An empirical analysis about forecasting Tmall air-conditioning sales using time series model Yan Xia
|
|
- Alexina Lang
- 7 years ago
- Views:
Transcription
1 An empirical analysis abou forecasing Tmall air-condiioning sales using ime series model Yan Xia Deparmen of Mahemaics, Ocean Universiy of China, China Absrac Time series model is a hospo in he research of saisics. On November 11, 2015, Tmall plaform s urnover was more han $91.2 billion which caused he aenion of scholars boh a home and abroad. So his paper aims o forecas sales of Tmall, which is helpful o he enerprises. Research mehods are ARIMA model and VAR model. The firs model is single-variable model and he laer is muli-variable model. In he sudy, ARIMA model makes he sequence smooh by using wo difference operaion. In VAR model, five explanaory variables are ransformed ino one main componen. By conras, VAR model does no give deailed accurae predicion, bu ARIMA model does. Therefore, single-variable ime series model is more suiable for sales forecas han muli-variable model. Keywords ARIMA model, VAR model, Sales forecas. I. INTRODUCTION In recen years, he prosperiy of elecronic commerce makes he enerprises pay more and more aenion o markeing sraegy, especially accurae sales forecas. Time series model is ha using hisorical daa relaed o pas behavior o infer he fuure behavior of ime sequence. So his paper aims o do an empirical analysis abou sales forecas using ime series model. Indusry daa such as sales, price, online acive sores, online sores clinching a deal, online producs and producs clinching a deal need collecing. Use MATLAB and EVIEWS sofware o process he daa. The body of his paper includes four pars. The firs par is inroducion. The second par is model definiion abou ARIMA model and VAR model. The hird par is modeling and forecasing sales. The las par is conclusion. Generally, AR(p) formula is as follow: II. MODEL DEFINITION X X X X (1) p p If he error erm is whie noise sequence, hen he process is described as pure AR(p). On he conrary, process as follow: q q is pure MA(q) (2) ARMA model[1] process has he model srucure as follow: X X X X (3) p p q q Auoregressive inegraed moving average model, ARIMA model for shor, is srucure is as follow: d ( B) x ( B) 2 E( ) 0, Var ( ), E( ) 0, s s Ex 0, s s (4) Is basic idea is ha le non-saionary ime series smooh by difference, so ha i can use ARMA model o esablish saionary ime series model. Vecor auoregressive model [2], VAR model for shor, is srucure is as follow: Page 45
2 X AX AX A X U (5) p p A and i X are m dimension marix. T E( UU ) 0, s s U and are m dimension vecor. Le T uu E( U ) 0, E( U U ), and Above wo models, when he reciprocal of characerisic roos are inside he uni circle, hey are smooh. III. MODELING AND FORECAST Monhly daa are used covering he period from January 2010 o December I was provided by a enerprises. 3.1 ARIMA modeling We need o do pure random inspecion. Because only he non-whie noise sequence modeling makes sense. LB[3] es resul is as follows. TABLE 1 LB TEST Delayed order LB saisic P value Table 1 show ha P value is less han he significance level of Therefore, he alernaive hypohesis may be acceped. This sequence is no whie noise sequence. Then le us do saionariy es. ADF es shows P value approximaes 1, so his sequence is non-saionary sequence. Modeling process is as follows: To eliminae seasonal flucuaions, do firs-order difference operaion by sep 12. To eliminae rend changes, do firs-order difference operaion by sep 1. Differenced sequence is smooh. Considering sample size, le p and q change beween 0 o 3. When AIC funcion reaches minimum value, we ge a row vecor including p and q [4]. TABLE 2 AIC FUNCTION VALUE p q AIC p q AIC Table 2 shows ha he srucure of his model should be confirmed as ARMA(2,3). Nex, le us esimae he parameers and forecas sales. Is formula is as follow: X X X (6) Forecas can be seen from Fig.1 Page 46
