bac o ime =1 is given a leas some weigh). The weighs given o he individual demands can be calculaed using he following formula (W 1 is he weigh given o he curren period's demand, W = (1- ) W 2 is he weigh given o he nex mos recen demand, and so on): -1
5 Example: =.1, F 1 = 100, A 1 = 105 Wha happens wih he exreme values of? Wih simple exponenial smoohing, he forecas for any period in he fuure (for example, he ph period beyond he curren period) is he same value: F +p = F +1, for p > 1. (I is liely ha his forecas will decline in accuracy he furher ino he fuure ha i is projeced.) 6.Calculaing Muliplicaive Seasonal Indexes: 1. collec monhly (quarerly) demand daa for several pas years 2. for each monh (quarer) of pas daa, calculae he raio of demand o a 4-quarer (12-monh) moving average 3. average he raios for several years of a given quarer (monh) o ge he seasonal index for ha quarer (monh) Example: Demand (000's of unis) Q Q Q Q
6 EVALUATING FORECAST QUALITY Forecas error, or a relaed performance measure, can be used o selec a forecasing mehod ha has he smalles forecas error, and o monior he performance of a forecasing mehod in use. Error is he difference beween acual demand and forecas demand: error = A - F. The cumulaive error is ofen called he running sum of forecas errors (RSFE). The mean forecas error (MFE), ofen called average cumulaive error or bias, measures he endency of a forecasing model o consisenly overforecas or underforecas. RSFE = ( A - F ), MFE = BIAS = ( A - F ) / =1 If bias > 0, forecass consisenly are oo low, and If bias < 0, forecass consisenly are oo high. Ideally, bias will be close o zero. The primary drawbac o using bias alone o evaluae forecas qualiy is ha posiive and negaive errors end o cancel. A relaed performance measure ha does no have his problem is he mean absolue deviaion (MAD): The MAD is relaed o he sandard deviaion σ in ha for normally-disribued forecas errors, σ 1.25MAD. A hird forecasing performance measure is mean squared error (MSE): MAD = A - F / =1 MSE = ( A - F ) / =1 MSE has he same advanage over bias ha MAD has, namely, posiive and negaive errors do no cancel each oher ou. The difference beween MSE and MAD is ha MSE penalizes large errors much more han MAD does. To selec a forecasing mehod from several poenial models, one approach is o ae a series of acual hisorical daa (demand) and apply each model o he daa. The mehod ha yields he smalles MAD or MSE and has bias close o zero usually is he preferred mehod. To monior he performance of a forecasing model in use, a racing signal is ofen used: TS = RSFE MAD 2 =1 Ideally, he racing signal will be close o zero, bu values wihin a specified range (for example, -4 TS 4) are considered accepable. If he racing signal falls ouside of he accepable range (his may occur if he underlying demand paern has changed sharply), sop and rese he forecas. Some researchers feel ha simple exponenial smoohing wih racing signal conrol is beer han rend-adjused exponenial smoohing.
7 CAUSAL FORECASTING Causal forecasing is appropriae when here is a "cause and effec" relaionship beween one or more independen variables (he "cause") and a dependen variable (he "effec") such as demand or some oher variable ha is being forecas. Causal models have he poenial o predic urning poins in he demand funcion, somehing ha ime series models can no do. (Why?) The general approach o causal forecasing is: 1. collec hisorical daa 2. develop and validae he model 3. use he model o forecas Muliple regression analysis is one approach used o develop a causal forecasing model. I is imporan o noe ha regression implies dependence and no necessarily causaion, however, causaion does no have o be proven for a causal forecasing model o be used effecively. The general form for a muliple linear regression equaion is: Y c = a + b 1 X 1 + b 2 X b X Y c = calculaed (prediced) value of he dependen variable a = inercep (consan erm) X j = jh independen (predicor) variable b j = coefficien associaed wih he jh independen variable A compuer (or programmable calculaor) is used for calculaing he inercep (a) and slope (b) coefficiens. The poin (single value) forecas made wih his model is he value of Y c afer curren values for he X j 's have been insered. Before a causal forecasing model is used i mus be validaed. This means o chec wheher he model conains only variables ha significanly help mae an accurae forecas. Following he "principle of parsimony", he simples model (he one having he fewes variables) ha gives good resuls should be seleced. Larger models, wih more variables, will have a smaller bias componen (good) bu also resul in larger forecas variance (bad). Some facors ha help in validaing a causal model include: 1. R-SQUARE (r 2, he Coefficien of Deerminaion) measures he percenage of variaion in he daa ha is explained by he model. R-SQUARE can ae any value beween zero and one. Ideally, R-SQUARE will be close o one. Alhough a large R-SQUARE value is desirable, he model wih he larges R-SQUARE may no be he bes model. Each variable included in he model will conribue o R-SQUARE, so he model ha conains all variables being considered will have he larges R-SQUARE of any model. However, if some variables do no significanly conribue o he model, i is beer o drop hem from he model and selec a model wih a
8 slighly smaller R-SQUARE. 2. ADJUSTED R-SQUARE is a variaion of R-SQUARE ha penalizes for overfiing he model (including oo many variables), and herefore is useful for geing a feel for how many variables should be included. 3. To es he significance of each independen variable, eiher a -es or an F-es (or boh) should be performed. For our purposes, he -saisic will be sufficien evidence of he significance (or lac hereof) of a variable. The -es for a given variable uses he null hypohesis ha he coefficien of ha variable is equal o zero. A large -value (small PROB value) indicaes ha he null hypohesis should be rejeced, and hus he b i coefficien is liely o be non-zero and he variable should be included in he model. Conversely, a small -value (large PROB value) implies ha he null hypohesis should be acceped, and herefore he b i coefficien is no significanly differen from zero. In his laer case, he variable should no be included in he model. A general guideline for selecing and validaing a causal forecasing model is o (1) hin of all variables ha may help predic he iem being forecas and (2) collec hisorical daa for several observaions, where each observaion conains he value of he iem being forecas (for example, sales) as well as he values of he predicor variables (for example, he prime ineres rae) a he same poin in ime. Nex, he facors described above (R-SQUARE, ADJUSTED R-SQUARE, and he -ess) can be used o (3) examine each model in deail and deermine which variables should remain in he final model.