3 FIG.1 SALES FORECAST OF 2014 USING ARIMA MODEL The average error rae calculaed is , accurae for sales. The residual sequence is whie noise. 3.2 VAR modeling FIG.2 THE RESIDUALS OF ARMA MODEL AND ITS ACF AND PACF Facors ha affec sales are five variables, so i is suiable o use principal componen analysis or PCA [5] for shor o reduce dimensions before esablishing he VAR model. TABLE 3 RESULTS OF PCA Number Principal Componen Conribuion Rae Cumulaive Conribuion Rae 1 z z z z z The conribuion rae of he firs principal componen is above 90%.Is formula as follow: z x x x x x (7) Page 47
4 The VAR model uses wo variables, namely z1 and sales. Firsly, saionariy es shows ha when p 6, ha is, he reciprocal of characerisic roos are inside he uni circle, model is smooh. 1.5 Inverse Roos of AR Characerisic Polynomial 1.5 Inverse Roos of AR Characerisic Polynomial VAR(6) model VAR(7) model FIG.3 THE RECIPROCAL OF CHARACTERISTIC ROOTS DISTRIBUTION ABOUT VAR(6) AND VAR(7) Similar wih ARIMA model, using MATLAB sofware o calculae AIC funcion. The srucure of his model is evenually deermined as VAR (6). Is parameers calculaed can be seen on equaion (8): sal es sal es( 1) sal es( 2) sal es( 3) sal es( 4) sal es( 5) sal es( 6) z1( 1) z1( 2) z1( 3) z1( 4) z1( 5) z1( 6) (8) FIG.4 VAR MODEL FORECAST SALES OF 2014 VAR model can grasp he change of ime series from he macroscopic aspec. Bu is precision is no high. Page 48
5 IV. CONCLUSION As single variable model, ARIMA model esablishes equaion by calculaing ACF and PACF, he residuals included. According o akaike informaion crierion, he model srucure is idenified as ARMA (2,3). I is very precise and credible. Wih regard o muli-variable ime series model, i did principal componen analysis o reduce dimensions. Five explanaory variables are ransformed o one main componen. VAR model has wo variables. On he conrary, predicion accuracy of VAR model is no high. I is suiable for macro-economic analysis. This paper sudies he issues of sales forecas, which belongs o he caegory of microeconomic. I comes o a conclusion ha single-variable ime series model is more suiable for his problem, especially he sochasic ime series models. Alhough we find an ideal ime series model, sales forecas in November may have oo big error. This is a problem of srucural breaks, which need furher research. REFERENCES [1] Shuyuan He. Random process [M]. Peking Universiy Press, [2] Tiemei Gao. Economeric analysis mehod and modeling: applicaion and insance EViews [M]. Beijing: Tsinghua Universiy press, 2006: [3] Jianhong Wu, Lixing Zhu. Mulivariae ime series GARCH ype model of diagnosic es [J]. Journal of mahemaical physics, 2010, 30 (3) : [4] Sifan Yang. Time series modeling and forecasing [D]. Tsinghua universiy, [5] Shoukui Si. Mahemaical modeling algorihm and applicaion [M]. Naional defense indusry press, 2011 Page 49
Usefulness 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 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 informationStock Price Prediction Using the ARIMA Model
2014 UKSim-AMSS 16h Inernaional Conference on Compuer Modelling and Simulaion Sock Price Predicion Using he ARIMA Model 1 Ayodele A. Adebiyi., 2 Aderemi O. Adewumi 1,2 School of Mahemaic, Saisics & Compuer