9 An Example of Causal Forecasing Using Muliple Regression Analysis The manager of a real esae firm in a large meropolian area wans o esablish a model for forecasing he mare value of residenial propery. She believes ha such a model would enable her firm o decrease he lengh of ime ha a clien's house remains on he mare, since he asing price would be more in line wih rue mare values. (1) The manager hins ha he mare value of a house may be influenced by four facors: he size of he house, he disance ha he house is from he business disric, he condiion of he house, and he size of he lo. (2) A random sample of 10 houses sold wihin he pas wo monhs generaed he following daa: [MV] [SQFT] [DIST] [COND] [LOTSIZE] Mare Value Size of House Disance from Condiion Size of Lo (selling price) (in square fee) Business Dis. of House (in acres) (in miles) (0-10) $ 50,000 1, $ 90,000 2, $ 72,000 3, $ 42,000 1, $ 120,000 3, $ 75,000 2, $ 35,000 1, $ 75,000 2, $ 60,000 1, $ 100,000 3,
Chapter 8 Student Lecture Notes 8-1
Chaper Suden Lecure Noes - Chaper Goals QM: Business Saisics Chaper Analyzing and Forecasing -Series Daa Afer compleing his chaper, you should be able o: Idenify he componens presen in a ime series Develop
More informationChapter 8: Regression with Lagged Explanatory Variables
Chaper 8: Regression wih Lagged Explanaory Variables Time series daa: Y for =1,..,T End goal: Regression model relaing a dependen variable o explanaory variables. Wih ime series new issues arise: 1. One
More informationForecasting, Ordering and Stock- Holding for Erratic Demand
ISF 2002 23 rd o 26 h June 2002 Forecasing, Ordering and Sock- Holding for Erraic Demand Andrew Eaves Lancaser Universiy / Andalus Soluions Limied Inroducion Erraic and slow-moving demand Demand classificaion
More informationMACROECONOMIC FORECASTS AT THE MOF A LOOK INTO THE REAR VIEW MIRROR
MACROECONOMIC FORECASTS AT THE MOF A LOOK INTO THE REAR VIEW MIRROR The firs experimenal publicaion, which summarised pas and expeced fuure developmen of basic economic indicaors, was published by he Minisry
More informationThe naive method discussed in Lecture 1 uses the most recent observations to forecast future values. That is, Y ˆ t + 1
Business Condiions & Forecasing Exponenial Smoohing LECTURE 2 MOVING AVERAGES AND EXPONENTIAL SMOOTHING OVERVIEW This lecure inroduces ime-series smoohing forecasing mehods. Various models are discussed,
More informationANALYSIS FOR FINDING AN EFFICIENT SALES FORECASTING METHOD IN THE PROCESS OF PRODUCTION PLANNING, OPERATION AND OTHER AREAS OF DECISION MAKING
Inernaional Journal of Mechanical and Producion Engineering Research and Developmen (IJMPERD ) Vol.1, Issue 2 Dec 2011 1-36 TJPRC Pv. Ld., ANALYSIS FOR FINDING AN EFFICIENT SALES FORECASTING METHOD IN
More informationUsefulness of the Forward Curve in Forecasting Oil Prices
Usefulness of he Forward Curve in Forecasing Oil Prices Akira Yanagisawa Leader Energy Demand, Supply and Forecas Analysis Group The Energy Daa and Modelling Cener Summary When people analyse oil prices,
More informationDuration and Convexity ( ) 20 = Bond B has a maturity of 5 years and also has a required rate of return of 10%. Its price is $613.