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 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 informationPurchasing Power Parity (PPP), Sweden before and after EURO times
School of Economics and Managemen Purchasing Power Pariy (PPP), Sweden before and afer EURO imes - Uni Roo Tes - Coinegraion Tes Masers hesis in Saisics - Spring 2008 Auhors: Mansoor, Rashid Smora, Ami
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 informationDOES TRADING VOLUME INFLUENCE GARCH EFFECTS? SOME EVIDENCE FROM THE GREEK MARKET WITH SPECIAL REFERENCE TO BANKING SECTOR
Invesmen Managemen and Financial Innovaions, Volume 4, Issue 3, 7 33 DOES TRADING VOLUME INFLUENCE GARCH EFFECTS? SOME EVIDENCE FROM THE GREEK MARKET WITH SPECIAL REFERENCE TO BANKING SECTOR Ahanasios
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 informationThe Kinetics of the Stock Markets
Asia Pacific Managemen Review (00) 7(1), 1-4 The Kineics of he Sock Markes Hsinan Hsu * and Bin-Juin Lin ** (received July 001; revision received Ocober 001;acceped November 001) This paper applies he
More informationStrictly as per the compliance and regulations of:
Global Journal of Managemen and Business Research Finance Volume 3 Issue 3 Version.0 Year 03 Type: Double Blind Peer Reviewed Inernaional Research Journal Publisher: Global Journals Inc. (USA) Online ISSN:
More informationHow To Write A Demand And Price Model For A Supply Chain
Proc. Schl. ITE Tokai Univ. vol.3,no,,pp.37-4 Vol.,No.,,pp. - Paper Demand and Price Forecasing Models for Sraegic and Planning Decisions in a Supply Chain by Vichuda WATTANARAT *, Phounsakda PHIMPHAVONG
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 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 informationStatistical Analysis with Little s Law. Supplementary Material: More on the Call Center Data. by Song-Hee Kim and Ward Whitt
Saisical Analysis wih Lile s Law Supplemenary Maerial: More on he Call Cener Daa by Song-Hee Kim and Ward Whi Deparmen of Indusrial Engineering and Operaions Research Columbia Universiy, New York, NY 17-99
More informationINTRODUCTION TO FORECASTING
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
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 informationForecasting Electricity Consumption: A Comparison of Models for New Zealand
Paper Tile: Forecasing Elecriciy Consumpion: A Comparison of Models for New Zealand Auhors: Zaid Mohamed and Pa Bodger,* Affiliaions:. Mohamed, Z., B.E (Hons), is a Ph.D. suden in he Deparmen of Elecrical
More informationImprovement in Forecasting Accuracy Using the Hybrid Model of ARFIMA and Feed Forward Neural Network
American Journal of Inelligen Sysems 2012, 2(2): 12-17 DOI: 10.5923/j.ajis.20120202.02 Improvemen in Forecasing Accuracy Using he Hybrid Model of ARFIMA and Feed Forward Neural Nework Cagdas Hakan Aladag
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 informationChapter 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 informationAnalysis of I-Series, An Appraisal and Its Models
Vol. No.2, pp.-, June 203 MODELING TO ANTICIPATE WORLD PRICE OF EACH OUNCE OF GOLD IN INTERNATIONAL MARKETS Mohammad Rikhegar Business Managemen, MA Suden Islamic Azad Universiy, a Souh Tehran Branch 009893632406
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 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 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 informationKey Words: Steel Modelling, ARMA, GARCH, COGARCH, Lévy Processes, Discrete Time Models, Continuous Time Models, Stochastic Modelling