Graduae School of Business Adminisraion Universiy of Virginia UVA-F-38 Duraion and Convexiy he price of a bond is a funcion of he promised paymens and he marke required rae of reurn. Since he promised
More informationForecasting. Including an Introduction to Forecasting using the SAP R/3 System
Forecasing Including an Inroducion o Forecasing using he SAP R/3 Sysem by James D. Blocher Vincen A. Maber Ashok K. Soni Munirpallam A. Venkaaramanan Indiana Universiy Kelley School of Business February
More informationChapter 6: Business Valuation (Income Approach)
Chaper 6: Business Valuaion (Income Approach) Cash flow deerminaion is one of he mos criical elemens o a business valuaion. Everyhing may be secondary. If cash flow is high, hen he value is high; if he
More informationSEASONAL ADJUSTMENT. 1 Introduction. 2 Methodology. 3 X-11-ARIMA and X-12-ARIMA Methods
SEASONAL ADJUSTMENT 1 Inroducion 2 Mehodology 2.1 Time Series and Is Componens 2.1.1 Seasonaliy 2.1.2 Trend-Cycle 2.1.3 Irregulariy 2.1.4 Trading Day and Fesival Effecs 3 X-11-ARIMA and X-12-ARIMA Mehods
More informationCointegration: The Engle and Granger approach
Coinegraion: The Engle and Granger approach Inroducion Generally one would find mos of he economic variables o be non-saionary I(1) variables. Hence, any equilibrium heories ha involve hese variables require
More informationMorningstar Investor Return
Morningsar Invesor Reurn Morningsar Mehodology Paper Augus 31, 2010 2010 Morningsar, Inc. All righs reserved. The informaion in his documen is he propery of Morningsar, Inc. Reproducion or ranscripion
More informationCLASSICAL TIME SERIES DECOMPOSITION
Time Series Lecure Noes, MSc in Operaional Research Lecure CLASSICAL TIME SERIES DECOMPOSITION Inroducion We menioned in lecure ha afer we calculaed he rend, everyhing else ha remained (according o ha
More informationChapter 7. Response of First-Order RL and RC Circuits
Chaper 7. esponse of Firs-Order L and C Circuis 7.1. The Naural esponse of an L Circui 7.2. The Naural esponse of an C Circui 7.3. The ep esponse of L and C Circuis 7.4. A General oluion for ep and Naural
More informationVector Autoregressions (VARs): Operational Perspectives
Vecor Auoregressions (VARs): Operaional Perspecives Primary Source: Sock, James H., and Mark W. Wason, Vecor Auoregressions, Journal of Economic Perspecives, Vol. 15 No. 4 (Fall 2001), 101-115. Macroeconomericians
More informationPlanning Demand and Supply in a Supply Chain. Forecasting and Aggregate Planning
Planning Demand and Supply in a Supply Chain Forecasing and Aggregae Planning 1 Learning Objecives Overview of forecasing Forecas errors Aggregae planning in he supply chain Managing demand Managing capaciy
More informationCHARGE AND DISCHARGE OF A CAPACITOR
REFERENCES RC Circuis: Elecrical Insrumens: Mos Inroducory Physics exs (e.g. A. Halliday and Resnick, Physics ; M. Sernheim and J. Kane, General Physics.) This Laboraory Manual: Commonly Used Insrumens:
More informationA New Type of Combination Forecasting Method Based on PLS
American Journal of Operaions Research, 2012, 2, 408-416 hp://dx.doi.org/10.4236/ajor.2012.23049 Published Online Sepember 2012 (hp://www.scirp.org/journal/ajor) A New Type of Combinaion Forecasing Mehod
More informationMeasuring macroeconomic volatility Applications to export revenue data, 1970-2005
FONDATION POUR LES ETUDES ET RERS LE DEVELOPPEMENT INTERNATIONAL Measuring macroeconomic volailiy Applicaions o expor revenue daa, 1970-005 by Joël Cariolle Policy brief no. 47 March 01 The FERDI is a
More informationUSE OF EDUCATION TECHNOLOGY IN ENGLISH CLASSES
USE OF EDUCATION TECHNOLOGY IN ENGLISH CLASSES Mehme Nuri GÖMLEKSİZ Absrac Using educaion echnology in classes helps eachers realize a beer and more effecive learning. In his sudy 150 English eachers were
More informationIndividual Health Insurance April 30, 2008 Pages 167-170
Individual Healh Insurance April 30, 2008 Pages 167-170 We have received feedback ha his secion of he e is confusing because some of he defined noaion is inconsisen wih comparable life insurance reserve
More information11/6/2013. Chapter 14: Dynamic AD-AS. Introduction. Introduction. Keeping track of time. The model s elements
Inroducion Chaper 14: Dynamic D-S dynamic model of aggregae and aggregae supply gives us more insigh ino how he economy works in he shor run. I is a simplified version of a DSGE model, used in cuing-edge
More informationHedging with Forwards and Futures
Hedging wih orwards and uures Hedging in mos cases is sraighforward. You plan o buy 10,000 barrels of oil in six monhs and you wish o eliminae he price risk. If you ake he buy-side of a forward/fuures
More informationSmall and Large Trades Around Earnings Announcements: Does Trading Behavior Explain Post-Earnings-Announcement Drift?
Small and Large Trades Around Earnings Announcemens: Does Trading Behavior Explain Pos-Earnings-Announcemen Drif? Devin Shanhikumar * Firs Draf: Ocober, 2002 This Version: Augus 19, 2004 Absrac This paper
More informationForecasting Sales: A Model and Some Evidence from the Retail Industry. Russell Lundholm Sarah McVay Taylor Randall
Forecasing Sales: A odel and Some Evidence from he eail Indusry ussell Lundholm Sarah cvay aylor andall Why forecas financial saemens? Seems obvious, bu wo common criicisms: Who cares, can we can look
More informationA Note on Using the Svensson procedure to estimate the risk free rate in corporate valuation
A Noe on Using he Svensson procedure o esimae he risk free rae in corporae valuaion By Sven Arnold, Alexander Lahmann and Bernhard Schwezler Ocober 2011 1. The risk free ineres rae in corporae valuaion
More informationTime-Series Forecasting Model for Automobile Sales in Thailand
การประช มว ชาการด านการว จ ยด าเน นงานแห งชาต ประจ าป 255 ว นท 24 25 กรกฎาคม พ.ศ. 255 Time-Series Forecasing Model for Auomobile Sales in Thailand Taweesin Apiwaanachai and Jua Pichilamken 2 Absrac Invenory
More informationHotel Room Demand Forecasting via Observed Reservation Information
Proceedings of he Asia Pacific Indusrial Engineering & Managemen Sysems Conference 0 V. Kachivichyanuul, H.T. Luong, and R. Piaaso Eds. Hoel Room Demand Forecasing via Observed Reservaion Informaion aragain
More informationEstimating Time-Varying Equity Risk Premium The Japanese Stock Market 1980-2012
Norhfield Asia Research Seminar Hong Kong, November 19, 2013 Esimaing Time-Varying Equiy Risk Premium The Japanese Sock Marke 1980-2012 Ibboson Associaes Japan Presiden Kasunari Yamaguchi, PhD/CFA/CMA
More informationPrincipal components of stock market dynamics. Methodology and applications in brief (to be updated ) Andrei Bouzaev, bouzaev@ya.
Principal componens of sock marke dynamics Mehodology and applicaions in brief o be updaed Andrei Bouzaev, bouzaev@ya.ru Why principal componens are needed Objecives undersand he evidence of more han one
More informationStability. Coefficients may change over time. Evolution of the economy Policy changes
Sabiliy Coefficiens may change over ime Evoluion of he economy Policy changes Time Varying Parameers y = α + x β + Coefficiens depend on he ime period If he coefficiens vary randomly and are unpredicable,
More informationSPEC model selection algorithm for ARCH models: an options pricing evaluation framework
Applied Financial Economics Leers, 2008, 4, 419 423 SEC model selecion algorihm for ARCH models: an opions pricing evaluaion framework Savros Degiannakis a, * and Evdokia Xekalaki a,b a Deparmen of Saisics,
More informationDEMAND FORECASTING MODELS
DEMAND FORECASTING MODELS Conens E-2. ELECTRIC BILLED SALES AND CUSTOMER COUNTS Sysem-level Model Couny-level Model Easside King Couny-level Model E-6. ELECTRIC PEAK HOUR LOAD FORECASTING Sysem-level Forecas
More informationJournal Of Business & Economics Research September 2005 Volume 3, Number 9
Opion Pricing And Mone Carlo Simulaions George M. Jabbour, (Email: jabbour@gwu.edu), George Washingon Universiy Yi-Kang Liu, (yikang@gwu.edu), George Washingon Universiy ABSTRACT The advanage of Mone Carlo
More informationBALANCE OF PAYMENTS. First quarter 2008. Balance of payments
BALANCE OF PAYMENTS DATE: 2008-05-30 PUBLISHER: Balance of Paymens and Financial Markes (BFM) Lena Finn + 46 8 506 944 09, lena.finn@scb.se Camilla Bergeling +46 8 506 942 06, camilla.bergeling@scb.se
More informationContrarian insider trading and earnings management around seasoned equity offerings; SEOs
Journal of Finance and Accounancy Conrarian insider rading and earnings managemen around seasoned equiy offerings; SEOs ABSTRACT Lorea Baryeh Towson Universiy This sudy aemps o resolve he differences in
More informationCan Individual Investors Use Technical Trading Rules to Beat the Asian Markets?