Vol 4, No, 01 ISSN: 1309-8055 (Online STEEL PRICE MODELLING WITH LEVY PROCESS Emre Kahraman Türk Ekonomi Bankası (TEB A.Ş. Direcor / Risk Capial Markes Deparmen emre.kahraman@eb.com.r Gazanfer Unal Yediepe
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 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 informationDescription of the CBOE S&P 500 BuyWrite Index (BXM SM )
Descripion of he CBOE S&P 500 BuyWrie Index (BXM SM ) Inroducion. The CBOE S&P 500 BuyWrie Index (BXM) is a benchmark index designed o rack he performance of a hypoheical buy-wrie sraegy on he S&P 500
More informationModule 4. Single-phase AC circuits. Version 2 EE IIT, Kharagpur
Module 4 Single-phase A circuis ersion EE T, Kharagpur esson 5 Soluion of urren in A Series and Parallel ircuis ersion EE T, Kharagpur n he las lesson, wo poins were described:. How o solve for he impedance,
More informationExplaining long-term trends in groundwater hydrographs
18 h World IMACS / MODSIM Congress, Cairns, Ausralia 13-17 July 2009 hp://mssanz.org.au/modsim09 Explaining long-erm rends in groundwaer hydrographs Ferdowsian, R. 1 and D.J. Pannell 2 1 Deparmen of Agriculure
More informationUNIVARIATE TIME SERIES FORECASTING
UNIVERSITY OF VIENNA DEPARTMENT OF ECONOMICS MAY 2004 TERM PAPER UNIVARIATE TIME SERIES FORECASTING Wrien by Chrisoph Klose (9706253), Marion Pircher (9815456), Sephan Sharma (0008897) for 406347/UK Ökonomerische
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 informationSupply chain management of consumer goods based on linear forecasting models
Supply chain managemen of consumer goods based on linear forecasing models Parícia Ramos (paricia.ramos@inescporo.p) INESC TEC, ISCAP, Insiuo Poliécnico do Poro Rua Dr. Robero Frias, 378 4200-465, Poro,
More informationTime Series Analysis using In a Nutshell
1 Time Series Analysis using In a Nushell dr. JJM J.J.M. Rijpkema Eindhoven Universiy of Technology, dep. Mahemaics & Compuer Science P.O.Box 513, 5600 MB Eindhoven, NL 2012 j.j.m.rijpkema@ue.nl Sochasic
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 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 informationMTH6121 Introduction to Mathematical Finance Lesson 5
26 MTH6121 Inroducion o Mahemaical Finance Lesson 5 Conens 2.3 Brownian moion wih drif........................... 27 2.4 Geomeric Brownian moion........................... 28 2.5 Convergence of random
More informationTime Series Analysis for Predicting the Occurrences of Large Scale Earthquakes
Inernaional Journal of Applied Science and Technology Vol. No. 7; Augus 01 Time Series Analysis for Predicing he Occurrences of Large Scale Earhquakes Amei Amei* Wandong Fu** Chih-Hsiang Ho*** Absrac Earhquakes
More information(Received June 17, 2004)
TRANSPORTATION THE MALAYSIAN GOVERNMENT S ROAD ACCIDENT DEATH REDUCTION TARGET FOR YEAR 200 LAW, T.H. RADIN UMAR, R.S. WONG, S.V. Road Safey Research Cener Professor, Road Safey Research Cener Mechanical
More informationModeling Tourist Arrivals Using Time Series Analysis: Evidence From Australia
Journal of Mahemaics and Saisics 8 (3): 348-360, 2012 ISSN 1549-3644 2012 Science Publicaions Modeling Touris Arrivals Using Time Series Analysis: Evidence From Ausralia 1 Gurudeo AnandTularam, 2 Vicor
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 informationA comparison of the Lee-Carter model and AR-ARCH model for forecasting mortality rates
A comparison of he Lee-Carer model and AR-ARCH model for forecasing moraliy raes Rosella Giacomei a, Marida Berocchi b, Svelozar T. Rachev c, Frank J. Fabozzi d,e a Rosella Giacomei Deparmen of Mahemaics,
More informationNETWORK TRAFFIC MODELING AND PREDICTION USING MULTIPLICATIVE SEASONAL ARIMA MODELS
1s Inernaional Conference on Exerimens/Process/Sysem Modeling/Simulaion/Oimizaion 1s IC-EsMsO Ahens, 6-9 July, 2005 IC-EsMsO NETWORK TRAFFIC MODELING AND PREDICTION USING MULTIPLICATIVE SEASONAL ARIMA
More informationA DCC Analysis of Two Stock Market Returns Volatility with an Oil Price Factor: An Evidence Study of Singapore and Thailand s Stock Markets
Journal of Convergence Informaion Technology Volume 4, Number 1, March 9 A DCC Analysis of Two Sock Marke Reurns Volailiy wih an Oil Price Facor: An Evidence Sudy of Singapore and Thailand s Sock Markes
More informationAn Agent-based Bayesian Forecasting Model for Enhanced Network Security
An Agen-based Forecasing Model for Enhanced Nework Securiy J. PIKOULAS, W.J. BUCHANAN, Napier Universiy, Edinburgh, UK. M. MANNION, Glasgow Caledonian Universiy, Glasgow, UK. K. TRIANTAFYLLOPOULOS, Universiy
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 informationPerformance of combined double seasonal univariate time series models for forecasting water demand
1 Performance of combined double seasonal univariae ime series models for forecasing waer demand Jorge Caiado a a Cener for Applied Mahemaics and Economics (CEMAPRE), Insiuo Superior de Economia e Gesão,
More informationSkewness and Kurtosis Adjusted Black-Scholes Model: A Note on Hedging Performance
Finance Leers, 003, (5), 6- Skewness and Kurosis Adjused Black-Scholes Model: A Noe on Hedging Performance Sami Vähämaa * Universiy of Vaasa, Finland Absrac his aricle invesigaes he dela hedging performance
More informationThe Relationship between Stock Return Volatility and. Trading Volume: The case of The Philippines*
The Relaionship beween Sock Reurn Volailiy and Trading Volume: The case of The Philippines* Manabu Asai Faculy of Economics Soka Universiy Angelo Unie Economics Deparmen De La Salle Universiy Manila May
More informationANALYSIS OF ECONOMIC CYCLES USING UNOBSERVED COMPONENTS MODELS
ANALYSIS OF ECONOMIC CYCLES USING UNOBSERVED COMPONENTS MODELS Diego J. Pedregal Escuela Técnica Superior de Ingenieros Indusriales Universidad de Casilla-La Mancha Avda. Camilo José Cela, 3 13071 Ciudad
More informationWATER MIST FIRE PROTECTION RELIABILITY ANALYSIS
WATER MIST FIRE PROTECTION RELIABILITY ANALYSIS Shuzhen Xu Research Risk and Reliabiliy Area FM Global Norwood, Massachuses 262, USA David Fuller Engineering Sandards FM Global Norwood, Massachuses 262,
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 informationSingle-machine Scheduling with Periodic Maintenance and both Preemptive and. Non-preemptive jobs in Remanufacturing System 1
Absrac number: 05-0407 Single-machine Scheduling wih Periodic Mainenance and boh Preempive and Non-preempive jobs in Remanufacuring Sysem Liu Biyu hen Weida (School of Economics and Managemen Souheas Universiy
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 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 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 informationANALYSIS AND COMPARISONS OF SOME SOLUTION CONCEPTS FOR STOCHASTIC PROGRAMMING PROBLEMS
ANALYSIS AND COMPARISONS OF SOME SOLUTION CONCEPTS FOR STOCHASTIC PROGRAMMING PROBLEMS R. Caballero, E. Cerdá, M. M. Muñoz and L. Rey () Deparmen of Applied Economics (Mahemaics), Universiy of Málaga,