Can Individual Invesors Use Technical Trading Rules o Bea he Asian Markes? INTRODUCTION In radiional ess of he weak-form of he Efficien Markes Hypohesis, price reurn differences are found o be insufficien
More informationThe Application of Multi Shifts and Break Windows in Employees Scheduling
The Applicaion of Muli Shifs and Brea Windows in Employees Scheduling Evy Herowai Indusrial Engineering Deparmen, Universiy of Surabaya, Indonesia Absrac. One mehod for increasing company s performance
More informationCOMPARISON OF AIR TRAVEL DEMAND FORECASTING METHODS
COMPARISON OF AIR RAVE DEMAND FORECASING MEHODS Ružica Škurla Babić, M.Sc. Ivan Grgurević, B.Eng. Universiy of Zagreb Faculy of ranspor and raffic Sciences Vukelićeva 4, HR- Zagreb, Croaia skurla@fpz.hr,
More informationChapter 4: Exponential and Logarithmic Functions
Chaper 4: Eponenial and Logarihmic Funcions Secion 4.1 Eponenial Funcions... 15 Secion 4. Graphs of Eponenial Funcions... 3 Secion 4.3 Logarihmic Funcions... 4 Secion 4.4 Logarihmic Properies... 53 Secion
More informationPerformance Center Overview. Performance Center Overview 1
Performance Cener Overview Performance Cener Overview 1 ODJFS Performance Cener ce Cener New Performance Cener Model Performance Cener Projec Meeings Performance Cener Execuive Meeings Performance Cener
More informationA Re-examination of the Joint Mortality Functions
Norh merican cuarial Journal Volume 6, Number 1, p.166-170 (2002) Re-eaminaion of he Join Morali Funcions bsrac. Heekung Youn, rkad Shemakin, Edwin Herman Universi of S. Thomas, Sain Paul, MN, US Morali
More informationAppendix D Flexibility Factor/Margin of Choice Desktop Research
Appendix D Flexibiliy Facor/Margin of Choice Deskop Research Cheshire Eas Council Cheshire Eas Employmen Land Review Conens D1 Flexibiliy Facor/Margin of Choice Deskop Research 2 Final Ocober 2012 \\GLOBAL.ARUP.COM\EUROPE\MANCHESTER\JOBS\200000\223489-00\4
More informationMathematics in Pharmacokinetics What and Why (A second attempt to make it clearer)
Mahemaics in Pharmacokineics Wha and Why (A second aemp o make i clearer) We have used equaions for concenraion () as a funcion of ime (). We will coninue o use hese equaions since he plasma concenraions
More informationConsumer sentiment is arguably the
Does Consumer Senimen Predic Regional Consumpion? Thomas A. Garre, Rubén Hernández-Murillo, and Michael T. Owyang This paper ess he abiliy of consumer senimen o predic reail spending a he sae level. The
More informationPROFIT TEST MODELLING IN LIFE ASSURANCE USING SPREADSHEETS PART ONE
Profi Tes Modelling in Life Assurance Using Spreadshees PROFIT TEST MODELLING IN LIFE ASSURANCE USING SPREADSHEETS PART ONE Erik Alm Peer Millingon 2004 Profi Tes Modelling in Life Assurance Using Spreadshees
More informationInductance and Transient Circuits
Chaper H Inducance and Transien Circuis Blinn College - Physics 2426 - Terry Honan As a consequence of Faraday's law a changing curren hrough one coil induces an EMF in anoher coil; his is known as muual
More informationTime Series Analysis Using SAS R Part I The Augmented Dickey-Fuller (ADF) Test
ABSTRACT Time Series Analysis Using SAS R Par I The Augmened Dickey-Fuller (ADF) Tes By Ismail E. Mohamed The purpose of his series of aricles is o discuss SAS programming echniques specifically designed
More informationEconomics Honors Exam 2008 Solutions Question 5
Economics Honors Exam 2008 Soluions Quesion 5 (a) (2 poins) Oupu can be decomposed as Y = C + I + G. And we can solve for i by subsiuing in equaions given in he quesion, Y = C + I + G = c 0 + c Y D + I
More informationTerm Structure of Prices of Asian Options
Term Srucure of Prices of Asian Opions Jirô Akahori, Tsuomu Mikami, Kenji Yasuomi and Teruo Yokoa Dep. of Mahemaical Sciences, Risumeikan Universiy 1-1-1 Nojihigashi, Kusasu, Shiga 525-8577, Japan E-mail:
More informationChapter 1 Overview of Time Series
Chaper 1 Overview of Time Series 1.1 Inroducion 1 1.2 Analysis Mehods and SAS/ETS Sofware 2 1.2.1 Opions 2 1.2.2 How SAS/ETS Sofware Procedures Inerrelae 4 1.3 Simple Models: Regression 6 1.3.1 Linear
More informationINTEREST RATE FUTURES AND THEIR OPTIONS: SOME PRICING APPROACHES
INTEREST RATE FUTURES AND THEIR OPTIONS: SOME PRICING APPROACHES OPENGAMMA QUANTITATIVE RESEARCH Absrac. Exchange-raded ineres rae fuures and heir opions are described. The fuure opions include hose paying
More informationWhy Did the Demand for Cash Decrease Recently in Korea?
Why Did he Demand for Cash Decrease Recenly in Korea? Byoung Hark Yoo Bank of Korea 26. 5 Absrac We explores why cash demand have decreased recenly in Korea. The raio of cash o consumpion fell o 4.7% in
More informationChapter 2 Problems. 3600s = 25m / s d = s t = 25m / s 0.5s = 12.5m. Δx = x(4) x(0) =12m 0m =12m
Chaper 2 Problems 2.1 During a hard sneeze, your eyes migh shu for 0.5s. If you are driving a car a 90km/h during such a sneeze, how far does he car move during ha ime s = 90km 1000m h 1km 1h 3600s = 25m
More informationDYNAMIC MODELS FOR VALUATION OF WRONGFUL DEATH PAYMENTS
DYNAMIC MODELS FOR VALUATION OF WRONGFUL DEATH PAYMENTS Hong Mao, Shanghai Second Polyechnic Universiy Krzyszof M. Osaszewski, Illinois Sae Universiy Youyu Zhang, Fudan Universiy ABSTRACT Liigaion, exper
More informationForecasting and Information Sharing in Supply Chains Under Quasi-ARMA Demand
Forecasing and Informaion Sharing in Supply Chains Under Quasi-ARMA Demand Avi Giloni, Clifford Hurvich, Sridhar Seshadri July 9, 2009 Absrac In his paper, we revisi he problem of demand propagaion in
More informationThe Transport Equation
The Transpor Equaion Consider a fluid, flowing wih velociy, V, in a hin sraigh ube whose cross secion will be denoed by A. Suppose he fluid conains a conaminan whose concenraion a posiion a ime will be
More informationChapter 1.6 Financial Management
Chaper 1.6 Financial Managemen Par I: Objecive ype quesions and answers 1. Simple pay back period is equal o: a) Raio of Firs cos/ne yearly savings b) Raio of Annual gross cash flow/capial cos n c) = (1
More informationThe Effect of Working Capital Management on Reducing the Stock Price Crash Risk(Case Study: Companies Listed in Tehran Stock Exchange)
Inernaional Research Journal of Applied and Basic Sciences 2013 Available online a www.irjabs.com ISSN 2251-838X / Vol, 6 (9): 1222-1228 Science Explorer Publicaions The Effec of Working Capial Managemen
More informationJournal of Business & Economics Research Volume 1, Number 10
Annualized Invenory/Sales Journal of Business & Economics Research Volume 1, Number 1 A Macroeconomic Analysis Of Invenory/Sales Raios William M. Bassin, Shippensburg Universiy Michael T. Marsh (E-mail:
More informationRandom Walk in 1-D. 3 possible paths x vs n. -5 For our random walk, we assume the probabilities p,q do not depend on time (n) - stationary
Random Walk in -D Random walks appear in many cones: diffusion is a random walk process undersanding buffering, waiing imes, queuing more generally he heory of sochasic processes gambling choosing he bes
More informationInventory Management and Demand Prediction System for Reagents and Consumables
Invenory Managemen and Demand Predicion Sysem for Reagens and Consumables Tzu-Chuen Lu, Shih-Chieh Lai, 3 Chun-Ya Tseng *, Firs Auhor, Corresponding Auhor Deparmen of Informaion Managemen, Chaoyang Universiy
More informationSetting Accuracy Targets for. Short-Term Judgemental Sales Forecasting
Seing Accuracy Targes for Shor-Term Judgemenal Sales Forecasing Derek W. Bunn London Business School Sussex Place, Regen s Park London NW1 4SA, UK Tel: +44 (0)171 262 5050 Fax: +44(0)171 724 7875 Email:
More informationARCH 2013.1 Proceedings
Aricle from: ARCH 213.1 Proceedings Augus 1-4, 212 Ghislain Leveille, Emmanuel Hamel A renewal model for medical malpracice Ghislain Léveillé École d acuaria Universié Laval, Québec, Canada 47h ARC Conference
More informationUPDATE OF QUARTERLY NATIONAL ACCOUNTS MANUAL: CONCEPTS, DATA SOURCES AND COMPILATION 1 CHAPTER 7. SEASONAL ADJUSTMENT 2
UPDATE OF QUARTERLY NATIONAL ACCOUNTS MANUAL: CONCEPTS, DATA SOURCES AND COMPILATION 1 CHAPTER 7. SEASONAL ADJUSTMENT 2 Table of Conens 1. Inroducion... 3 2. Main Principles of Seasonal Adjusmen... 6 3.
More informationTEMPORAL PATTERN IDENTIFICATION OF TIME SERIES DATA USING PATTERN WAVELETS AND GENETIC ALGORITHMS
TEMPORAL PATTERN IDENTIFICATION OF TIME SERIES DATA USING PATTERN WAVELETS AND GENETIC ALGORITHMS RICHARD J. POVINELLI AND XIN FENG Deparmen of Elecrical and Compuer Engineering Marquee Universiy, P.O.