More informationLoad Prediction Using Hybrid Model for Computational Grid
Load Predicion Using Hybrid Model for Compuaional Grid Yongwei Wu, Yulai Yuan, Guangwen Yang 3, Weimin Zheng 4 Deparmen of Compuer Science and Technology, Tsinghua Universiy, Beijing 00084, China, 3, 4
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 informationFORECASTING WATER DEMAND FOR AGRICULTURAL, INDUSTRIAL AND DOMESTIC USE IN LIBYA
Inernaional Review of Business Research Papers Vol.4 No. 5 Ocober-November 8 Pp. 31-48 FORECASTING WATER DEMAND FOR AGRICULTURAL, INDUSTRIAL AND DOMESTIC USE IN LIBYA Fahis F. Lawgali* This paper examines
More informationAn Analysis of Tax Revenue Forecast Errors
An Analysis of Tax Revenue Forecas Errors Marin Keene and Peer Thomson N EW Z EALAND T REASURY W ORKING P APER 07/02 M ARCH 2007 NZ TREASURY WORKING PAPER 07/02 An Analysis of Tax Revenue Forecas Errors
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 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 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 informationMALAYSIAN FOREIGN DIRECT INVESTMENT AND GROWTH: DOES STABILITY MATTER? Jarita Duasa 1
Journal of Economic Cooperaion, 8, (007), 83-98 MALAYSIAN FOREIGN DIRECT INVESTMENT AND GROWTH: DOES STABILITY MATTER? Jaria Duasa 1 The objecive of he paper is wofold. Firs, is o examine causal relaionship
More informationWorking Paper A fractionally integrated exponential model for UK unemployment
econsor www.econsor.eu Der Open-Access-Publikaionsserver der ZBW Leibniz-Informaionszenrum Wirschaf The Open Access Publicaion Server of he ZBW Leibniz Informaion Cenre for Economics Gil-Alaña, Luis A.
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 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 informationGovernment Revenue Forecasting in Nepal
Governmen Revenue Forecasing in Nepal T. P. Koirala, Ph.D.* Absrac This paper aemps o idenify appropriae mehods for governmen revenues forecasing based on ime series forecasing. I have uilized level daa
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 informationHow Useful are the Various Volatility Estimators for Improving GARCH-based Volatility Forecasts? Evidence from the Nasdaq-100 Stock Index
Inernaional Journal of Economics and Financial Issues Vol. 4, No. 3, 04, pp.65-656 ISSN: 46-438 www.econjournals.com How Useful are he Various Volailiy Esimaors for Improving GARCH-based Volailiy Forecass?
More informationRelationships between Stock Prices and Accounting Information: A Review of the Residual Income and Ohlson Models. Scott Pirie* and Malcolm Smith**
Relaionships beween Sock Prices and Accouning Informaion: A Review of he Residual Income and Ohlson Models Sco Pirie* and Malcolm Smih** * Inernaional Graduae School of Managemen, Universiy of Souh Ausralia
More informationStock Trading with Recurrent Reinforcement Learning (RRL) CS229 Application Project Gabriel Molina, SUID 5055783
Sock raing wih Recurren Reinforcemen Learning (RRL) CS9 Applicaion Projec Gabriel Molina, SUID 555783 I. INRODUCION One relaively new approach o financial raing is o use machine learning algorihms o preic
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 informationA PROPOSAL TO OBTAIN A LONG QUARTERLY CHILEAN GDP SERIES *
CUADERNOS DE ECONOMÍA, VOL. 43 (NOVIEMBRE), PP. 285-299, 2006 A PROPOSAL TO OBTAIN A LONG QUARTERLY CHILEAN GDP SERIES * JUAN DE DIOS TENA Universidad de Concepción y Universidad Carlos III, España MIGUEL
More informationTESTING OF SEASONAL FRACTIONAL INTEGRATION IN UK AND JAPANESE CONSUMPTION AND INCOME *