More informationWORKING CAPITAL ACCRUALS AND EARNINGS MANAGEMENT 1
Invesmen Managemen and Financial Innovaions, Volume 4, Issue 2, 2007 33 WORKING CAPITAL ACCRUALS AND EARNINGS MANAGEMENT Joseph Kersein *, Aul Rai ** Absrac We reexamine marke reacions o large and small
More informationThe Grantor Retained Annuity Trust (GRAT)
WEALTH ADVISORY Esae Planning Sraegies for closely-held, family businesses The Granor Reained Annuiy Trus (GRAT) An efficien wealh ransfer sraegy, paricularly in a low ineres rae environmen Family business
More informationPredicting Stock Market Index Trading Signals Using Neural Networks
Predicing Sock Marke Index Trading Using Neural Neworks C. D. Tilakarane, S. A. Morris, M. A. Mammadov, C. P. Hurs Cenre for Informaics and Applied Opimizaion School of Informaion Technology and Mahemaical
More informationIdealistic characteristics of Islamic Azad University masters - Islamshahr Branch from Students Perspective
Available online a www.pelagiaresearchlibrary.com European Journal Experimenal Biology, 202, 2 (5):88789 ISSN: 2248 925 CODEN (USA): EJEBAU Idealisic characerisics Islamic Azad Universiy masers Islamshahr
More informationThe Greek financial crisis: growing imbalances and sovereign spreads. Heather D. Gibson, Stephan G. Hall and George S. Tavlas
The Greek financial crisis: growing imbalances and sovereign spreads Heaher D. Gibson, Sephan G. Hall and George S. Tavlas The enry The enry of Greece ino he Eurozone in 2001 produced a dividend in he
More informationFlorida State University Libraries
Florida Sae Universiy Libraries Elecronic Theses, Treaises and Disseraions The Graduae School 2008 Two Essays on he Predicive Abiliy of Implied Volailiy Consanine Diavaopoulos Follow his and addiional
More informationMarkit Excess Return Credit Indices Guide for price based indices
Marki Excess Reurn Credi Indices Guide for price based indices Sepember 2011 Marki Excess Reurn Credi Indices Guide for price based indices Conens Inroducion...3 Index Calculaion Mehodology...4 Semi-annual
More informationII.1. Debt reduction and fiscal multipliers. dbt da dpbal da dg. bal
Quarerly Repor on he Euro Area 3/202 II.. Deb reducion and fiscal mulipliers The deerioraion of public finances in he firs years of he crisis has led mos Member Saes o adop sizeable consolidaion packages.
More informationAppendix A: Area. 1 Find the radius of a circle that has circumference 12 inches.
Appendi A: Area worked-ou s o Odd-Numbered Eercises Do no read hese worked-ou s before aemping o do he eercises ourself. Oherwise ou ma mimic he echniques shown here wihou undersanding he ideas. Bes wa
More informationSupplementary Appendix for Depression Babies: Do Macroeconomic Experiences Affect Risk-Taking?
Supplemenary Appendix for Depression Babies: Do Macroeconomic Experiences Affec Risk-Taking? Ulrike Malmendier UC Berkeley and NBER Sefan Nagel Sanford Universiy and NBER Sepember 2009 A. Deails on SCF
More informationDiane K. Michelson, SAS Institute Inc, Cary, NC Annie Dudley Zangi, SAS Institute Inc, Cary, NC
ABSTRACT Paper DK-02 SPC Daa Visualizaion of Seasonal and Financial Daa Using JMP Diane K. Michelson, SAS Insiue Inc, Cary, NC Annie Dudley Zangi, SAS Insiue Inc, Cary, NC JMP Sofware offers many ypes
More informationHow To Calculate A Person'S Income From A Life Insurance
How Much Life Insurance o You Need? Chris Robinson 1 and Vicoria Zaremba 2 Augus 14, 2012 Absrac We presen formal models of he differen mehods of esimaing a person s required life insurance coverage. The
More informationMaking a Faster Cryptanalytic Time-Memory Trade-Off
Making a Faser Crypanalyic Time-Memory Trade-Off Philippe Oechslin Laboraoire de Securié e de Crypographie (LASEC) Ecole Polyechnique Fédérale de Lausanne Faculé I&C, 1015 Lausanne, Swizerland philippe.oechslin@epfl.ch
More informationInformation Theoretic Evaluation of Change Prediction Models for Large-Scale Software
Informaion Theoreic Evaluaion of Change Predicion Models for Large-Scale Sofware Mina Askari School of Compuer Science Universiy of Waerloo Waerloo, Canada maskari@uwaerloo.ca Ric Hol School of Compuer
More informationImproving timeliness of industrial short-term statistics using time series analysis
Improving imeliness of indusrial shor-erm saisics using ime series analysis Discussion paper 04005 Frank Aelen The views expressed in his paper are hose of he auhors and do no necessarily reflec he policies
More informationCALCULATION OF OMX TALLINN
CALCULATION OF OMX TALLINN CALCULATION OF OMX TALLINN 1. OMX Tallinn index...3 2. Terms in use...3 3. Comuaion rules of OMX Tallinn...3 3.1. Oening, real-ime and closing value of he Index...3 3.2. Index