TESTING OF SEASONAL FRACTIONAL INTEGRATION IN UK AND JAPANESE CONSUMPTION AND INCOME * by L A Gil-Alaña Humbold Universiy, Berlin, and Universiy of Navarre, Spain and P M Robinson London School of Economics
More informationA Probability Density Function for Google s stocks
A Probabiliy Densiy Funcion for Google s socks V.Dorobanu Physics Deparmen, Poliehnica Universiy of Timisoara, Romania Absrac. I is an approach o inroduce he Fokker Planck equaion as an ineresing naural
More informationAcceleration Lab Teacher s Guide
Acceleraion Lab Teacher s Guide Objecives:. Use graphs of disance vs. ime and velociy vs. ime o find acceleraion of a oy car.. Observe he relaionship beween he angle of an inclined plane and he acceleraion
More informationTime Consisency in Porfolio Managemen
1 Time Consisency in Porfolio Managemen Traian A Pirvu Deparmen of Mahemaics and Saisics McMaser Universiy Torono, June 2010 The alk is based on join work wih Ivar Ekeland Time Consisency in Porfolio Managemen
More informationReview of Middle East Economics and Finance
Review of Middle Eas Economics and Finance Volume 4, Number 008 Aricle 3 Transiory and Permanen Volailiy s: The Case of he Middle Eas Sock Markes Bashar Abu Zarour, Universiy of Paras Cosas P. Siriopoulos,
More informationStatistical Approaches to Electricity Price Forecasting
Saisical Approaches o Elecriciy Price Forecasing By J. Suar McMenamin, Ph.D., Frank A. Monfore, Ph.D. Chrisine Fordham, Eric Fox, Fredrick D. Sebold Ph.D., and Mark Quan 1. Inroducion Wih he adven of compeiion,
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 informationAPPLICATION OF THE KALMAN FILTER FOR ESTIMATING CONTINUOUS TIME TERM STRUCTURE MODELS: THE CASE OF UK AND GERMANY. January, 2005
APPLICATION OF THE KALMAN FILTER FOR ESTIMATING CONTINUOUS TIME TERM STRUCTURE MODELS: THE CASE OF UK AND GERMANY Somnah Chaeree* Deparmen of Economics Universiy of Glasgow January, 2005 Absrac The purpose
More informationGoRA. For more information on genetics and on Rheumatoid Arthritis: Genetics of Rheumatoid Arthritis. Published work referred to in the results:
For more informaion on geneics and on Rheumaoid Arhriis: Published work referred o in he resuls: The geneics revoluion and he assaul on rheumaoid arhriis. A review by Michael Seldin, Crisopher Amos, Ryk
More informationChapter Four: Methodology
Chaper Four: Mehodology 1 Assessmen of isk Managemen Sraegy Comparing Is Cos of isks 1.1 Inroducion If we wan o choose a appropriae risk managemen sraegy, no only we should idenify he influence ha risks
More informationSURVEYING THE RELATIONSHIP BETWEEN STOCK MARKET MAKER AND LIQUIDITY IN TEHRAN STOCK EXCHANGE COMPANIES
Inernaional Journal of Accouning Research Vol., No. 7, 4 SURVEYING THE RELATIONSHIP BETWEEN STOCK MARKET MAKER AND LIQUIDITY IN TEHRAN STOCK EXCHANGE COMPANIES Mohammad Ebrahimi Erdi, Dr. Azim Aslani,
More informationForecasting the dynamics of financial markets. Empirical evidence in the long term
Leonardo Franci (Ialy), Andi Duqi (Ialy), Giuseppe Torluccio (Ialy) Forecasing he dynamics of financial markes. Empirical evidence in he long erm Absrac This sudy aims o verify wheher here are any macroeconomic
More informationCEEP-BIT WORKING PAPER SERIES. The crude oil market and the gold market: Evidence for cointegration, causality and price discovery
CEEP-BIT WORKING PAPER SERIES The crude oil marke and he gold marke: Evidence for coinegraion, causaliy and price discovery Yue-Jun Zhang Yi-Ming Wei Working Paper 5 hp://www.ceep.ne.cn/english/publicaions/wp/
More informationAllowing the Data to Speak Freely: The Macroeconometrics of the Cointegrated Vector Autoregression
Allowing he Daa o Speak Freely: The Macroeconomerics of he Coinegraed Vecor Auoregression 16 November 2007 Corresponding Auhor: Kevin D. Hoover Deparmen of Economics Duke Universiy Box 90097 Durham, NC
More informationECONOMETRIC MODELLING AND FORECASTING OF FREIGHT TRANSPORT DEMAND IN GREAT BRITAIN
ECONOMETRIC MODELLING AND FORECASTING OF FREIGHT TRANSPORT DEMAND IN GREAT BRITAIN Shujie Shen, Tony Fowkes, Tony Whieing and Daniel Johnson Insiue for Transpor Sudies, Universiy of Leeds, Leeds, UK, LS2
More informationRelationship between Stock Returns and Trading Volume: Domestic and Cross-Country Evidence in Asian Stock Markets
Proceedings of he 2013 Inernaional Conference on Economics and Business Adminisraion Relaionship beween Sock Reurns and Trading olume: Domesic and Cross-Counry Evidence in Asian Sock Markes Ki-Hong Choi
More informationCausal Relationship between Macro-Economic Indicators and Stock Market in India
Asian Journal of Finance & Accouning Causal Relaionship beween Macro-Economic Indicaors and Sock Marke in India Dr. Naliniprava ripahy Associae Professor (Finance), Indian Insiue of Managemen Shillong
More informationForecasting and Forecast Combination in Airline Revenue Management Applications
Forecasing and Forecas Combinaion in Airline Revenue Managemen Applicaions Chrisiane Lemke 1, Bogdan Gabrys 1 1 School of Design, Engineering & Compuing, Bournemouh Universiy, Unied Kingdom. E-mail: {clemke,
More informationEstimating the Term Structure with Macro Dynamics in a Small Open Economy
Esimaing he Term Srucure wih Macro Dynamics in a Small Open Economy Fousseni Chabi-Yo Bank of Canada Jun Yang Bank of Canada April 18, 2006 Preliminary work. Please do no quoe wihou permission. The paper
More informationAN ECONOMETRIC CHARACTERIZATION OF BUSINESS CYCLE DYNAMICS WITH FACTOR STRUCTURE AND REGIME SWITCHING * Marcelle Chauvet 1
AN ECONOMETRIC CHARACTERIZATION OF BUSINESS CYCLE DYNAMICS WITH FACTOR STRUCTURE AND REGIME SWITCHING * Marcelle Chauve Deparmen of Economics Universiy of California, Riverside 5 Universiy Avenue Riverside,
More informationTitle: Who Influences Latin American Stock Market Returns? China versus USA
Cenre for Global Finance Working Paper Series (ISSN 2041-1596) Paper Number: 05/10 Tile: Who Influences Lain American Sock Marke Reurns? China versus USA Auhor(s): J.G. Garza-García; M.E. Vera-Juárez Cenre
More informationModelling and Forecasting Volatility of Gold Price with Other Precious Metals Prices by Univariate GARCH Models
Deparmen of Saisics Maser's Thesis Modelling and Forecasing Volailiy of Gold Price wih Oher Precious Meals Prices by Univariae GARCH Models Yuchen Du 1 Supervisor: Lars Forsberg 1 Yuchen.Du.84@suden.uu.se
More informationAnalysis of Pricing and Efficiency Control Strategy between Internet Retailer and Conventional Retailer
Recen Advances in Business Managemen and Markeing Analysis of Pricing and Efficiency Conrol Sraegy beween Inerne Reailer and Convenional Reailer HYUG RAE CHO 1, SUG MOO BAE and JOG HU PARK 3 Deparmen of
More informationAnalogue and Digital Signal Processing. First Term Third Year CS Engineering By Dr Mukhtiar Ali Unar
Analogue and Digial Signal Processing Firs Term Third Year CS Engineering By Dr Mukhiar Ali Unar Recommended Books Haykin S. and Van Veen B.; Signals and Sysems, John Wiley& Sons Inc. ISBN: 0-7-380-7 Ifeachor
More information