More informationBid-ask Spread and Order Size in the Foreign Exchange Market: An Empirical Investigation
Bid-ask Spread and Order Size in he Foreign Exchange Marke: An Empirical Invesigaion Liang Ding* Deparmen of Economics, Macaleser College, 1600 Grand Avenue, S. Paul, MN55105, U.S.A. Shor Tile: Bid-ask
More informationPredicting Implied Volatility in the Commodity Futures Options Markets
Predicing Implied Volailiy in he Commodiy Fuures Opions Markes By Sephen Ferris* Deparmen of Finance College of Business Universiy of Missouri - Columbia Columbia, MO 65211 Phone: 573-882-9905 Email: ferris@missouri.edu
More informationProstate Cancer. Options for Localised Cancer
Prosae Cancer Opions for Localised Cancer You or someone you know is considering reamen opions for localised prosae cancer. his leafle is designed o give you a shor overview of he opions available. For
More information4. International Parity Conditions
4. Inernaional ariy ondiions 4.1 urchasing ower ariy he urchasing ower ariy ( heory is one of he early heories of exchange rae deerminaion. his heory is based on he concep ha he demand for a counry's currency
More informationThe Interest Rate Risk of Mortgage Loan Portfolio of Banks
The Ineres Rae Risk of Morgage Loan Porfolio of Banks A Case Sudy of he Hong Kong Marke Jim Wong Hong Kong Moneary Auhoriy Paper presened a he Exper Forum on Advanced Techniques on Sress Tesing: Applicaions
More informationSegmentation, Probability of Default and Basel II Capital Measures. for Credit Card Portfolios
Segmenaion, Probabiliy of Defaul and Basel II Capial Measures for Credi Card Porfolios Draf: Aug 3, 2007 *Work compleed while a Federal Reserve Bank of Philadelphia Dennis Ash Federal Reserve Bank of Philadelphia
More informationOne dictionary: Native language - English/English - native language or English - English
Faculy of Social Sciences School of Business Corporae Finance Examinaion December 03 English Dae: Monday 09 December, 03 Time: 4 hours/ 9:00-3:00 Toal number of pages including he cover page: 5 Toal number
More informationConceptually calculating what a 110 OTM call option should be worth if the present price of the stock is 100...
Normal (Gaussian) Disribuion Probabiliy De ensiy 0.5 0. 0.5 0. 0.05 0. 0.9 0.8 0.7 0.6? 0.5 0.4 0.3 0. 0. 0 3.6 5. 6.8 8.4 0.6 3. 4.8 6.4 8 The Black-Scholes Shl Ml Moel... pricing opions an calculaing
More informationMonetary Policy & Real Estate Investment Trusts *
Moneary Policy & Real Esae Invesmen Truss * Don Bredin, Universiy College Dublin, Gerard O Reilly, Cenral Bank and Financial Services Auhoriy of Ireland & Simon Sevenson, Cass Business School, Ciy Universiy
More informationForecasting Daily Supermarket Sales Using Exponentially Weighted Quantile Regression
Forecasing Daily Supermarke Sales Using Exponenially Weighed Quanile Regression James W. Taylor Saïd Business School Universiy of Oxford European Journal of Operaional Research, 2007, Vol. 178, pp. 154-167.
More informationµ r of the ferrite amounts to 1000...4000. It should be noted that the magnetic length of the + δ
Page 9 Design of Inducors and High Frequency Transformers Inducors sore energy, ransformers ransfer energy. This is he prime difference. The magneic cores are significanly differen for inducors and high
More informationPresent Value Methodology
Presen Value Mehodology Econ 422 Invesmen, Capial & Finance Universiy of Washingon Eric Zivo Las updaed: April 11, 2010 Presen Value Concep Wealh in Fisher Model: W = Y 0 + Y 1 /(1+r) The consumer/producer
More information9. Capacitor and Resistor Circuits
ElecronicsLab9.nb 1 9. Capacior and Resisor Circuis Inroducion hus far we have consider resisors in various combinaions wih a power supply or baery which provide a consan volage source or direc curren
More informationAutomatic measurement and detection of GSM interferences
Auomaic measuremen and deecion of GSM inerferences Poor speech qualiy and dropped calls in GSM neworks may be caused by inerferences as a resul of high raffic load. The radio nework analyzers from Rohde
More informationLong-Run Stock Returns: Participating in the Real Economy
Long-Run Sock Reurns: Paricipaing in he Real Economy Roger G. Ibboson and Peng Chen In he sudy repored here, we esimaed he forward-looking long-erm equiy risk premium by exrapolaing he way i has paricipaed
More informationVALUE BASED FINANCIAL PERFORMANCE MEASURES: AN EVALUATION OF RELATIVE AND INCREMENTAL INFORMATION CONTENT
VALUE BASED FINANCIAL PERFORMANCE MEASURES: AN EVALUATION OF RELATIVE AND INCREMENTAL INFORMATION CONTENT Pierre Erasmus Absrac Value-based (VB) financial performance measures are ofen advanced as improvemens
More information